[{"data":1,"prerenderedAt":444},["ShallowReactive",2],{"categories-init":3,"stack-n8n-streamlit-ai":4},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":32,"scores":33,"options":41,"additions":159,"option_groups":160,"multi_select_option_types":161,"tools_by_category":162,"related_stacks":339,"faqs":408,"pricing":424,"system_requirements":32,"experience_level":372,"project_type":397,"stack_type_slug":347,"stack_type_icon_url":348,"published_date":105,"last_updated_date":32,"seo_meta":440},186,"n8n-streamlit-ai","n8n + Streamlit AI Agent","n8n orchestrates AI workflows surfaced as an interactive Streamlit dashboard.","This stack combines n8n's workflow automation with Streamlit's interactive Python UI to create **AI-powered internal tools**. n8n handles the heavy lifting (calling LLM APIs, fetching external data, and storing results in PostgreSQL) while Streamlit provides a dashboard interface where users can trigger workflows, view outputs, and interact with AI results.\n\nPython scripts called from Streamlit invoke n8n webhooks or query PostgreSQL directly for workflow results. This separation keeps the **automation logic in n8n** (where non-developers can inspect and modify it) and the UI layer in Streamlit (where Python data scientists can customize it). Claude or another LLM sits in the n8n workflow, processing incoming data before it reaches the Streamlit display layer.\n\nThis stack is well-suited for data and ops teams building **AI-assisted review tools**, content generation dashboards, and automated reporting UIs that mix LLM output with database-backed data.",[11,12,13,14,15,16],"n8n orchestrates LLM calls, data fetching, and multi-step AI processing","Streamlit Python UI for interactive display and workflow triggering","PostgreSQL stores AI workflow results for display and audit history","Claude or other LLM integrated in n8n for text processing and generation","Webhook triggers connect Streamlit actions to n8n workflow executions","Python data manipulation between the database and Streamlit display layer",[18,19,20,21,22],"AI-assisted content review dashboards where editors approve LLM-generated drafts","Automated reporting tools that combine database data with LLM summaries","Internal AI tools for operations teams that need a UI without a full web app","Data quality monitoring dashboards powered by AI classification workflows","Research tools that query external APIs, process with LLM, and display results",[24,25,26,27],"Streamlit UI requires no frontend development knowledge","n8n keeps automation logic visible and modifiable by non-developers","PostgreSQL provides a persistent audit trail of AI workflow outputs","Combining both tools covers the full stack without a custom backend",[29,30,31],"Two-tool architecture adds operational complexity for simple use cases","Streamlit is not suitable for high-traffic user-facing products","Debugging issues across n8n, PostgreSQL, and Streamlit can be time-consuming",null,{"popularity":34,"learning_curve":36,"flexibility":37,"performance":39,"portability":40},{"score":35,"reasoning":32},3,{"score":35,"reasoning":32},{"score":38,"reasoning":32},4,{"score":35,"reasoning":32},{"score":38,"reasoning":32},{"database":42,"orm":46,"authentication":50,"analytics":54,"coding_agent":58,"llm":62,"language":135,"frontend_framework":139,"cms":143,"hosting":147,"reverse_proxy":151,"self_hosted_paas":155},{"tools":43,"descriptions":44,"aliases":45,"see_all":32},[],{},{},{"tools":47,"descriptions":48,"aliases":49,"see_all":32},[],{},{},{"tools":51,"descriptions":52,"aliases":53,"see_all":32},[],{},{},{"tools":55,"descriptions":56,"aliases":57,"see_all":32},[],{},{},{"tools":59,"descriptions":60,"aliases":61,"see_all":32},[],{},{},{"tools":63,"descriptions":128,"aliases":131,"see_all":132},[64,106],{"tool_id":65,"name":66,"slug":67,"tooltip_description":68,"logo_url":69,"logo_bg":70,"pricing_model":71,"learning_curve_score":75,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":78,"subcategory":82,"categories":86,"subcategories":89,"flexibility_score":38,"performance_score":76,"portability_score":35,"is_featured":91,"tags":92,"score_reasonings":98,"published_date":104,"last_updated_date":105},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 extended thinking.