[{"data":1,"prerenderedAt":1614},["ShallowReactive",2],{"categories-init":3,"stack-python-ai-agent":4,"stack-res-python-ai-agent":1613},true,{"stack_id":5,"slug":6,"name":7,"tagline":8,"long_description":9,"key_features":10,"use_cases":17,"pros":23,"cons":28,"cover_image_url":33,"scores":34,"options":48,"additions":651,"addition_groups":1236,"option_groups":1268,"multi_select_option_types":1269,"tools_by_category":1270,"related_stacks":1403,"faqs":1572,"pricing":1588,"system_requirements":33,"experience_level":1409,"project_type":1410,"stack_type_slug":1411,"stack_type_icon_url":1412,"published_date":388,"last_updated_date":1608,"seo_meta":1609},236,"python-ai-agent","Python AI Agent","A code-first AI agent in Python: an agent framework for the loop, an API in front, and a database for memory.","This stack is for an agent you write yourself: Python code in which **a language model decides the next step**, calls your tools, reads the results, and repeats until the task is done. An agent framework supplies that loop. LangGraph is the default, and it models the agent as a graph of steps with a typed state, so you control where the model has freedom and where the path is fixed. Pydantic AI, CrewAI, and LangChain fill the same role in different styles, and the rest of the stack stays the same whichever you choose.\n\nFastAPI **serves the agent as an HTTP API**. It streams tokens and tool events to the caller as they happen, and it starts longer runs in the background so a request doesn't have to stay open for minutes. The same service holds what the agent's tools need: API clients, credentials, and the business logic the model is allowed to call.\n\nPostgreSQL is **the agent's memory**, not an application database full of users and orders. With LangGraph, each conversation is a thread and a checkpoint is saved after every step, so a run that crashes or waits for a person's approval resumes exactly where it stopped. A second store keeps facts that should outlive one conversation. The same database can hold embeddings through the pgvector extension, which is enough retrieval for many agents before a dedicated vector database is worth running.\n\nThe model is a separate choice from the framework. Claude is the default, and OpenAI, Gemini, or DeepSeek is swapped in by changing the model name and the API key, since all four frameworks are model-agnostic. Model usage is also **where most of the money goes**: the server and database cost a few dollars a month, while token spend grows with every step the agent takes. Common optional extras are tracing with Langfuse or LangSmith to see what each run did, a sandbox such as E2B for code the agent writes, and a vector database once pgvector is no longer enough.",[11,12,13,14,15,16],"An agent loop on a framework: LangGraph by default, or Pydantic AI, CrewAI, or LangChain","FastAPI endpoints that stream tokens and tool events while a run is in progress","With LangGraph, the default framework, run state is checkpointed to PostgreSQL after every step, so a run resumes after a crash or a restart","Human-in-the-loop pauses: a run can stop for approval and continue later from the same point","Model-agnostic: Claude by default, with OpenAI, Gemini, or DeepSeek a configuration change away","Vector search in the same database through the pgvector extension",[18,19,20,21,22],"Support and operations agents that look things up and act through your internal APIs","Research and analysis agents that search, read sources, and write a report","Long-running workflows that pause for a person to approve a step before continuing","Multi-agent systems where specialised agents hand work to each other","An agent API that a web or mobile app calls as its backend",[24,25,26,27],"Every step is code you can read, test, and version, with no visual builder between you and the logic","Each layer swaps on its own: framework, model, database, and host","Python has the widest choice of agent frameworks, model SDKs, and data libraries","One PostgreSQL database covers run state, long-term memory, and vector search for many projects",[29,30,31,32],"No user interface included: this is a backend, and a chat or admin frontend is a separate build","Agent frameworks change quickly, so upgrades can mean reworking code","Model spend is hard to predict, because one request can turn into dozens of model calls","Finding out why an agent gave a wrong answer usually takes a tracing tool, which is one more thing to set up",null,{"popularity":35,"learning_curve":38,"flexibility":40,"performance":43,"portability":46},{"score":36,"reasoning":37},4,"Python is the main language for agent development, and LangGraph and LangChain are among the most used frameworks in it, with FastAPI and PostgreSQL as common companions. Building agents in code is still a specialised activity next to mainstream web development.",{"score":36,"reasoning":39},"FastAPI and PostgreSQL are familiar ground for a Python developer, but the agent layer is not: graph state, checkpoints, tool schemas, prompt design, and evaluation all have to be learned, and the frameworks' APIs are still moving.",{"score":41,"reasoning":42},5,"Every layer is code and every layer swaps independently: four agent frameworks, four model providers, three ways to run PostgreSQL, and hosts from a container platform to a plain VPS. Nothing about the agent's logic is fixed by a visual builder or a vendor runtime.",{"score":44,"reasoning":45},3,"FastAPI's async handlers and PostgreSQL add little overhead, but response time is set by the model: each step waits on a model call, and a run takes several. Streaming keeps the interface responsive without making the run itself faster.",{"score":36,"reasoning":47},"The frameworks, FastAPI, and PostgreSQL are open source and run on any host that takes a container, and the model is reached through an API that can be pointed at another provider. The agent code itself is written against one framework, so changing framework means a rewrite of that layer.",{"agent_framework":49,"database":219,"llm":345,"hosting":483},{"name":50,"tools":51,"descriptions":210,"aliases":215,"see_all":216},"Agent Framework",[52,114,146,174],{"tool_id":53,"name":54,"slug":55,"tooltip_description":56,"logo_url":57,"logo_bg":58,"pricing_model":59,"learning_curve_score":36,"popularity_score":36,"hosting_assignment_type":63,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":65,"subcategory":69,"categories":73,"subcategories":76,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":79,"aliases":106,"score_reasonings":107,"published_date":113,"last_updated_date":33},233,"LangGraph","langgraph","A low-level orchestration runtime from LangChain for building stateful, multi-agent applications as graphs — nodes, edges, cyclic execution, and durable state with checkpointing, human-in-the-loop, and time travel. 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No paid plans from the project itself.",2,{"category_id":66,"name":67,"slug":68},{"subcategory_id":70,"name":71,"slug":72},[127],{"category_id":66,"name":67,"slug":68,"is_primary":3,"display_order":75},[129],{"subcategory_id":70,"name":71,"slug":72,"category_id":66,"is_primary":3,"display_order":75},[131,132,133,134,135],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":98,"name":99,"slug":100,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},25,"Machine Learning","machine-learning",[],{"learning_curve":141,"flexibility":142,"performance":143,"popularity":144,"portability":145},"Developers already comfortable with Pydantic and type hints are productive almost immediately, and the API is deliberately small and Pythonic rather than sprawling.","Model-agnostic with typed dependency injection, MCP, multiple agent-to-UI protocols, and durable execution, though it is opinionated toward type-safe, schema-first design.","A thin, validation-focused layer over the underlying models with streaming and durable execution keeps overhead low while supporting production workloads.","Fast-growing and well-regarded, backed by the widely used Pydantic brand, but still smaller in adoption than the most established agent frameworks.","MIT-licensed, self-hostable anywhere Python runs, and provider-agnostic, so there is no vendor lock-in in the framework itself.",{"tool_id":147,"name":148,"slug":149,"tooltip_description":150,"logo_url":151,"logo_bg":58,"pricing_model":152,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":63,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":153,"subcategory":154,"categories":155,"subcategories":157,"flexibility_score":36,"performance_score":36,"portability_score":41,"is_featured":78,"tags":159,"aliases":166,"score_reasonings":167,"published_date":113,"last_updated_date":173},232,"CrewAI","crewai","An open-source Python framework for orchestrating role-playing autonomous AI agents into collaborating teams — MIT-licensed, standalone (no LangChain dependency), with a managed platform for deploying and monitoring agent workflows.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fcrewai.svg",{"slug":60,"display_name":61,"description":62},{"category_id":66,"name":67,"slug":68},{"subcategory_id":70,"name":71,"slug":72},[156],{"category_id":66,"name":67,"slug":68,"is_primary":3,"display_order":75},[158],{"subcategory_id":70,"name":71,"slug":72,"category_id":66,"is_primary":3,"display_order":75},[160,161,162,163,164,165],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":98,"name":99,"slug":100,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":102,"name":103,"slug":104,"tag_type":105},[],{"learning_curve":168,"flexibility":169,"performance":170,"portability":171,"popularity":172},"The role\u002Fgoal\u002Ftask abstraction and extensive examples make a first crew quick to assemble, though tuning Flows and reliable multi-agent delegation takes more investment.","Model-agnostic with custom tools, MCP support, and Flows for control flow, though it is more opinionated around the crew metaphor than a bare orchestration primitive.","The from-scratch, dependency-light design keeps overhead low, and checkpointing plus Flows support production-grade workloads at scale.","MIT-licensed, self-hostable, and provider-agnostic, so the core framework carries no lock-in even if the managed platform is not used.","A Python-native multi-agent orchestration framework with real enterprise deployments (DocuSign, PwC, and IBM case studies) and a 100K+ developer certification program. Adoption is strong specifically among Python AI-engineering teams — outside that corner of the ecosystem, the name is much less familiar.","2026-10-01",{"tool_id":175,"name":176,"slug":177,"tooltip_description":178,"logo_url":179,"logo_bg":58,"pricing_model":180,"learning_curve_score":44,"popularity_score":36,"hosting_assignment_type":63,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":181,"subcategory":182,"categories":183,"subcategories":185,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":187,"aliases":203,"score_reasonings":204,"published_date":113,"last_updated_date":173},214,"LangChain","langchain","Open-source Python and JavaScript framework for building LLM-powered agents and applications, with LangGraph for stateful multi-agent orchestration and LangSmith for tracing, evaluation, and deployment.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Flangchain.svg",{"slug":121,"display_name":90,"description":122},{"category_id":66,"name":67,"slug":68},{"subcategory_id":70,"name":71,"slug":72},[184],{"category_id":66,"name":67,"slug":68,"is_primary":3,"display_order":75},[186],{"subcategory_id":70,"name":71,"slug":72,"category_id":66,"is_primary":3,"display_order":75},[188,189,192,193,197,198],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":123,"name":190,"slug":191,"tag_type":84},"JavaScript","javascript",{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},13,"Free Tier","free-tier",{"tag_id":98,"name":99,"slug":100,"tag_type":92},{"tag_id":199,"name":200,"slug":201,"tag_type":202},40,"Web","web","platform",[],{"learning_curve":205,"flexibility":206,"performance":207,"portability":208,"popularity":209},"Basic chains are quick to assemble with the huge library of existing examples, but building genuinely stateful multi-agent workflows with LangGraph, or wiring up LangSmith observability well, takes real investment.","A massive integration ecosystem across models, tools, vector stores, and memory backends, plus LangGraph's low-level orchestration primitives, make it applicable to nearly any agent architecture a developer wants to build.","LangGraph's durable execution and human-in-the-loop support are built for production-grade multi-agent systems, and the framework itself adds minimal overhead since it's a thin orchestration layer over whichever models are configured.","MIT-licensed with no usage limits on the core framework or LangGraph, and provider-agnostic across virtually every major LLM, so there's no lock-in to use it.","The reference point most LLM app tutorials, courses, and third-party integrations are still written against, with the framework reaching a 1.0 release in October 2025. The bleeding edge of the agent-framework conversation has since moved to LangGraph and newer entrants, leaving LangChain the mature, well-trodden starting point rather than what developers are actively buzzing about.",{"langgraph":211,"pydantic-ai":212,"crewai":213,"langchain":214},"The default. LangGraph models the agent as a graph: nodes are steps, edges decide what runs next, and a typed state object passes through all of them. You choose where the model is free to pick the next tool and where the path is fixed. Its Postgres checkpointer saves state after every step, which is what makes resuming, approval pauses, and replaying a past run work. It asks for more code up front than the other three.","Built by the Pydantic team, so it feels like FastAPI: tools are typed Python functions, dependencies are injected, and the model's output is validated against a schema and requested again when it doesn't fit. Pick it when the agent is one model with a set of tools and structured results matter more than a custom control flow. Runs that must survive a restart go through one of its durable execution integrations, such as Temporal or DBOS.","Organises the work as a crew: several agents, each with a role, a goal, and its own tools, that pass tasks between them. Flows wrap those crews in event-driven steps when the order has to be fixed. It suits jobs that split naturally into roles, such as a researcher, a writer, and a reviewer. Flow state is saved to SQLite by default, so check how persistence fits your deployment before relying on resumed runs.","The higher-level route from the company behind LangGraph. Its create_agent function gives a working tool-calling agent in a few lines and runs on LangGraph underneath, so checkpointing to PostgreSQL works the same way. Prebuilt middleware covers common needs such as summarising long conversations, retries, and human approval. Start here for a standard agent, and drop down to LangGraph when you need to draw the control flow yourself.",{},{"kind":217,"slug":72,"name":71,"href":218},"subcategory","\u002Ftools\u002Fcategories\u002Fagentic-ai\u002Fagent-frameworks",{"name":220,"tools":221,"descriptions":337,"aliases":341,"see_all":342},"Database",[222,259,294],{"tool_id":199,"name":223,"slug":224,"tooltip_description":225,"logo_url":226,"logo_bg":58,"pricing_model":227,"learning_curve_score":44,"popularity_score":41,"hosting_assignment_type":228,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":229,"subcategory":232,"categories":235,"subcategories":237,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":3,"tags":239,"aliases":249,"score_reasonings":252,"published_date":258,"last_updated_date":173},"PostgreSQL","postgresql","PostgreSQL is a free, open-source object-relational database known for reliability, standards compliance, and extensibility, with rich data types, JSONB, and a large ecosystem of extensions such as PostGIS and pgvector.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpostgresql.svg",{"slug":121,"display_name":90,"description":122},"deployable",{"category_id":36,"name":230,"slug":231},"Databases","databases",{"subcategory_id":136,"name":233,"slug":234},"OLTP Databases","oltp-databases",[236],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[238],{"subcategory_id":136,"name":233,"slug":234,"category_id":36,"is_primary":3,"display_order":75},[240,243,244,245],{"tag_id":36,"name":241,"slug":242,"tag_type":84},"SQL","sql",{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":246,"name":247,"slug":248,"tag_type":92},22,"ACID Compliant","acid-compliant",[250],{"alias":251,"is_primary":3},"PG",{"learning_curve":253,"flexibility":254,"performance":255,"portability":256,"popularity":257},"Standard SQL is broadly known, but CTEs, window functions, and JSONB take time to master.","Extensions like PostGIS and pgvector, custom types, and PL\u002FpgSQL cover virtually any need.","Battle-tested query planner; efficient with proper indexing and regular vacuum.","Open SQL standard with no lock-in; data and skills transfer to any SQL-compatible system.","The most popular relational database among developers per Stack Overflow 2024; rapidly growing.","2026-05-29",{"tool_id":260,"name":261,"slug":262,"tooltip_description":263,"logo_url":264,"logo_bg":58,"pricing_model":265,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":267,"subcategory":268,"categories":272,"subcategories":274,"flexibility_score":36,"performance_score":36,"portability_score":41,"is_featured":78,"tags":276,"aliases":287,"score_reasonings":288,"published_date":113,"last_updated_date":33},226,"Neon","neon","Serverless PostgreSQL that separates storage from compute, scales to zero when idle, and lets you branch a database like Git.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fneon.svg",{"slug":60,"display_name":61,"description":62},"managed_only",{"category_id":36,"name":230,"slug":231},{"subcategory_id":269,"name":270,"slug":271},65,"Serverless OLTP","serverless-oltp",[273],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[275],{"subcategory_id":269,"name":270,"slug":271,"category_id":36,"is_primary":3,"display_order":75},[277,278,279,283,286],{"tag_id":36,"name":241,"slug":242,"tag_type":84},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":280,"name":281,"slug":282,"tag_type":92},14,"Serverless","serverless",{"tag_id":66,"name":284,"slug":285,"tag_type":92},"Auto-scaling","auto-scaling",{"tag_id":246,"name":247,"slug":248,"tag_type":92},[],{"learning_curve":289,"flexibility":290,"performance":291,"popularity":292,"portability":293},"For anyone who knows Postgres there is almost nothing new to learn, since it is the same SQL, drivers, and extensions. Branching and scale-to-zero are the only additional concepts.","Full Postgres with extension support covers a wide range of workloads, and separated compute plus branching add deployment options a plain managed instance lacks, within the bounds of the Postgres feature set.","Autoscaling and modern storage give solid throughput for typical web workloads, and scale-to-zero saves cost, though the first query after idle pays a cold-start penalty.","A leading name in serverless Postgres with strong adoption in the Vercel and Next.js communities, and increased visibility following the Databricks acquisition.","Because it is standard PostgreSQL, a logical dump restores onto any Postgres host, so there is minimal engine lock-in and skills transfer completely.",{"tool_id":81,"name":295,"slug":296,"tooltip_description":297,"logo_url":298,"logo_bg":58,"pricing_model":299,"learning_curve_score":44,"popularity_score":36,"hosting_assignment_type":300,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":301,"subcategory":302,"categories":305,"subcategories":307,"flexibility_score":36,"performance_score":36,"portability_score":44,"is_featured":78,"tags":309,"aliases":330,"score_reasonings":331,"published_date":258,"last_updated_date":113},"Supabase","supabase","Open-source Firebase alternative built on PostgreSQL. Provides a full backend stack with database, authentication, real-time subscriptions, file storage, and edge functions — deployable on Supabase Cloud or self-hosted.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fsupabase.svg",{"slug":60,"display_name":61,"description":62},"self_hostable",{"category_id":36,"name":230,"slug":231},{"subcategory_id":81,"name":303,"slug":304},"Backend as a Service","baas",[306],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[308],{"subcategory_id":81,"name":303,"slug":304,"category_id":36,"is_primary":3,"display_order":75},[310,311,312,313,314,318,322,326],{"tag_id":36,"name":241,"slug":242,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":315,"name":316,"slug":317,"tag_type":92},15,"Real-time","real-time",{"tag_id":319,"name":320,"slug":321,"tag_type":105},28,"Web Development","web-development",{"tag_id":323,"name":324,"slug":325,"tag_type":105},30,"API Development","api-development",{"tag_id":327,"name":328,"slug":329,"tag_type":105},34,"Authentication","authentication",[],{"learning_curve":332,"flexibility":333,"performance":334,"popularity":335,"portability":336},"SQL and REST are familiar; realtime subscriptions and RLS policies need dedicated study.","PostgreSQL underneath supports any SQL; the Supabase client SDK nudges toward specific patterns.","PostgreSQL query performance is solid; edge functions are fast; realtime runs via Postgres LISTEN\u002FNOTIFY.","One of the most popular open-source BaaS platforms; rapidly growing developer community.","PostgreSQL underneath is open, but the Supabase SDK and realtime layer create meaningful lock-in.",{"postgresql":338,"neon":339,"supabase":340},"The default: a PostgreSQL instance next to the API, on the same server or as the host's own database service. It stores the agent's threads, checkpoints, and long-term memory, and with the pgvector extension installed it also holds embeddings. Most of the load is a small write after each step, so a modest instance goes a long way.","Serverless PostgreSQL that scales to zero when no run is active, which fits an agent that is busy in bursts and idle in between. The free plan covers a prototype, and paid usage is billed at $0.106 per compute-unit hour and $0.35 per GB a month with no minimum. pgvector is available on every plan, and branching gives a test run its own copy of real conversation state. The first query after an idle period waits for the database to wake.","Managed PostgreSQL with a dashboard for browsing the agent's threads and memory tables, and with auth and file storage on hand if the project later grows a user-facing app. pgvector is built in. The free plan pauses a project after a week without activity, so anything long-lived belongs on Pro, from $25 a month. The frameworks connect over the standard connection string and need nothing Supabase-specific.",{},{"kind":343,"slug":231,"name":230,"href":344},"category","\u002Ftools\u002Fcategories\u002Fdatabases",{"name":346,"tools":347,"descriptions":475,"aliases":480,"see_all":481},"LLM",[348,389,418,448],{"tool_id":349,"name":350,"slug":351,"tooltip_description":352,"logo_url":353,"logo_bg":58,"pricing_model":354,"learning_curve_score":123,"popularity_score":41,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":358,"subcategory":361,"categories":365,"subcategories":367,"flexibility_score":36,"performance_score":41,"portability_score":44,"is_featured":78,"tags":369,"aliases":371,"score_reasonings":382,"published_date":258,"last_updated_date":388},153,"Claude","claude","Anthropic's family of AI models, from fast Haiku to frontier Fable, available through the Claude API, Amazon Bedrock, Google Vertex AI, Microsoft Foundry, and claude.ai, known for long context, tool use, and adaptive thinking.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fclaude.svg",{"slug":355,"display_name":356,"description":357},"usage_based","Usage-Based","Pricing scales with consumption: API calls, data volume, compute time, or similar metered units.",{"category_id":359,"name":346,"slug":360},19,"llm",{"subcategory_id":362,"name":363,"slug":364},51,"Proprietary","proprietary",[366],{"category_id":359,"name":346,"slug":360,"is_primary":3,"display_order":75},[368],{"subcategory_id":362,"name":363,"slug":364,"category_id":359,"is_primary":3,"display_order":75},[370],{"tag_id":199,"name":200,"slug":201,"tag_type":202},[372,374,376,378,380],{"alias":373,"is_primary":3},"Sonnet",{"alias":375,"is_primary":3},"Opus",{"alias":377,"is_primary":3},"Haiku",{"alias":379,"is_primary":3},"Fable",{"alias":381,"is_primary":3},"Anthropic",{"learning_curve":383,"flexibility":384,"performance":385,"popularity":386,"portability":387},"The Anthropic API follows the standard messages array pattern familiar from other LLM providers. Python and TypeScript SDKs are well-documented. Basic integration takes under an hour.","Supports tool use, vision, extended thinking, prompt caching, batch processing, streaming, and system prompts. No fine-tuning and no image output limits the ceiling. Multi-cloud availability adds operational flexibility.","Claude 4 Opus and Sonnet 4 are at the frontier of reasoning and coding benchmarks as of 2025. Extended thinking mode significantly improves accuracy on hard problems. Competitive with GPT-4o and Gemini Ultra across most evaluation suites.","Claude is one of the two or three most-referenced LLMs in developer communities worldwide. Massive adoption via Claude.ai, API, Bedrock, and Vertex AI. Powers Claude Code, Cursor (optional model), and many third-party AI products.","Anthropic API uses its own message format and tool-calling schema — not OpenAI-compatible. Migrating to\u002Ffrom Claude requires prompt and schema rewrites. Available on Bedrock and Vertex AI reduces infrastructure lock-in but does not eliminate model lock-in.","2026-10-02",{"tool_id":390,"name":391,"slug":392,"tooltip_description":393,"logo_url":394,"logo_bg":395,"pricing_model":396,"learning_curve_score":123,"popularity_score":41,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":397,"subcategory":398,"categories":399,"subcategories":401,"flexibility_score":44,"performance_score":41,"portability_score":123,"is_featured":78,"tags":403,"aliases":405,"score_reasonings":412,"published_date":258,"last_updated_date":388},155,"OpenAI","openai","OpenAI's API platform for the GPT-6 family (Astra, Sol, Luna), plus real-time voice, image generation, transcription, and embeddings, with built-in tools, structured outputs, and agent-building APIs.