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fclaude.svg","dark",{"slug":72,"display_name":73,"description":74},"usage_based","Usage-Based","Pricing scales with consumption: API calls, data volume, compute time, or similar metered units.",2,5,"open",{"category_id":79,"name":80,"slug":81},19,"LLM","llm",{"subcategory_id":83,"name":84,"slug":85},51,"Proprietary","proprietary",[87],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},0,[90],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},false,[93],{"tag_id":94,"name":95,"slug":96,"tag_type":97},40,"Web","web","platform",{"learning_curve":99,"flexibility":100,"performance":101,"popularity":102,"portability":103},"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.","2026-05-29","2026-09-27",{"tool_id":107,"name":108,"slug":109,"tooltip_description":110,"logo_url":111,"logo_bg":112,"pricing_model":113,"learning_curve_score":75,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":114,"subcategory":115,"categories":116,"subcategories":118,"flexibility_score":35,"performance_score":76,"portability_score":75,"is_featured":91,"tags":120,"score_reasonings":122,"published_date":104,"last_updated_date":105},155,"OpenAI","openai","OpenAI's API platform for the GPT-6 family (Astra, Sol, Luna), Codex coding models, real-time voice, image generation, and embeddings, with tool use, structured outputs, and fine-tuning.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fopenai.svg","white",{"slug":72,"display_name":73,"description":74},{"category_id":79,"name":80,"slug":81},{"subcategory_id":83,"name":84,"slug":85},[117],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},[119],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},[121],{"tag_id":94,"name":95,"slug":96,"tag_type":97},{"performance":123,"popularity":124,"portability":125,"learning_curve":126,"flexibility":127},"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.",{"claude":129,"openai":130},"Claude is the default pick for this stack's agent workflows: strong instruction-following and long-context handling suit multi-step n8n logic that mixes fetched data with prompt context, and per-token pricing across several model tiers lets a dashboard step down capability (and cost) where the task is simple.","OpenAI is the alternative when the workflow leans on its ecosystem: the most widely integrated LLM across n8n's community nodes and AI tooling, with model tiers from cost-efficient to flagship reasoning, all billed per token. Swapping it in is a model-node change in n8n; the Streamlit and PostgreSQL layers are unaffected.",{},{"kind":133,"slug":81,"name":80,"href":134},"category","\u002Ftools\u002Fcategories\u002Fllm",{"tools":136,"descriptions":137,"aliases":138,"see_all":32},[],{},{},{"tools":140,"descriptions":141,"aliases":142,"see_all":32},[],{},{},{"tools":144,"descriptions":145,"aliases":146,"see_all":32},[],{},{},{"tools":148,"descriptions":149,"aliases":150,"see_all":32},[],{},{},{"tools":152,"descriptions":153,"aliases":154,"see_all":32},[],{},{},{"tools":156,"descriptions":157,"aliases":158,"see_all":32},[],{},{},{},{},[],{"Programming Languages":163,"Databases":211,"Automation & Integration":248,"Data Apps":290,"LLM":327},[164],{"tool_id":38,"name":165,"slug":166,"tooltip_description":167,"logo_url":168,"logo_bg":70,"pricing_model":169,"learning_curve_score":75,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":173,"subcategory":32,"categories":176,"subcategories":178,"flexibility_score":76,"performance_score":35,"portability_score":76,"is_featured":3,"tags":179,"score_reasonings":205,"published_date":104,"last_updated_date":105},"Python","python","Python is a high-level, interpreted, dynamically typed programming language emphasising readability and simplicity. It dominates data science, machine learning, and general-purpose scripting.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpython.svg",{"slug":170,"display_name":171,"description":172},"open_source","Open Source","Source code is publicly available and free to use, modify, and distribute. No paid plans from the project itself.",{"category_id":35,"name":174,"slug":175},"Programming Languages","programming-languages",[177],{"category_id":35,"name":174,"slug":175,"is_primary":3,"display_order":88},[],[180,183,187,192,196,201],{"tag_id":181,"name":165,"slug":166,"tag_type":182},1,"technology",{"tag_id":184,"name":171,"slug":185,"tag_type":186},11,"open-source","feature",{"tag_id":188,"name":189,"slug":190,"tag_type":191},25,"Machine Learning","machine-learning","use_case",{"tag_id":193,"name":194,"slug":195,"tag_type":191},39,"Data Science","data-science",{"tag_id":197,"name":198,"slug":199,"tag_type":200},48,"Functional","functional","paradigm",{"tag_id":202,"name":203,"slug":204,"tag_type":200},49,"Object-oriented","object-oriented",{"learning_curve":206,"flexibility":207,"performance":208,"popularity":209,"portability":210},"Clean, readable syntax with vast learning resources; beginner-friendly from day one.","No constraints; equally suited to scripting, data science, web servers, and systems programming.","Interpreted and GIL-limited; efficient for I\u002FO-bound work but slow for CPU-intensive tasks.","The most widely used programming language globally; dominant in data science, AI, and automation.","Universal language; skills transfer across every domain and environment.",[212],{"tool_id":94,"name":213,"slug":214,"tooltip_description":215,"logo_url":216,"logo_bg":70,"pricing_model":217,"learning_curve_score":35,"popularity_score":76,"hosting_assignment_type":218,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":219,"subcategory":222,"categories":225,"subcategories":227,"flexibility_score":76,"performance_score":38,"portability_score":76,"is_featured":3,"tags":229,"score_reasonings":242,"published_date":104,"last_updated_date":105},"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":170,"display_name":171,"description":172},"deployable",{"category_id":38,"name":220,"slug":221},"Databases","databases",{"subcategory_id":188,"name":223,"slug":224},"OLTP Databases","oltp-databases",[226],{"category_id":38,"name":220,"slug":221,"is_primary":3,"display_order":88},[228],{"subcategory_id":188,"name":223,"slug":224,"category_id":38,"is_primary":3,"display_order":88},[230,233,234,238],{"tag_id":38,"name":231,"slug":232,"tag_type":182},"SQL","sql",{"tag_id":184,"name":171,"slug":185,"tag_type":186},{"tag_id":235,"name":236,"slug":237,"tag_type":186},12,"Self-hostable","self-hostable",{"tag_id":239,"name":240,"slug":241,"tag_type":186},22,"ACID Compliant","acid-compliant",{"learning_curve":243,"flexibility":244,"performance":245,"portability":246,"popularity":247},"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.",[249],{"tool_id":239,"name":250,"slug":250,"tooltip_description":251,"logo_url":252,"logo_bg":70,"pricing_model":253,"learning_curve_score":35,"popularity_score":38,"hosting_assignment_type":257,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":258,"subcategory":32,"categories":262,"subcategories":264,"flexibility_score":76,"performance_score":38,"portability_score":38,"is_featured":3,"tags":265,"score_reasonings":284,"published_date":104,"last_updated_date":105},"n8n","Fair-code workflow automation tool with a visual node-based editor, 500+ integrations, and code nodes. Self-hostable alternative to Zapier and Make, also offered as n8n Cloud.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fn8n.svg",{"slug":254,"display_name":255,"description":256},"freemium","Freemium","A free tier is available; additional features, usage limits, or managed hosting require a paid plan.","self_hostable",{"category_id":259,"name":260,"slug":261},10,"Automation & Integration","automation-integration",[263],{"category_id":259,"name":260,"slug":261,"is_primary":3,"display_order":88},[],[266,267,268,272,276,280],{"tag_id":184,"name":171,"slug":185,"tag_type":186},{"tag_id":235,"name":236,"slug":237,"tag_type":186},{"tag_id":269,"name":270,"slug":271,"tag_type":186},13,"Free Tier","free-tier",{"tag_id":273,"name":274,"slug":275,"tag_type":186},17,"No-code","no-code",{"tag_id":277,"name":278,"slug":279,"tag_type":191},33,"Workflow Automation","workflow-automation",{"tag_id":281,"name":282,"slug":283,"tag_type":200},44,"Event-driven","event-driven",{"learning_curve":285,"performance":286,"flexibility":287,"portability":288,"popularity":289},"Visual