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fopenai.svg","white",{"slug":355,"display_name":356,"description":357},{"category_id":359,"name":346,"slug":360},{"subcategory_id":362,"name":363,"slug":364},[400],{"category_id":359,"name":346,"slug":360,"is_primary":3,"display_order":75},[402],{"subcategory_id":362,"name":363,"slug":364,"category_id":359,"is_primary":3,"display_order":75},[404],{"tag_id":199,"name":200,"slug":201,"tag_type":202},[406,408,410],{"alias":407,"is_primary":3},"ChatGPT",{"alias":409,"is_primary":3},"GPT",{"alias":411,"is_primary":3},"Astra",{"performance":413,"popularity":414,"portability":415,"learning_curve":416,"flexibility":417},"Leads or co-leads industry benchmarks across coding (SWE-bench), reasoning (MATH, GPQA), and instruction-following. o3 and GPT-5 are best-in-class for their respective task types as of 2026.","The most-used LLM API in the world by developer count, third-party integrations, and mindshare. ChatGPT's cultural reach directly drives API adoption. Virtually every developer tool, no-code platform, and enterprise software suite lists OpenAI as a primary integration.","Entirely cloud-bound; no self-hosted option. The API is proprietary — migrating to a different provider requires rewriting prompt logic, tool schemas, and API call structure. Azure and Bedrock availability reduces cloud lock-in but not provider lock-in.","The Chat Completions API is one of the most beginner-friendly interfaces in software — a single POST request with a messages array returns a completion. Official SDKs for Python and Node ship with comprehensive docs and hundreds of cookbook examples. The main learning curve is choosing the right model family and managing costs rather than the API mechanics themselves.","OpenAI offers genuine breadth — five model families, fine-tuning (supervised + reinforcement), function calling, structured outputs, and real-time audio. However, all inference is cloud-only and proprietary; you cannot swap the underlying model weights, run locally, or choose your hardware. Compared to open-weight alternatives, customisation stops at fine-tuning.",{"tool_id":419,"name":420,"slug":421,"tooltip_description":422,"logo_url":423,"logo_bg":58,"pricing_model":424,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":425,"subcategory":426,"categories":427,"subcategories":429,"flexibility_score":36,"performance_score":41,"portability_score":123,"is_featured":78,"tags":431,"aliases":433,"score_reasonings":442,"published_date":258,"last_updated_date":113},159,"Google Gemini","google-gemini","Google's LLM API with a full model family, from low-cost Flash-Lite to the Gemini 3.1 Pro flagship, with 1M-token context, native multimodal input, a free tier in AI Studio, and enterprise access through Google Cloud.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fgoogle-gemini.svg",{"slug":355,"display_name":356,"description":357},{"category_id":359,"name":346,"slug":360},{"subcategory_id":362,"name":363,"slug":364},[428],{"category_id":359,"name":346,"slug":360,"is_primary":3,"display_order":75},[430],{"subcategory_id":362,"name":363,"slug":364,"category_id":359,"is_primary":3,"display_order":75},[432],{"tag_id":199,"name":200,"slug":201,"tag_type":202},[434,436,438,440],{"alias":435,"is_primary":3},"Gemini Flash",{"alias":437,"is_primary":3},"Gemini Pro",{"alias":439,"is_primary":3},"Flash",{"alias":441,"is_primary":3},"Bard",{"learning_curve":443,"flexibility":444,"performance":445,"popularity":446,"portability":447},"Getting started is exceptionally frictionless — Google AI Studio requires only a Google account, no billing setup. The API follows standard REST conventions with well-maintained Python and JavaScript SDKs. The learning curve is low for basic usage; more advanced features like Vertex AI agent orchestration, grounding configuration, and context caching add moderate complexity.","Broad model selection (Flash-Lite to 3.1 Pro), native multimodal capabilities, function calling, structured output, code execution, and grounding with Search cover a wide range of use cases. Vertex AI adds fine-tuning and custom grounding sources. The main flexibility constraint is that all models are proprietary and cloud-hosted — no self-hosting or weights access.","Gemini 3.1 Pro leads on ARC-AGI-2 (77.1%) and outperforms GPT-5.4 on GPQA Diamond. Gemini 2.5 Flash consistently benchmarks near the top of its price tier. The 1M+ token context window is unmatched for long-document tasks. Competitive with the best frontier models across reasoning, code, and multimodal tasks as of 2026.","750M+ consumer Gemini users by early 2026 and 13M developers reported building with Google's generative models. API request volume hit 85B in January 2026 (142% YoY growth). Strong and accelerating, but OpenAI maintains a larger developer ecosystem and ChatGPT holds ~65% consumer chatbot market share vs Gemini's ~21%.","Deeply tied to Google infrastructure — the API is available only through Google AI Studio or Vertex AI (GCP). No open weights, no self-hosting, no third-party deployment. Moving workloads to another provider requires prompt re-testing and SDK changes. Vertex AI features (grounding, residency controls, agent orchestration) deepen GCP lock-in further.",{"tool_id":449,"name":450,"slug":451,"tooltip_description":452,"logo_url":453,"logo_bg":58,"pricing_model":454,"learning_curve_score":44,"popularity_score":41,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":455,"subcategory":456,"categories":460,"subcategories":462,"flexibility_score":41,"performance_score":41,"portability_score":41,"is_featured":78,"tags":464,"aliases":468,"score_reasonings":469,"published_date":258,"last_updated_date":113},161,"DeepSeek","deepseek","Chinese open-weight LLM family under the MIT license, led by DeepSeek-V4.1-Flash, offering frontier-level results at very low API prices, with full self-hosting through vLLM, SGLang, and Ollama.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdeepseek.svg",{"slug":60,"display_name":61,"description":62},{"category_id":359,"name":346,"slug":360},{"subcategory_id":457,"name":458,"slug":459},52,"Open-weight","open-weight",[461],{"category_id":359,"name":346,"slug":360,"is_primary":3,"display_order":75},[463],{"subcategory_id":457,"name":458,"slug":459,"category_id":359,"is_primary":3,"display_order":75},[465,466,467],{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"learning_curve":470,"popularity":471,"portability":472,"flexibility":473,"performance":474},"The DeepSeek API (OpenAI-compatible) is trivial to call for developers already using OpenAI. Running distilled models via Ollama is also beginner-friendly. The steep part is self-hosting the full 671B V3\u002FR1 models — that requires multi-GPU infrastructure, tensor parallelism configuration in vLLM, and knowledge of quantisation trade-offs.","One of the fastest open-source repository growth stories in GitHub history. DeepSeek-R1 reached ~92K stars within weeks; total deepseek-ai org stars exceeded 170K by end of 2025. Massive developer adoption, significant industry impact, and strong Hugging Face download counts.","MIT license, Hugging Face weights, Ollama\u002FvLLM\u002FLM Studio support, and availability on Amazon Bedrock, Fireworks, Together AI, and OpenRouter. No lock-in whatsoever.","Open MIT weights with no use restrictions, publishable on any infrastructure, fine-tuneable with standard tools, and accessible via multiple APIs (direct, Bedrock, Together AI, Fireworks). Maximum possible flexibility for an LLM.","DeepSeek-V3 and R1 match or exceed GPT-4o on coding (HumanEval, SWE-bench), math (AIME, MATH-500), and reasoning benchmarks. R1 matches OpenAI o1. V4-Pro (2026) rivals the world's top closed models. Exceptional especially on coding tasks.",{"claude":476,"openai":477,"google-gemini":478,"deepseek":479},"The default. Claude holds up well over long tool-calling runs and follows detailed instructions across many steps, which is most of what an agent does. The current Sonnet model costs $2 per million input tokens and $10 per million output, and Haiku at $1 and $5 suits simple steps. Prompt caching cuts the price of the instructions and tool definitions an agent resends on every step.","GPT models have the broadest support across agent frameworks and third-party tools, and many framework examples are written against them first. The mid-tier model is priced like Claude's, at $2 per million input tokens and $10 per million output, and the smallest tier at $0.10 and $0.50 is a cheap way to run high-volume simple steps.","Gemini's Flash and Flash-Lite models are priced below the mid-tier models from Anthropic and OpenAI, and they take images, audio, and video as input, which helps when the agent reads screenshots or scanned documents. Several of them have a rate-limited free tier in Google AI Studio, enough to build and test an agent before paying anything.","The low-cost choice. DeepSeek's API follows the OpenAI format and supports tool calls, and its Flash model costs $0.15 per million input tokens and $0.60 per million output off-peak, with weekday peak hours billed at double. The weights are open, so the same model can later run on your own hardware or at a hosted inference provider. The first-party API is run by a Chinese company, which rules it out under some data policies.",{},{"kind":343,"slug":360,"name":346,"href":482},"\u002Ftools\u002Fcategories\u002Fllm",{"name":484,"tools":485,"descriptions":642,"aliases":648,"see_all":649},"Hosting",[486,519,549,579,614],{"tool_id":487,"name":488,"slug":489,"tooltip_description":490,"logo_url":491,"logo_bg":395,"pricing_model":492,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":493,"subcategory":496,"categories":500,"subcategories":502,"flexibility_score":36,"performance_score":36,"portability_score":44,"is_featured":78,"tags":504,"aliases":512,"score_reasonings":513,"published_date":258,"last_updated_date":113},53,"Railway","railway","Modern PaaS for deploying apps, databases, and workers. Git-based deployment with infrastructure as code approach.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Frailway.svg",{"slug":355,"display_name":356,"description":357},{"category_id":41,"name":494,"slug":495},"Hosting & Cloud","hosting-cloud",{"subcategory_id":497,"name":498,"slug":499},41,"App Hosting","app-hosting",[501],{"category_id":41,"name":494,"slug":495,"is_primary":3,"display_order":75},[503],{"subcategory_id":497,"name":498,"slug":499,"category_id":41,"is_primary":3,"display_order":75},[505,506,507,508],{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":66,"name":284,"slug":285,"tag_type":92},{"tag_id":509,"name":510,"slug":511,"tag_type":92},24,"Docker Compatible","docker-compatible",[],{"learning_curve":514,"performance":515,"portability":516,"flexibility":517,"popularity":518},"Git-push deploys work out of the box; most apps go live with zero configuration.","Container-based deployment mirrors the underlying cloud infrastructure performance.","Docker-based deployment is reasonably portable; switching to another host is manageable.","Dockerfile-based; supports any language, framework, and service composition.","Growing popularity for simple deployments; well-regarded in indie developer communities.",{"tool_id":520,"name":521,"slug":522,"tooltip_description":523,"logo_url":524,"logo_bg":58,"pricing_model":525,"learning_curve_score":44,"popularity_score":44,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":526,"subcategory":527,"categories":528,"subcategories":530,"flexibility_score":41,"performance_score":41,"portability_score":41,"is_featured":78,"tags":532,"aliases":542,"score_reasonings":543,"published_date":258,"last_updated_date":113},167,"Fly.io","fly-io","Docker-native edge hosting platform running apps in Firecracker microVMs across 35+ global regions. Scale-to-zero by default, with managed Postgres and built-in anycast networking.