workflow canvas is intuitive; advanced node expressions and APIs take more time.","Workflow execution is efficient; throughput scales with self-hosted resources.","Custom nodes via code editor, self-hostable, and API-accessible for any workflow imaginable.","Open source; JSON workflow export and self-hosting make migration feasible.","Very popular among technical users for self-hosted automation; large open-source community.",[291],{"tool_id":76,"name":292,"slug":293,"tooltip_description":294,"logo_url":295,"logo_bg":70,"pricing_model":296,"learning_curve_score":75,"popularity_score":35,"hosting_assignment_type":257,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":297,"subcategory":32,"categories":301,"subcategories":303,"flexibility_score":35,"performance_score":75,"portability_score":35,"is_featured":91,"tags":304,"score_reasonings":321,"published_date":104,"last_updated_date":105},"Streamlit","streamlit","Open-source Python framework for turning data scripts into interactive web apps in a few lines, with no frontend code. Part of Snowflake since 2022.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fstreamlit.svg",{"slug":254,"display_name":255,"description":256},{"category_id":298,"name":299,"slug":300},15,"Data Apps","data-apps",[302],{"category_id":298,"name":299,"slug":300,"is_primary":3,"display_order":88},[],[305,306,307,308,312,316,320],{"tag_id":181,"name":165,"slug":166,"tag_type":182},{"tag_id":184,"name":171,"slug":185,"tag_type":186},{"tag_id":269,"name":270,"slug":271,"tag_type":186},{"tag_id":309,"name":310,"slug":311,"tag_type":191},26,"Data Visualization","data-visualization",{"tag_id":313,"name":314,"slug":315,"tag_type":191},28,"Web Development","web-development",{"tag_id":317,"name":318,"slug":319,"tag_type":191},38,"Dashboards","dashboards",{"tag_id":193,"name":194,"slug":195,"tag_type":191},{"learning_curve":322,"flexibility":323,"performance":324,"popularity":325,"portability":326},"Minimal Python API; a working web app runs in under 20 lines of code.","Simple script model is easy but constrains layout, state management, and navigation.","Reruns the entire script on each interaction; not suited for high-frequency or real-time workloads.","Popular within the data science community; less known outside ML and analytics circles.","Script model is unique; switching to Gradio or Dash requires a full rewrite.",[328],{"tool_id":65,"name":66,"slug":67,"tooltip_description":68,"logo_url":69,"logo_bg":70,"pricing_model":329,"learning_curve_score":75,"popularity_score":76,"hosting_assignment_type":32,"hosting_provider_restriction":77,"hosting_target_restriction":77,"hosting_compatible_tool_ids":32,"parent_tool_id":32,"category":330,"subcategory":331,"categories":332,"subcategories":334,"flexibility_score":38,"performance_score":76,"portability_score":35,"is_featured":91,"tags":336,"score_reasonings":338,"published_date":104,"last_updated_date":105},{"slug":72,"display_name":73,"description":74},{"category_id":79,"name":80,"slug":81},{"subcategory_id":83,"name":84,"slug":85},[333],{"category_id":79,"name":80,"slug":81,"is_primary":3,"display_order":88},[335],{"subcategory_id":83,"name":84,"slug":85,"category_id":79,"is_primary":3,"display_order":88},[337],{"tag_id":94,"name":95,"slug":96,"tag_type":97},{"learning_curve":99,"flexibility":100,"performance":101,"popularity":102,"portability":103},[340,359,368,392],{"stack_id":341,"slug":342,"name":343,"tagline":344,"experience_level":345,"project_type":346,"stack_type_slug":347,"stack_type_icon_url":348,"score_popularity":35,"score_learning_curve":35,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":349},187,"n8n-streamlit-pipeline","n8n + Streamlit Pipeline Dashboard","n8n collects and processes data into PostgreSQL, Streamlit visualizes the pipeline output.","beginner","dashboard","project","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Fproject.svg",[350,351,352,353,358],{"tool_id":38,"slug":166,"name":165,"logo_url":168,"logo_bg":70},{"tool_id":94,"slug":214,"name":213,"logo_url":216,"logo_bg":70},{"tool_id":239,"slug":250,"name":250,"logo_url":252,"logo_bg":70},{"tool_id":354,"slug":355,"name":356,"logo_url":357,"logo_bg":112},8,"pandas","Pandas","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fpandas.svg",{"tool_id":76,"slug":293,"name":292,"logo_url":295,"logo_bg":70},{"stack_id":38,"slug":360,"name":361,"tagline":362,"experience_level":345,"project_type":346,"stack_type_slug":347,"stack_type_icon_url":348,"score_popularity":76,"score_learning_curve":75,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":363},"python-dashboard-starter","Python Dashboard Starter","Interactive Streamlit dashboard with pandas analytics and a PostgreSQL backend.",[364,365,366,367],{"tool_id":38,"slug":166,"name":165,"logo_url":168,"logo_bg":70},{"tool_id":94,"slug":214,"name":213,"logo_url":216,"logo_bg":70},{"tool_id":354,"slug":355,"name":356,"logo_url":357,"logo_bg":112},{"tool_id":76,"slug":293,"name":292,"logo_url":295,"logo_bg":70},{"stack_id":281,"slug":369,"name":370,"tagline":371,"experience_level":372,"project_type":373,"stack_type_slug":347,"stack_type_icon_url":348,"score_popularity":75,"score_learning_curve":35,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":374},"svelte-fastapi","Svelte + FastAPI","Svelte SPA frontend with FastAPI backend: minimal JavaScript output meets Python API performance.","intermediate","web_app",[375,380,385,386,391],{"tool_id":376,"slug":377,"name":378,"logo_url":379,"logo_bg":70},20,"svelte","Svelte","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fsvelte.svg",{"tool_id":381,"slug":382,"name":383,"logo_url":384,"logo_bg":70},14,"fastapi","FastAPI","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ffastapi.svg",{"tool_id":38,"slug":166,"name":165,"logo_url":168,"logo_bg":70},{"tool_id":387,"slug":388,"name":389,"logo_url":390,"logo_bg":70},103,"typescript","TypeScript","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Ftypescript.svg",{"tool_id":94,"slug":214,"name":213,"logo_url":216,"logo_bg":70},{"stack_id":393,"slug":394,"name":395,"tagline":396,"experience_level":345,"project_type":397,"stack_type_slug":398,"stack_type_icon_url":399,"score_popularity":38,"score_learning_curve":75,"catalog_display_order":32,"published_date":32,"last_updated_date":32,"core_tool_previews":400},194,"n8n-quick-deploy","n8n Quick Deploy","Deploy n8n in one click from a VPS marketplace template or a container-platform template, without assembling Docker, a proxy, or TLS yourself.","automation","infrastructure","https:\u002F\u002Fassets.tekyous.dev\u002Ficons\u002Fstack-types\u002Finfrastructure.svg",[401,402,403],{"tool_id":94,"slug":214,"name":213,"logo_url":216,"logo_bg":70},{"tool_id":239,"slug":250,"name":250,"logo_url":252,"logo_bg":70},{"tool_id":404,"slug":405,"name":406,"logo_url":407,"logo_bg":70},72,"docker","Docker","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fdocker.svg",[409,412,415,418,421],{"question":410,"answer":411},"Should I use Claude or OpenAI for the LLM step?","Claude is the default for this stack because multi-step agent logic benefits from strong instruction-following and long context: an n8n workflow that fetches records, builds a prompt from them, and asks for structured output tends to stay on track end to end. OpenAI is the pick when the workflow leans on its ecosystem: it is the most widely integrated LLM across n8n's community nodes and AI tooling, and it has a wide ladder of model tiers from cost-efficient to flagship reasoning. Both bill per token, so the cost profile is similar. The swap itself is one model node in n8n; nothing in Streamlit or PostgreSQL changes either way.",{"question":413,"answer":414},"What does running this stack cost?","Almost everything is free open source: n8n self-hosted, Streamlit, Python, and PostgreSQL cost nothing beyond the server they run on, and a $5 to $10 a month VPS runs the whole stack for a small team. The variable line item is the LLM: Claude and OpenAI bill per token, and an internal tool doing a few thousand interactions a month on mid-tier models typically lands in the tens of dollars. n8n's cloud plans ($20 Starter, $50 Pro, billed yearly) trade the self-hosting work for hosted execution, and Streamlit's Community Cloud hosts small apps free. See