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ffly-io.svg",{"slug":355,"display_name":356,"description":357},{"category_id":41,"name":494,"slug":495},{"subcategory_id":497,"name":498,"slug":499},[529],{"category_id":41,"name":494,"slug":495,"is_primary":3,"display_order":75},[531],{"subcategory_id":497,"name":498,"slug":499,"category_id":41,"is_primary":3,"display_order":75},[533,534,535,536,540,541],{"tag_id":199,"name":200,"slug":201,"tag_type":202},{"tag_id":280,"name":281,"slug":282,"tag_type":92},{"tag_id":66,"name":284,"slug":285,"tag_type":92},{"tag_id":537,"name":538,"slug":539,"tag_type":92},21,"Multi-region","multi-region",{"tag_id":509,"name":510,"slug":511,"tag_type":92},{"tag_id":319,"name":320,"slug":321,"tag_type":105},[],{"learning_curve":544,"flexibility":545,"performance":546,"popularity":547,"portability":548},"Fly.io requires understanding Docker, the flyctl CLI, and fly.toml configuration before deploying anything. Concepts like Machines, volumes, anycast routing, and 6PN networking add further depth. The documentation is excellent, but the on-ramp is noticeably steeper than Railway or Render for developers without container experience.","Full Docker runtime means any language, framework, or dependency is supported. Per-Machine resource allocation, custom scaling rules, volume mounts, multi-region replication, and GPU access cover a wide range of workload types from static sites to stateful ML inference pipelines. The platform imposes almost no constraints on architecture.","Firecracker microVMs deliver near-bare-metal performance with hardware isolation. Anycast routing across 35+ regions means requests hit a local PoP rather than a central origin. Cold starts are measured in milliseconds. Benchmarks consistently show Fly.io at the top tier for global P50 latency among PaaS providers.","Fly.io is a developer favorite in startup and indie-hacker communities, regularly cited in comparisons with Railway and Render. However it remains a niche platform relative to DigitalOcean or AWS. GitHub presence and community forum are active but not at the scale of mainstream cloud providers.","Pure Docker runtime with no proprietary SDK or framework requirements. Workloads can be lifted to any other Docker-compatible host — DigitalOcean, Hetzner, AWS ECS — with minimal configuration changes. Fly Postgres exports standard PostgreSQL dumps. No lock-in beyond fly.toml, which is straightforward to translate.",{"tool_id":550,"name":551,"slug":552,"tooltip_description":553,"logo_url":554,"logo_bg":395,"pricing_model":555,"learning_curve_score":81,"popularity_score":44,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":556,"subcategory":557,"categories":558,"subcategories":560,"flexibility_score":44,"performance_score":44,"portability_score":44,"is_featured":78,"tags":562,"aliases":572,"score_reasonings":573,"published_date":258,"last_updated_date":113},168,"Render","render","Fully managed cloud platform with Git-push deployments, managed Postgres, static sites, background workers, and cron jobs, all from one dashboard without writing infrastructure code.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Frender.svg",{"slug":60,"display_name":61,"description":62},{"category_id":41,"name":494,"slug":495},{"subcategory_id":497,"name":498,"slug":499},[559],{"category_id":41,"name":494,"slug":495,"is_primary":3,"display_order":75},[561],{"subcategory_id":497,"name":498,"slug":499,"category_id":41,"is_primary":3,"display_order":75},[563,564,565,566,570,571],{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":199,"name":200,"slug":201,"tag_type":202},{"tag_id":66,"name":284,"slug":285,"tag_type":92},{"tag_id":567,"name":568,"slug":569,"tag_type":92},23,"Git-based","git-based",{"tag_id":509,"name":510,"slug":511,"tag_type":92},{"tag_id":319,"name":320,"slug":321,"tag_type":105},[],{"learning_curve":574,"flexibility":575,"performance":576,"popularity":577,"portability":578},"Render is among the most beginner-accessible deployment platforms available. No Dockerfile, no CLI, no YAML — connect a repo, and the platform builds and deploys automatically. The dashboard is clean and self-explanatory. Developers new to cloud deployment consistently cite Render as the easiest on-ramp to production hosting.","Covers the standard web app deployment pattern well: HTTP services, workers, cron, databases, and static sites. Docker is supported, and private networking is first-class. However the platform does not offer managed Kubernetes, GPU machines, or fine-grained infrastructure control. Advanced networking, multi-region active-active, and custom VM configurations are out of scope.","Flat-rate VMs with up to 32 GB RAM and 8 CPUs are available for demanding workloads, and zero-downtime deploys keep services live during updates. The free tier's 30–50 second cold start after inactivity is a significant limitation for demos and hobby projects, but paid tiers are always-on. Performance is competitive for a PaaS but not benchmark-leading.","Render is the most commonly cited Heroku replacement in developer communities as of 2026. It appears regularly in Reddit discussions, dev tooling newsletters, and startup stack comparisons. Growth has been strong since Heroku removed its free tier in 2022, though it remains smaller than Railway in developer mindshare.","Workloads on Render can be containerised and moved to any Docker-compatible host with modest effort. Managed Postgres exports standard pg_dump files. However the per-service model and render.yaml configuration are Render-specific; migrating a multi-service app requires translating cron, worker, and networking config into a new platform's conventions.",{"tool_id":580,"name":581,"slug":582,"tooltip_description":583,"logo_url":584,"logo_bg":58,"pricing_model":585,"learning_curve_score":36,"popularity_score":41,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":586,"subcategory":587,"categories":590,"subcategories":592,"flexibility_score":41,"performance_score":41,"portability_score":44,"is_featured":78,"tags":594,"aliases":603,"score_reasonings":608,"published_date":258,"last_updated_date":113},50,"Amazon Web Services","aws","World's largest cloud platform with 200+ services. Market leader in infrastructure as a service (IaaS) and platform as a service (PaaS).","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Faws.png",{"slug":355,"display_name":356,"description":357},{"category_id":41,"name":494,"slug":495},{"subcategory_id":199,"name":588,"slug":589},"Cloud Providers","cloud-providers",[591],{"category_id":41,"name":494,"slug":495,"is_primary":3,"display_order":75},[593],{"subcategory_id":199,"name":588,"slug":589,"category_id":41,"is_primary":3,"display_order":75},[595,596,597,598,599],{"tag_id":280,"name":281,"slug":282,"tag_type":92},{"tag_id":66,"name":284,"slug":285,"tag_type":92},{"tag_id":537,"name":538,"slug":539,"tag_type":92},{"tag_id":509,"name":510,"slug":511,"tag_type":92},{"tag_id":600,"name":601,"slug":602,"tag_type":105},36,"CI\u002FCD","ci-cd",[604,606],{"alias":605,"is_primary":3},"AWS",{"alias":607,"is_primary":3},"Amazon",{"learning_curve":609,"flexibility":610,"performance":611,"portability":612,"popularity":613},"Vast service catalog; IAM, VPC, and networking alone can take months to master.","Hundreds of composable services with full IaC support; virtually unlimited architectural freedom.","Global infrastructure; EC2, Lambda, and CloudFront deliver performance at any scale.","Broad API surface and proprietary services create meaningful lock-in; migration is possible but costly.","Market-leading cloud provider; used by the majority of production deployments globally.",{"tool_id":457,"name":615,"slug":616,"tooltip_description":617,"logo_url":618,"logo_bg":395,"pricing_model":619,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":623,"subcategory":624,"categories":628,"subcategories":630,"flexibility_score":44,"performance_score":36,"portability_score":36,"is_featured":78,"tags":632,"aliases":635,"score_reasonings":636,"published_date":258,"last_updated_date":113},"Hetzner","hetzner","German cloud and dedicated server provider offering VPS, bare-metal servers, and storage at prices far below the big clouds, with data centers in the EU, the US, and Singapore.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fhetzner.svg",{"slug":620,"display_name":621,"description":622},"paid","Paid","No meaningful free tier — a subscription or one-time purchase is required to use the tool.",{"category_id":41,"name":494,"slug":495},{"subcategory_id":625,"name":626,"slug":627},42,"VPS & Servers","vps-and-servers",[629],{"category_id":41,"name":494,"slug":495,"is_primary":3,"display_order":75},[631],{"subcategory_id":625,"name":626,"slug":627,"category_id":41,"is_primary":3,"display_order":75},[633,634],{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":537,"name":538,"slug":539,"tag_type":92},[],{"learning_curve":637,"flexibility":638,"performance":639,"portability":640,"popularity":641},"Standard VPS setup; any developer comfortable with Linux can deploy in an afternoon.","Standard VPS; configure anything on Linux; no managed abstraction layer constraining choices.","Competitive hardware at low cost; dedicated and VPS servers deliver strong baseline performance.","Standard Linux VPS; highly portable with no managed-service abstraction to escape.","Popular among European developers and cost-conscious teams; strong reputation for value.",{"railway":643,"fly-io":644,"render":645,"aws":646,"hetzner":647},"A container platform that runs the FastAPI service as an always-on process, which suits an agent: runs last minutes, responses stream, and background work continues after the request returns. A PostgreSQL service is added to the same project from a template. The Hobby plan is $5 a month including $5 of usage, and memory is billed at about $10 per GB a month, so a small agent API usually stays near the minimum.","Runs the container on machines you place in regions close to your users or your model provider, billed per second while they run. Machines can stop when idle and start on the next request, which cuts the cost of an agent that is used only now and then, at the price of a short wake-up. A small always-on machine with 1 GB of RAM costs under $10 a month. Fly's Managed Postgres is priced for production, so small projects often pair it with a serverless database.","The same always-on service model with a flat price per instance instead of metered usage: $7 a month for the smallest always-on instance and $25 for one with 2 GB of RAM. Background workers and cron jobs are separate service types, useful when agent runs move off the request path. The free instance sleeps after 15 minutes without traffic and takes about a minute to wake, which is fine for a demo and not for an agent that people wait on.","For teams already on AWS. The container runs on ECS with Fargate, PostgreSQL on RDS, and Claude and other models are available through Amazon Bedrock under the same account and bill. Bedrock AgentCore Runtime is a second route built for agents: it hosts LangGraph or CrewAI code with an isolated session per user and sessions lasting up to eight hours, billed by consumption. Expect more setup than a container platform, and tens of dollars a month once a load balancer and a database are running.","A plain VPS for running everything yourself: the FastAPI container, PostgreSQL, and anything else the agent needs, on one machine. A server with 4 GB of RAM costs around €5 to €6 a month, the lowest fixed price here and enough for the API and the database together. You handle deployment, TLS, backups, and updates, and there is no managed database unless you bring one from another provider.",{},{"kind":343,"slug":495,"name":494,"href":650},"\u002Ftools\u002Fcategories\u002Fhosting-cloud",{"vector_db":652,"llm_observability":737,"code_sandbox":802,"model_inference":902,"model_aggregator":1021,"background_jobs":1082,"ci_cd":1123,"containerization":1197},{"name":653,"tools":654,"descriptions":731,"aliases":735,"preface":736,"see_all":33},"Vector Database",[655,682,706],{"tool_id":656,"name":657,"slug":658,"tooltip_description":659,"logo_url":660,"logo_bg":58,"pricing_model":661,"learning_curve_score":44,"popularity_score":36,"hosting_assignment_type":300,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":662,"subcategory":663,"categories":666,"subcategories":668,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":670,"aliases":675,"score_reasonings":676,"published_date":113,"last_updated_date":33},216,"Qdrant","qdrant","An open-source vector database written in Rust, free to self-host under Apache 2.0, with a managed Qdrant Cloud that includes a free-forever cluster and hybrid and private deployment options.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fqdrant.svg",{"slug":60,"display_name":61,"description":62},{"category_id":36,"name":230,"slug":231},{"subcategory_id":36,"name":664,"slug":665},"Vector Databases","vector-databases",[667],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[669],{"subcategory_id":36,"name":664,"slug":665,"category_id":36,"is_primary":3,"display_order":75},[671,672,673,674],{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"learning_curve":677,"flexibility":678,"performance":679,"popularity":680,"portability":681},"Getting a self-hosted instance running via Docker is quick, but making full use of composable retrieval primitives (custom scoring, hybrid dense\u002Fsparse queries) takes more hands-on tuning than a simpler managed alternative.","Composable indexing, scoring, and filtering primitives, deployment via self-hosted Docker\u002FKubernetes or managed cloud, and Hybrid\u002FPrivate Cloud options give it very broad configurability.","Rust-based core and production deployment at scale by companies like Tripadvisor and Canva demonstrate real production-grade throughput, with self-hosting becoming notably cost-efficient at high query volumes.","34K+ GitHub stars and 250M+ downloads, with real, named production adopters, though still behind Pinecone's overall market recognition as the most-referenced managed option.","Apache 2.0 licensed and genuinely self-hostable via Docker or Kubernetes, plus Hybrid\u002FPrivate Cloud for keeping data on customer infrastructure, meaningfully more portable than a closed-source, cloud-only competitor.",{"tool_id":683,"name":684,"slug":685,"tooltip_description":686,"logo_url":687,"logo_bg":58,"pricing_model":688,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":689,"subcategory":690,"categories":691,"subcategories":693,"flexibility_score":44,"performance_score":36,"portability_score":123,"is_featured":78,"tags":695,"aliases":699,"score_reasonings":700,"published_date":113,"last_updated_date":33},215,"Pinecone","pinecone","A fully managed, serverless vector database that stores billions of vectors with no servers to provision, plus hosted embedding and reranking (Pinecone Inference) and a RAG toolkit (Pinecone Assistant).","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpinecone.png",{"slug":60,"display_name":61,"description":62},{"category_id":36,"name":230,"slug":231},{"subcategory_id":36,"name":664,"slug":665},[692],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[694],{"subcategory_id":36,"name":664,"slug":665,"category_id":36,"is_primary":3,"display_order":75},[696,697,698],{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"learning_curve":701,"flexibility":702,"performance":703,"portability":704,"popularity":705},"Creating a serverless index and querying it via the API or SDK requires minimal setup, with Pinecone Inference removing the need to separately wire up an embedding provider for a first RAG pipeline.","Supports dense, sparse, and full-text indexes, Dedicated Read Nodes for read-heavy workloads, and Bring Your Own Cloud, though it remains a single-purpose vector database rather than a general-purpose one.","The serverless architecture and Dedicated Read Nodes are built for production-scale retrieval workloads, with high-availability SLAs on paid tiers backing that up.","Closed-source, fully managed SaaS with no self-hosted option, meaningful vendor lock-in compared to open-source vector databases that can run anywhere.","The default managed vector database referenced in nearly every RAG tutorial and framework integration guide, though vector databases themselves remain a specialty most teams outside AI\u002FML work never need to touch.",{"tool_id":707,"name":708,"slug":709,"tooltip_description":710,"logo_url":711,"logo_bg":58,"pricing_model":712,"learning_curve_score":81,"popularity_score":36,"hosting_assignment_type":300,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":713,"subcategory":714,"categories":715,"subcategories":717,"flexibility_score":36,"performance_score":44,"portability_score":41,"is_featured":78,"tags":719,"aliases":724,"score_reasonings":725,"published_date":113,"last_updated_date":173},217,"Chroma","chroma","Open-source, Apache 2.0 embedding database with a deliberately minimal 4-function API, the go-to choice for prototyping RAG apps before optionally scaling to Chroma Cloud's serverless offering.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fchroma.png",{"slug":60,"display_name":61,"description":62},{"category_id":36,"name":230,"slug":231},{"subcategory_id":36,"name":664,"slug":665},[716],{"category_id":36,"name":230,"slug":231,"is_primary":3,"display_order":75},[718],{"subcategory_id":36,"name":664,"slug":665,"category_id":36,"is_primary":3,"display_order":75},[720,721,722,723],{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"flexibility":726,"performance":727,"popularity":728,"portability":729,"learning_curve":730},"Self-hosted or Chroma Cloud on the same API, dense\u002Fmetadata\u002Ffull-text search combined, and Python\u002FJavaScript clients cover most RAG use cases, though the minimal API trades away some low-level tuning control.","Suitable for prototypes through moderate production workloads, but Chroma Cloud is newer to general availability than competitors with a longer track record at very large scale.","29K+ GitHub stars and status as the default vector database in most LangChain\u002FLlamaIndex RAG tutorials, extremely high mindshare among developers starting a new RAG project.","Apache 2.0 licensed, genuinely self-hostable with a single command, and the same API works locally or on Chroma Cloud, no lock-in to get started.","A deliberately minimal, handful-of-functions API and a single command to get a persistent local instance running make Chroma the fastest of the vector databases to start using productively.",{"qdrant":732,"pinecone":733,"chroma":734},"An open-source vector database that runs as one container beside the API or as a managed cluster, with a free small cluster on Qdrant Cloud for testing. It filters on metadata during the search itself, which matters when an agent must only see one customer's documents. Reach for it when the collection grows into the millions of vectors, or when pgvector queries on the main PostgreSQL database start to slow the agent down.","A fully managed, serverless vector database with nothing to run: you create an index and pay for storage, reads, and writes. Hosted embedding and reranking models are part of the service, so the agent needs no separate embedding provider. The Starter plan is free for prototypes. It is closed source and cloud only, so it fits a team that would rather not operate a database, and not one whose data has to stay on its own servers.","The simplest way to try retrieval. Chroma runs in-process or as a single local server, embeds documents for you, and needs a few lines of Python, which is why so many agent tutorials start with it. Chroma Cloud is the managed version with the same API and usage-based pricing. A good first step when you want a separate store but aren't ready to size and run one.",{},"Add a vector database when the agent needs to find documents, notes, or past conversations by meaning instead of exact keywords.",{"name":738,"tools":739,"descriptions":797,"aliases":800,"preface":801,"see_all":33},"LLM Observability",[740,774],{"tool_id":741,"name":742,"slug":743,"tooltip_description":744,"logo_url":745,"logo_bg":58,"pricing_model":746,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":300,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":747,"subcategory":751,"categories":755,"subcategories":757,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":759,"aliases":767,"score_reasonings":768,"published_date":173,"last_updated_date":33},296,"Langfuse","langfuse","Open-source platform for tracing, evaluating, and monitoring LLM applications and AI agents. It adds prompt management, datasets, and cost and latency analytics, and runs as a managed cloud or self-hosted.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Flangfuse.png",{"slug":60,"display_name":61,"description":62},{"category_id":748,"name":749,"slug":750},18,"Observability & Monitoring","observability-monitoring",{"subcategory_id":752,"name":753,"slug":754},75,"LLM & Agent Observability","llm-agent-observability",[756],{"category_id":748,"name":749,"slug":750,"is_primary":3,"display_order":75},[758],{"subcategory_id":752,"name":753,"slug":754,"category_id":748,"is_primary":3,"display_order":75},[760,761,762,763],{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":764,"name":765,"slug":766,"tag_type":105},35,"Monitoring","monitoring",[],{"performance":769,"learning_curve":770,"flexibility":771,"popularity":772,"portability":773},"The ClickHouse-backed analytics layer handles high trace volumes and fast dashboard queries, and SDKs send traces asynchronously so they add little latency to the application.","Framework integrations add tracing with a callback handler or a decorator, and the cloud version needs only an API key. Evaluations and datasets take more thought but are optional.","OpenTelemetry-based instrumentation, SDKs in Python and TypeScript, integrations for most agent frameworks, a public API, and a self-hostable codebase let teams wire it into nearly any stack.","Around 34K GitHub stars and the most-cited open-source option in LLM observability comparisons, well known to teams shipping LLM features but a young category that few developers outside AI work have looked at yet.","MIT-licensed core, self-hosting on any infrastructure, and OpenTelemetry instrumentation mean traces and prompts can move between the cloud and self-hosted deployments or to another OTel backend.",{"tool_id":775,"name":776,"slug":777,"tooltip_description":778,"logo_url":779,"logo_bg":395,"pricing_model":780,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":781,"subcategory":782,"categories":783,"subcategories":785,"flexibility_score":36,"performance_score":36,"portability_score":123,"is_featured":78,"tags":787,"aliases":790,"score_reasonings":791,"published_date":173,"last_updated_date":33},297,"LangSmith","langsmith","LangChain's platform for tracing, evaluating, and deploying LLM applications and agents. It works with any framework and is tightest with LangChain and LangGraph, adding datasets, prompt tooling, and managed agent hosting.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Flangsmith.png",{"slug":60,"display_name":61,"description":62},{"category_id":748,"name":749,"slug":750},{"subcategory_id":752,"name":753,"slug":754},[784],{"category_id":748,"name":749,"slug":750,"is_primary":3,"display_order":75},[786],{"subcategory_id":752,"name":753,"slug":754,"category_id":748,"is_primary":3,"display_order":75},[788,789],{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":764,"name":765,"slug":766,"tag_type":105},[],{"learning_curve":792,"flexibility":793,"performance":794,"popularity":795,"portability":796},"LangChain and LangGraph apps start tracing by setting an API key and one environment variable, and the UI is built around the concepts those frameworks already use. Evaluations and deployment add more to learn.","Covers tracing, evals, prompts, monitoring, and deployment with SDKs and OpenTelemetry for any framework, but customisation stops at what the hosted product and its API expose.","Handles production trace volumes for large LangChain customers, and its managed deployment runtime supports persistence and background runs for long agents.","The default observability choice for LangChain users and the brand most often named first in LLM tracing comparisons, but the category is still young and little known outside teams building with LLMs.","Traces and datasets live in LangChain's cloud unless an Enterprise contract pays for self-hosting, and agents on LangSmith Deployment are tied to its runtime, although OpenTelemetry ingestion eases instrumenting outside LangChain.",{"langfuse":798,"langsmith":799},"Open-source tracing and evaluation with integrations for LangGraph, LangChain, Pydantic AI, and CrewAI, so every model call and tool call in a run shows up as a nested trace with its cost and latency. The cloud Hobby plan is free up to 50,000 units a month, and Core is $29. It can also be self-hosted for free, which keeps prompts and outputs on your own infrastructure.","LangChain's own platform, and the closest fit when the agent is built on LangGraph or LangChain: tracing is switched on with environment variables, and Studio steps through a graph visually. The Developer plan is free for one seat with 5,000 traces a month, and Plus is $39 per seat. It is a hosted, closed-source service, and self-hosting is limited to the Enterprise plan.",{},"Add LLM observability when you want to see every model call, tool call, and token cost inside a run, so a wrong answer can be traced to the step that caused it.",{"name":803,"tools":804,"descriptions":894,"aliases":898,"preface":899,"see_all":900},"Code Sandbox",[805,836,868],{"tool_id":806,"name":807,"slug":808,"tooltip_description":809,"logo_url":810,"logo_bg":58,"pricing_model":811,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":300,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":812,"subcategory":815,"categories":819,"subcategories":821,"flexibility_score":36,"performance_score":36,"portability_score":36,"is_featured":78,"tags":823,"aliases":829,"score_reasonings":830,"published_date":173,"last_updated_date":33},300,"E2B","e2b","Open-source cloud sandboxes where AI agents safely run the code they generate, each isolated in its own Firecracker microVM. Controlled from Python and JavaScript SDKs, with a managed cloud or self-hosted deployment.