the Pricing section below for the breakdown.",{"question":416,"answer":417},"How does the Streamlit-to-n8n connection actually work?","Streamlit calls an n8n webhook URL with the user's input and waits for the workflow to finish, receiving the final output as JSON. Two behaviors bite teams the first week. First, a multi-step workflow with LLM calls can run longer than the default webhook or HTTP timeout, so long tasks need either a larger client timeout or the async pattern: n8n returns a execution-id immediately and Streamlit polls for the result. Second, Streamlit reruns its whole script on every widget interaction, so an unprotected webhook call fires on every click; wrap it with Streamlit's cache_data decorator keyed on the input, or move the call behind an explicit submit button. PostgreSQL sidesteps both issues for anything the workflow has already computed.",{"question":419,"answer":420},"What breaks first as usage grows?","The LLM token bill, then Streamlit. Token spend grows linearly with usage and is the first number to move: caching repeated prompts and stepping mid-tier models down for simple steps is the main lever. Streamlit's rerun-per-interaction model is built for small internal audiences, not many concurrent users; one shared instance serves a team fine but degrades as a product-grade frontend. n8n is the sturdier layer: self-hosted execution is unlimited, and cloud plans scale by execution count (2,500 a month on Starter, 10,000 on Pro). PostgreSQL at this stack's data volumes is rarely the constraint. The honest ceiling is a few dozen daily users; past that, graduate the frontend to a real web framework and keep the n8n workflows as the API layer.",{"question":422,"answer":423},"How is this different from the n8n + Streamlit Pipeline Dashboard stack?","Same architecture, different job. The pipeline stack is data in, charts out: n8n ingests and transforms on a schedule, PostgreSQL stores the history, and Streamlit renders it; nobody interacts with the workflow at run time. This stack is interactive: a user's input at a Streamlit widget triggers an n8n workflow through a webhook, an LLM processes it, and the response comes back to the same screen. That difference changes the cost profile (per-token LLM spend exists only here) and the failure modes (this stack worries about webhook latency; the pipeline stack worries about ingestion schedules). If no LLM belongs in the loop, the pipeline stack is the simpler and cheaper choice.",{"summary":425,"starting_cost_label":426,"has_free_tier":3,"line_items":427},"Almost everything here is free open source: n8n self-hosted, Streamlit, Python, and PostgreSQL cost nothing beyond the server they run on, and a $5 to $10 a month VPS runs the whole stack for a small team. The variable line item is the LLM: Claude and OpenAI bill per token, and an internal tool doing a few thousand interactions a month on mid-tier models typically lands in the tens of dollars. n8n's cloud plans ($20 Starter, $50 Pro, billed yearly) trade self-hosting for hosted execution, and Streamlit's Community Cloud hosts small apps free.","Free to start",[428,432,436],{"label":429,"cost":430,"note":431},"Orchestration (n8n)","Free–$50\u002Fmo","Self-hosted free with unlimited workflows; cloud plans from $20 (Starter, 2.5K executions) to $50 (Pro, 10K) a month, billed yearly.",{"label":433,"cost":434,"note":435},"UI + data layer (Streamlit, PostgreSQL)","Free (open source)","Streamlit, Python, and PostgreSQL are free; a $5 to $10 a month VPS runs the stack, and Streamlit's Community Cloud hosts small apps free.",{"label":437,"cost":438,"note":439},"LLM (Claude or OpenAI)","Usage-based (per token)","Both providers bill per token across model tiers; an internal tool's typical monthly volume on mid-tier models costs dollars, not hundreds.",{"title":441,"description":442,"og_image":32,"canonical":443},"n8n + Streamlit AI Agent: Tools, Pricing & How to Deploy | Tekyous","n8n orchestrates AI workflows surfaced as an interactive Streamlit dashboard. Compare n8n + Streamlit AI Agent tools, pricing & how to deploy on Tekyous.","https:\u002F\u002Ftekyous.dev\u002Fstacks\u002Fn8n-streamlit-ai",1790518887817]