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fe2b.png",{"slug":60,"display_name":61,"description":62},{"category_id":537,"name":813,"slug":814},"AI Infrastructure","ai-infrastructure",{"subcategory_id":816,"name":817,"slug":818},76,"Agent Sandboxes & Code Execution","agent-sandboxes",[820],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[822],{"subcategory_id":816,"name":817,"slug":818,"category_id":537,"is_primary":3,"display_order":75},[824,825,826,827,828],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":123,"name":190,"slug":191,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},[],{"learning_curve":831,"flexibility":832,"performance":833,"popularity":834,"portability":835},"Creating a sandbox and running code in it takes a few SDK calls in Python or JavaScript, and the Code Interpreter SDK handles output streaming. Custom templates and self-hosting take more learning.","Custom templates, full filesystem and network access, pause and resume, a graphical desktop mode, and an MCP gateway cover code interpreters, coding agents, and computer-use agents alike.","Sub-second microVM starts and sessions up to 24 hours suit most agent workloads, though container-based sandboxes start faster and nested virtualization adds some overhead on raw compute.","Around 14K GitHub stars and the most-cited name in agent sandbox comparisons, yet the whole category is only a couple of years old and unfamiliar to most developers who aren't building agents.","The SDKs and infrastructure are Apache-2.0 and can be self-hosted, but the documented self-host path targets Google Cloud first, and moving to another sandbox vendor means rewriting SDK calls.",{"tool_id":837,"name":838,"slug":839,"tooltip_description":840,"logo_url":841,"logo_bg":58,"pricing_model":842,"learning_curve_score":123,"popularity_score":44,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":843,"subcategory":844,"categories":845,"subcategories":847,"flexibility_score":36,"performance_score":41,"portability_score":123,"is_featured":78,"tags":849,"aliases":860,"score_reasonings":861,"published_date":173,"last_updated_date":867},301,"Daytona","daytona","Cloud sandbox infrastructure for running AI-generated code, with sandbox creation the vendor puts under 90 milliseconds and sandboxes that persist until deleted. It is controlled through Python, TypeScript, Ruby, Go, and Java SDKs, with per-second billing.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdaytona.svg",{"slug":355,"display_name":356,"description":357},{"category_id":537,"name":813,"slug":814},{"subcategory_id":816,"name":817,"slug":818},[846],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[848],{"subcategory_id":816,"name":817,"slug":818,"category_id":537,"is_primary":3,"display_order":75},[850,851,852,855,859],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":44,"name":86,"slug":87,"tag_type":84},{"tag_id":41,"name":853,"slug":854,"tag_type":84},"Go","go",{"tag_id":856,"name":857,"slug":858,"tag_type":84},7,"Ruby","ruby",{"tag_id":509,"name":510,"slug":511,"tag_type":92},[],{"learning_curve":862,"flexibility":863,"performance":864,"popularity":865,"portability":866},"Creating a sandbox and running code is a couple of SDK calls in any of four languages, and default snapshots already include Python, Node, and their language servers.","Any Docker image, declarative builds, persistent or throwaway sandboxes, volumes, GPUs, computer use, and an MCP server cover most agent designs, within the limits of a managed platform whose code is no longer open.","Sub-100 ms starts from snapshots lead the category in independent comparisons, and massive parallel creation is a core design goal.","Its former open-source repository passed 65K GitHub stars and it is named alongside E2B and Modal in nearly every sandbox comparison, though the category itself is still new to most developers.","Closed-source since June 2026 with a proprietary API and control plane, so leaving means rewriting against another provider's SDK. Customer-managed runners keep compute, but not control, in-house.","2026-10-06",{"tool_id":869,"name":870,"slug":871,"tooltip_description":872,"logo_url":873,"logo_bg":58,"pricing_model":874,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":875,"subcategory":876,"categories":877,"subcategories":879,"flexibility_score":41,"performance_score":41,"portability_score":123,"is_featured":78,"tags":881,"aliases":887,"score_reasonings":888,"published_date":173,"last_updated_date":33},302,"Modal","modal","Serverless cloud for running Python code on CPUs and GPUs, defined entirely in code, with Sandboxes for executing AI-generated code in isolated containers. Billed per second with no infrastructure to manage.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fmodal.svg",{"slug":60,"display_name":61,"description":62},{"category_id":537,"name":813,"slug":814},{"subcategory_id":816,"name":817,"slug":818},[878],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[880],{"subcategory_id":816,"name":817,"slug":818,"category_id":537,"is_primary":3,"display_order":75},[882,883,884,885,886],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":280,"name":281,"slug":282,"tag_type":92},{"tag_id":66,"name":284,"slug":285,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},[],{"learning_curve":889,"flexibility":890,"performance":891,"popularity":892,"portability":893},"A decorator and a few lines of Python deploy a function or start a sandbox with no Docker or cloud console, although tuning images, GPUs, and concurrency for production takes more understanding of the platform.","Arbitrary container images, any GPU class, sandboxes, web endpoints, schedules, and storage primitives let one platform cover agent code execution, inference, training, and data jobs.","Sub-second container starts, fast GPU cold starts, and autoscaling that has handled more than a million sandboxes in a weekend put it at the top of the serverless compute field.","A $4.7B valuation, a few hundred million dollars in annualised revenue, and customers such as Anthropic and Meta make it one of the best-known AI infrastructure names among ML engineers and agent builders, though web-focused developers rarely touch it.","Code is plain Python and images are standard containers, but the decorators, sandbox API, and scheduling model are Modal-specific and there is no way to run the platform elsewhere.",{"e2b":895,"daytona":896,"modal":897},"Sandboxes made for running code a model wrote: each one is a Firecracker microVM with its own kernel, started in a fraction of a second from the Python SDK. The Code Interpreter SDK returns output, errors, and charts to the agent, which covers the usual data-analysis tool. The Hobby plan includes free credits and sessions up to an hour, and Pro is $150 a month for sessions up to 24 hours.","Sandboxes that start in under 100 milliseconds and can stay alive with no time limit, so an agent can keep a working directory, a Git checkout, or a long process between steps. They are built from any Docker image, with file, Git, and process APIs in the Python SDK. New accounts get $200 in compute credits, then billing is per second. Daytona went closed source in June 2026, and the old public repository is archived.","A serverless compute platform whose Sandboxes run generated code in gVisor-isolated containers, defined and launched from Python. Pick it when the agent's code needs a GPU, or when the same project also runs batch jobs or serves a model, since all of it shares one platform. The Starter plan includes $30 of compute a month, and usage is billed per second.",{},"Add a code sandbox when the agent runs code it wrote itself, and that code should execute in an isolated environment away from your server and its credentials.",{"kind":217,"slug":818,"name":817,"href":901},"\u002Ftools\u002Fcategories\u002Fai-infrastructure\u002Fagent-sandboxes",{"name":903,"tools":904,"descriptions":1012,"aliases":1017,"preface":1018,"see_all":1019},"Model Inference",[905,931,958,987],{"tool_id":906,"name":907,"slug":908,"tooltip_description":909,"logo_url":910,"logo_bg":58,"pricing_model":911,"learning_curve_score":81,"popularity_score":36,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":912,"subcategory":913,"categories":917,"subcategories":919,"flexibility_score":44,"performance_score":41,"portability_score":123,"is_featured":78,"tags":921,"aliases":924,"score_reasonings":925,"published_date":113,"last_updated_date":173},220,"Groq","groq","Fast LLM inference on Groq's own LPU hardware: unlike aggregators such as OpenRouter, Groq runs the compute itself, with low time-to-first-token and per-token pricing among the lowest of the major providers.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fgroq.svg",{"slug":355,"display_name":356,"description":357},{"category_id":537,"name":813,"slug":814},{"subcategory_id":914,"name":915,"slug":916},77,"AI Inference Providers","ai-inference-providers",[918],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[920],{"subcategory_id":914,"name":915,"slug":916,"category_id":537,"is_primary":3,"display_order":75},[922,923],{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"flexibility":926,"performance":927,"popularity":928,"portability":929,"learning_curve":930},"Model catalogue is curated around what runs well on LPU hardware, narrower than a general-purpose aggregator, and there's no self-hosting or custom deployment option.","Sub-100ms time-to-first-token and a purpose-built LPU architecture make it the fastest, most consistent inference provider by a real technical margin, not just marketing.","A $650M fundraise and Nvidia's ~$20B LPU licensing deal signal strong external validation, though its curated model catalogue keeps it a step behind aggregators in raw adoption breadth.","Closed, hosted-only SaaS running on proprietary hardware, no self-hosting path and no way to replicate the LPU speed advantage outside Groq's own infrastructure.","OpenAI-compatible API and a single API key get requests flowing in minutes, no infrastructure or model-serving setup required.",{"tool_id":932,"name":933,"slug":934,"tooltip_description":935,"logo_url":936,"logo_bg":58,"pricing_model":937,"learning_curve_score":123,"popularity_score":41,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":938,"subcategory":939,"categories":940,"subcategories":942,"flexibility_score":41,"performance_score":36,"portability_score":36,"is_featured":78,"tags":944,"aliases":949,"score_reasonings":952,"published_date":113,"last_updated_date":33},213,"Hugging Face","hugging-face","The largest model hub and ecosystem in AI — 2M+ models, 500K+ datasets, and 1M+ Spaces, with three distinct ways to run inference (Serverless API, dedicated Inference Endpoints, or 200+ third-party Inference Providers).","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fhugging-face.svg",{"slug":60,"display_name":61,"description":62},{"category_id":537,"name":813,"slug":814},{"subcategory_id":914,"name":915,"slug":916},[941],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[943],{"subcategory_id":914,"name":915,"slug":916,"category_id":537,"is_primary":3,"display_order":75},[945,946,947,948],{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":98,"name":99,"slug":100,"tag_type":92},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[950],{"alias":951,"is_primary":3},"HF",{"learning_curve":953,"flexibility":954,"performance":955,"popularity":956,"portability":957},"Browsing and downloading a model from the Hub or calling the Serverless Inference API requires minimal setup, though choosing correctly between Serverless, Endpoints, and Providers for a given workload takes some familiarity with the platform.","The largest model\u002Fdataset catalogue in the industry, three distinct inference paths for different scale and cost needs, and a framework-agnostic library make it broadly applicable across nearly any ML workflow.","Serverless is fine for lighter workloads, Inference Endpoints and Providers scale to production-grade throughput, though performance ultimately depends on the specific model and tier chosen rather than a single fixed baseline.","The largest and most recognized hub in the ML ecosystem, with the transformers library serving as a near-universal standard across the broader industry.","Models hosted on the Hub can generally be downloaded and self-hosted elsewhere (via transformers, Ollama, vLLM, etc.), and the Apache-2.0-licensed transformers library itself has no vendor lock-in, though Inference Endpoints and Providers are Hugging-Face-managed services.",{"tool_id":959,"name":960,"slug":961,"tooltip_description":962,"logo_url":963,"logo_bg":395,"pricing_model":964,"learning_curve_score":81,"popularity_score":41,"hosting_assignment_type":228,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":965,"subcategory":966,"categories":970,"subcategories":972,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":974,"aliases":979,"score_reasonings":980,"published_date":113,"last_updated_date":986},210,"Ollama","ollama","The most widely used way to run open-weight LLMs locally — one command downloads and serves models like Llama, Qwen, DeepSeek, GLM, and MiniMax through an OpenAI-compatible API, with an optional paid Ollama Cloud tier for larger models than local hardware can handle.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Follama.svg",{"slug":60,"display_name":61,"description":62},{"category_id":537,"name":813,"slug":814},{"subcategory_id":967,"name":968,"slug":969},60,"AI Runtime & Serving","ai-runtime-serving",[971],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[973],{"subcategory_id":967,"name":968,"slug":969,"category_id":537,"is_primary":3,"display_order":75},[975,976,977,978],{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":194,"name":195,"slug":196,"tag_type":92},{"tag_id":98,"name":99,"slug":100,"tag_type":92},[],{"learning_curve":981,"flexibility":982,"performance":983,"popularity":984,"portability":985},"A single install command and a single command to pull and run a model make it the lowest-friction way to try a local LLM, with no configuration required to get started.","100+ supported models, an OpenAI-compatible API for drop-in tooling reuse, and both local and cloud execution modes give it very broad applicability across workflows.","Automatic hardware tuning and support for the latest open-weight models keep it competitive for local inference, though it is not purpose-built for high-throughput production serving the way dedicated inference servers are.","The most widely used local LLM runtime by a clear margin, with 178K+ GitHub stars, 52 million monthly downloads, and 2.5 billion+ cumulative downloads.","MIT-licensed, runs on macOS, Windows, and Linux across Apple Silicon, NVIDIA, and AMD hardware, and models are entirely self-hosted with no forced cloud dependency.","2026-09-28",{"tool_id":988,"name":989,"slug":990,"tooltip_description":991,"logo_url":992,"logo_bg":58,"pricing_model":993,"learning_curve_score":36,"popularity_score":36,"hosting_assignment_type":228,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":994,"subcategory":995,"categories":996,"subcategories":998,"flexibility_score":41,"performance_score":41,"portability_score":41,"is_featured":78,"tags":1000,"aliases":1005,"score_reasonings":1006,"published_date":173,"last_updated_date":33},298,"vLLM","vllm","Open-source, high-throughput inference and serving engine for large language models, exposing an OpenAI-compatible API. It is the most common way to self-host open-weight models in production on GPU and accelerator clusters.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fvllm.svg",{"slug":121,"display_name":90,"description":122},{"category_id":537,"name":813,"slug":814},{"subcategory_id":967,"name":968,"slug":969},[997],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[999],{"subcategory_id":967,"name":968,"slug":969,"category_id":537,"is_primary":3,"display_order":75},[1001,1002,1003,1004],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},[],{"learning_curve":1007,"flexibility":1008,"performance":1009,"popularity":1010,"portability":1011},"Starting a server is one command, but running it well in production means understanding GPU memory, KV-cache sizing, quantization, and multi-GPU parallelism, plus the Kubernetes layer around it.","Serves hundreds of model architectures with configurable quantization, parallelism, LoRA adapters, structured outputs, and speculative decoding, and can be embedded as a Python library or run as a server.","PagedAttention and continuous batching set the throughput bar that other open-source engines are measured against, and the V1 engine cut scheduling overhead further.","Around 93K GitHub stars and the engine underneath many hosted inference services, so anyone self-hosting models knows it, while developers who only call hosted APIs rarely touch it directly.","Apache-2.0, runs on GPUs and accelerators from several vendors, installs anywhere Python or Docker runs, and exposes a standard OpenAI-style API.",{"groq":1013,"hugging-face":1014,"ollama":1015,"vllm":1016},"A hosted inference provider that runs open-weight models such as Llama, Qwen, and Kimi on its own LPU chips, with very fast output and an OpenAI-compatible API. Speed matters for agents because every step waits on a model response. Billing is per token, with a free rate-limited tier. Other hosted providers, such as Together AI and Fireworks AI, connect the same way.","The hub where open-weight models are published, with two ways to run them. Inference Providers routes one API key to many hosted providers at their own prices, and Inference Endpoints gives a model its own dedicated GPU server billed by the hour. Pick it when the model you want is a specific fine-tune from the Hub, not one of the popular models every provider carries.","Runs an open-weight model on your own machine with one command and serves it on an OpenAI-compatible API, so the agent talks to a local address instead of a paid endpoint. Good for development without token costs and for data that must not leave the server. The model has to support tool calling, and small local models are weaker at multi-step tool use than the large hosted ones, so test the agent's hardest task first.","The serving engine for running an open-weight model on your own GPUs for many users at once. It batches concurrent requests and exposes an OpenAI-compatible API, so the agent code doesn't change. It earns its place when volume makes per-token pricing more expensive than a GPU server, or when policy requires the model inside your own cloud. It needs a datacenter-class GPU and someone to operate it.",{},"Add model inference when you want an open-weight model in the mix: on your own hardware for privacy, or on a hosted inference provider for speed and low per-token prices.",{"kind":217,"slug":969,"name":968,"href":1020},"\u002Ftools\u002Fcategories\u002Fai-infrastructure\u002Fai-runtime-serving",{"name":1022,"tools":1023,"descriptions":1075,"aliases":1078,"preface":1079,"see_all":1080},"Model Aggregator",[1024,1050],{"tool_id":1025,"name":1026,"slug":1027,"tooltip_description":1028,"logo_url":1029,"logo_bg":58,"pricing_model":1030,"learning_curve_score":81,"popularity_score":36,"hosting_assignment_type":266,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":1031,"subcategory":1032,"categories":1036,"subcategories":1038,"flexibility_score":36,"performance_score":36,"portability_score":44,"is_featured":78,"tags":1040,"aliases":1043,"score_reasonings":1044,"published_date":113,"last_updated_date":173},218,"OpenRouter","openrouter","Unified, OpenAI-compatible API to 500+ models across 80+ providers, with automatic provider fallback and one credits-based bill: the hosted, nothing-to-deploy counterpart to a self-hosted gateway like LiteLLM.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fopenrouter.svg",{"slug":355,"display_name":356,"description":357},{"category_id":537,"name":813,"slug":814},{"subcategory_id":1033,"name":1034,"slug":1035},63,"AI Model Aggregators","ai-model-aggregators",[1037],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[1039],{"subcategory_id":1033,"name":1034,"slug":1035,"category_id":537,"is_primary":3,"display_order":75},[1041,1042],{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"flexibility":1045,"performance":1046,"portability":1047,"popularity":1048,"learning_curve":1049},"500+ models across 80+ providers, custom data policies, and a Bring-Your-Own-Key mode cover most routing needs, though there's no control over the underlying routing logic the way a self-hosted gateway offers.","Edge-deployed routing and automatic provider fallback keep added latency low and reliability high, backed by real scale (200+ trillion tokens\u002Fmonth).","Closed-source, hosted-only SaaS with no self-hosting option, credits and API keys are OpenRouter-specific and don't transfer if switching to a self-hosted gateway.","Aggregates 400+ models from 60+ providers behind one OpenAI-style API and is usually the first stop for developers who want to try many models without juggling separate provider keys. A fixture of the LLM-app-building corner of AI development specifically, not a name outside that corner.","A single OpenAI-compatible API key and endpoint gives instant access to hundreds of models, no infrastructure setup or provider-specific integration work required.",{"tool_id":1051,"name":1052,"slug":1053,"tooltip_description":1054,"logo_url":1055,"logo_bg":58,"pricing_model":1056,"learning_curve_score":123,"popularity_score":36,"hosting_assignment_type":228,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":1057,"subcategory":1058,"categories":1059,"subcategories":1061,"flexibility_score":41,"performance_score":36,"portability_score":41,"is_featured":78,"tags":1063,"aliases":1068,"score_reasonings":1069,"published_date":113,"last_updated_date":867},221,"LiteLLM","litellm","The open-source AI gateway you run yourself: one OpenAI-compatible endpoint in front of more than 100 model providers, with virtual keys, budgets, and spend tracking. It is the self-hosted counterpart to OpenRouter.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Flitellm.png",{"slug":60,"display_name":61,"description":62},{"category_id":537,"name":813,"slug":814},{"subcategory_id":1033,"name":1034,"slug":1035},[1060],{"category_id":537,"name":813,"slug":814,"is_primary":3,"display_order":75},[1062],{"subcategory_id":1033,"name":1034,"slug":1035,"category_id":537,"is_primary":3,"display_order":75},[1064,1065,1066,1067],{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":136,"name":137,"slug":138,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"learning_curve":1070,"flexibility":1071,"performance":1072,"popularity":1073,"portability":1074},"Requires standing up and maintaining real infrastructure, PostgreSQL, Redis, the proxy server itself, meaningfully more setup than a zero-ops hosted aggregator like OpenRouter.","Full control over routing logic, guardrails, virtual keys, and spend limits across 100+ providers, the most configurable option in the aggregator genre precisely because you own the deployment.","8ms P95 latency at 1,000 RPS and a Rust core deliver genuinely fast proxy performance, though real-world throughput depends on the self-hosted infrastructure backing it.","56K+ GitHub stars and production adoption at Stripe, Google ADK, Greptile, and OpenHands make it the clear self-hosted leader in this genre.","MIT licensed, genuinely self-hostable anywhere with Docker or the provided Terraform modules, no vendor lock-in to a hosted service.",{"openrouter":1076,"litellm":1077},"One API key and one prepaid balance for hundreds of models from many providers, behind an OpenAI-compatible endpoint that every framework here can call. Useful for comparing models on the same agent without opening an account with each provider, and for automatic fallback when one provider is down. Models are billed at the providers' prices, with a 5.5% fee when you buy credits.","An open-source gateway you host yourself, placed between the agent and the model providers. It gives one OpenAI-compatible endpoint, budgets and spend tracking per key, and fallback rules, while requests still go from your server to each provider under your own API keys. It stores keys and spend in PostgreSQL, which this stack already runs, and it is one more service to deploy and keep running.",{},"Add a model aggregator when you want one API key and one bill for models from many providers, with automatic fallback when one of them is down, instead of setting up each provider separately.",{"kind":217,"slug":1035,"name":1034,"href":1081},"\u002Ftools\u002Fcategories\u002Fai-infrastructure\u002Fai-model-aggregators",{"name":1083,"tools":1084,"descriptions":1117,"aliases":1119,"preface":1120,"see_all":1121},"Background Jobs",[1085],{"tool_id":1086,"name":1087,"slug":1088,"tooltip_description":1089,"logo_url":1090,"logo_bg":58,"pricing_model":1091,"learning_curve_score":44,"popularity_score":44,"hosting_assignment_type":33,"hosting_provider_restriction":64,"hosting_target_restriction":64,"hosting_compatible_tool_ids":33,"parent_tool_id":33,"category":1092,"subcategory":1095,"categories":1099,"subcategories":1101,"flexibility_score":36,"performance_score":36,"portability_score":41,"is_featured":78,"tags":1103,"aliases":1109,"score_reasonings":1110,"published_date":1116,"last_updated_date":33},315,"Celery","celery","Celery is the standard Python task queue: it turns functions into background jobs that separate worker processes run, with retries and scheduling, using Redis or RabbitMQ as the broker.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fcelery.svg",{"slug":121,"display_name":90,"description":122},{"category_id":89,"name":1093,"slug":1094},"APIs & Infrastructure","apis-infrastructure",{"subcategory_id":1096,"name":1097,"slug":1098},79,"Message Queues & Background Jobs","message-queues-background-jobs",[1100],{"category_id":89,"name":1093,"slug":1094,"is_primary":3,"display_order":75},[1102],{"subcategory_id":1096,"name":1097,"slug":1098,"category_id":89,"is_primary":3,"display_order":75},[1104,1105,1106,1107,1108],{"tag_id":81,"name":82,"slug":83,"tag_type":84},{"tag_id":89,"name":90,"slug":91,"tag_type":92},{"tag_id":94,"name":95,"slug":96,"tag_type":92},{"tag_id":319,"name":320,"slug":321,"tag_type":105},{"tag_id":199,"name":200,"slug":201,"tag_type":202},[],{"learning_curve":1111,"flexibility":1112,"performance":1113,"popularity":1114,"portability":1115},"A first task and worker take a few lines, but brokers, result backends, acknowledgement settings, and worker pools add a configuration layer that takes time to get right in production.","Two stable brokers, many result backends, canvas workflows, scheduling, and routing cover most background-job patterns, though it stays Python-only and not async-native.","Scales horizontally by adding workers, and the prefork pool handles CPU-bound jobs well; overall throughput depends on the broker underneath.","The best-known task queue in the Python ecosystem with tens of millions of monthly downloads, but developers outside Python backends rarely meet it.","BSD-licensed, self-hosted, and broker-agnostic between Redis and RabbitMQ, so it runs on any host or cloud without lock-in.","2026-10-12",{"celery":1118},"Agent runs can take minutes, longer than a request should stay open. FastAPI's BackgroundTasks start them in-process but lose them on a restart, while Celery runs each invocation in a separate worker, so the API returns a run ID at once and the client polls for the result. LangGraph's PostgreSQL checkpointer keeps the graph's state between steps, so a retried task can continue from the last checkpoint instead of starting over. Celery needs a broker such as Redis, which this stack does not otherwise run.",{},"Add background jobs when work should run outside the request and must survive restarts, retry on failure, or run on a schedule: a separate worker takes it from a queue. 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concerns.",[1461,1466,1467,1468,1469],{"tool_id":1462,"slug":1463,"name":1464,"logo_url":1465,"logo_bg":58},101,"vue","Vue.js","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fvue.svg",{"tool_id":280,"slug":1274,"name":1273,"logo_url":1276,"logo_bg":58},{"tool_id":36,"slug":83,"name":82,"logo_url":1303,"logo_bg":58},{"tool_id":1440,"slug":87,"name":86,"logo_url":1441,"logo_bg":58},{"tool_id":199,"slug":224,"name":223,"logo_url":226,"logo_bg":58},{"stack_id":1471,"slug":1472,"name":1473,"tagline":1474,"experience_level":1448,"project_type":1449,"stack_type_slug":1411,"stack_type_icon_url":1412,"score_popularity":123,"score_learning_curve":44,"catalog_display_order":33,"published_date":33,"last_updated_date":33,"core_tool_previews":1475},44,"svelte-fastapi","Svelte + FastAPI","Svelte SPA frontend with FastAPI backend: minimal JavaScript output meets Python API 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framework.",[1545,1546,1550,1551,1552],{"tool_id":1500,"slug":1501,"name":1502,"logo_url":1503,"logo_bg":58},{"tool_id":194,"slug":1547,"name":1548,"logo_url":1549,"logo_bg":395},"django","Django","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdjango.svg",{"tool_id":36,"slug":83,"name":82,"logo_url":1303,"logo_bg":58},{"tool_id":600,"slug":1507,"name":1508,"logo_url":1509,"logo_bg":395},{"tool_id":1511,"slug":1512,"name":1513,"logo_url":1514,"logo_bg":395},{"stack_id":1554,"slug":1555,"name":1556,"tagline":1557,"experience_level":1448,"project_type":1488,"stack_type_slug":1411,"stack_type_icon_url":1412,"score_popularity":36,"score_learning_curve":123,"catalog_display_order":33,"published_date":33,"last_updated_date":33,"core_tool_previews":1558},116,"fastapi-supabase","FastAPI + Supabase","FastAPI backend using Supabase as the database, auth, and storage layer.",[1559,1560,1561],{"tool_id":280,"slug":1274,"name":1273,"logo_url":1276,"logo_bg":58},{"tool_id":36,"slug":83,"name":82,"logo_url":1303,"logo_bg":58},{"tool_id":81,"slug":296,"name":295,"logo_url":298,"logo_bg":58},{"stack_id":1500,"slug":1563,"name":1564,"tagline":1565,"experience_level":1448,"project_type":1449,"stack_type_slug":1411,"stack_type_icon_url":1412,"score_popularity":123,"score_learning_curve":44,"catalog_display_order":33,"published_date":33,"last_updated_date":33,"core_tool_previews":1566},"fastapi-supabase-htmx","FastAPI + Supabase + HTMX","FastAPI serving HTMX-enhanced templates with Supabase for data and auth: interactive Python without a JS framework.",[1567,1568,1569,1570,1571],{"tool_id":1500,"slug":1501,"name":1502,"logo_url":1503,"logo_bg":58},{"tool_id":280,"slug":1274,"name":1273,"logo_url":1276,"logo_bg":58},{"tool_id":36,"slug":83,"name":82,"logo_url":1303,"logo_bg":58},{"tool_id":600,"slug":1507,"name":1508,"logo_url":1509,"logo_bg":395},{"tool_id":1511,"slug":1512,"name":1513,"logo_url":1514,"logo_bg":395},[1573,1576,1579,1582,1585],{"question":1574,"answer":1575},"Which agent framework should I start with?","Start from the shape of the agent. If it is one model with a set of tools and you care about typed, validated output, Pydantic AI is the least code and feels like FastAPI. If the run has branches, loops, approval steps, or several agents handing off work, LangGraph gives you explicit control and saves state to PostgreSQL after every step. LangChain's create_agent is the quick route to a standard tool-calling agent and runs on LangGraph underneath, so you can move down a level later without changing libraries. CrewAI fits work that divides into roles, such as a researcher and a writer. Switching later means rewriting the agent code, but the API, the database, and the host stay as they are.",{"question":1577,"answer":1578},"Do I need a vector database, or is PostgreSQL enough?","For most agents PostgreSQL is enough to begin with. The pgvector extension stores embeddings in the database this stack already runs, and with an index it works well into the millions of vectors. That keeps retrieval, run state, and your own tables in one place with one backup. Add a dedicated vector database when the collection is much larger, when you filter heavily on metadata at high query rates, or when search load starts competing with the agent's own writes. Qdrant is the usual self-hosted choice, Pinecone the fully managed one, and Chroma the simplest to try. If searching documents is the whole job, the [Python RAG App](\u002Fstacks\u002Fpython-rag-app) stack is built around ingestion and retrieval, with the vector store as a decision you make up front.",{"question":1580,"answer":1581},"What drives the model bill, and how do I keep it down?","Token spend is the bill that grows, not hosting. An agent resends its instructions, its tool definitions, and the conversation so far on every step, so a ten-step run costs far more than ten single questions. Three things help most. Prompt caching, which Claude and OpenAI both discount heavily for repeated input. Routing simple steps to a small model and keeping the larger one for planning. And a cap on the number of steps a run may take. If you add tracing with Langfuse or LangSmith, it shows the cost of each run and which step is the expensive one. The Pricing section has the per-token rates.",{"question":1583,"answer":1584},"How is this different from the TypeScript AI Agent stack?","The [TypeScript AI Agent](\u002Fstacks\u002Ftypescript-ai-agent) stack puts the agent and its chat interface in one [Next.js](\u002Ftools\u002Fnextjs) app, which is the faster route when the product is a conversation in a browser. This stack is a backend only: FastAPI serves the agent as an API, and any interface is a separate project that calls it. In exchange, Python has the wider choice of agent frameworks, evaluation tools, and data libraries, and a long-lived FastAPI process suits background agents, heavy retrieval, and data analysis better than serverless functions do. Both stacks use PostgreSQL and the same models, so the choice is mostly about language and where the interface lives.",{"question":1586,"answer":1587},"Can I add a frontend to this stack?","Yes. The stack is an API, so the interface is a separate piece that calls it, and there are three common routes. A single-page app in [React](\u002Ftools\u002Freact) or [Vue](\u002Ftools\u002Fvue) talks to the FastAPI endpoints, which is the shape of the [Python Web (FastAPI + React)](\u002Fstacks\u002Fpython-web-fastapi-react) and [Vue + FastAPI](\u002Fstacks\u002Fvue-fastapi) stacks. A [Next.js](\u002Ftools\u002Fnextjs) app can render the chat with ready-made hooks while the agent stays in Python, because Pydantic AI can stream in the format those hooks read. Or the same FastAPI app can serve HTML templates with [HTMX](\u002Ftools\u002Fhtmx) for a simple internal screen. If the chat interface is the product and you would rather write everything in one language, the [TypeScript AI Agent](\u002Fstacks\u002Ftypescript-ai-agent) stack includes it.",{"summary":1589,"starting_cost_label":1590,"has_free_tier":3,"line_items":1591},"The agent frameworks, FastAPI, and PostgreSQL are open source and free. A small always-on server with its database costs roughly $5 to $15 a month on a container platform or a VPS. The model is the line that grows: mid-tier models from Anthropic and OpenAI cost $2 per million input tokens and $10 per million output, small models a tenth of that or less, and an agent makes many calls per task. Tracing, sandboxes, and a vector database are optional and have free tiers.","From ~$5\u002Fmo plus model usage",[1592,1596,1600,1604],{"label":1593,"cost":1594,"note":1595},"Agent framework and API","Free (open source)","LangGraph, Pydantic AI, CrewAI, LangChain, and FastAPI are free to use.",{"label":1597,"cost":1598,"note":1599},"Model usage","Usage-based","About $2 per million input tokens and $10 per million output on mid-tier models; small models from $0.10 and $0.50. Usually the largest line.",{"label":1601,"cost":1602,"note":1603},"Hosting (PaaS, cloud, or VPS)","$5–25\u002Fmo","An always-on container or a small server. A free instance that sleeps when idle is enough for a demo.",{"label":1605,"cost":1606,"note":1607},"Database (PostgreSQL)","Free–$25\u002Fmo","Free on the same server or on a serverless free plan; managed plans run from usage-based to $25 a month.","2026-10-16",{"title":1610,"description":1611,"og_image":33,"canonical":1612},"Python AI Agent: Tools, Pricing & How to Deploy | Tekyous","A code-first AI agent in Python: an agent framework for the loop, an API in front, and a dat… Compare Python AI Agent tools, pricing & how to deploy on Tekyous.","https:\u002F\u002Ftekyous.dev\u002Fstacks\u002Fpython-ai-agent",[],1792917293260]