[{"data":1,"prerenderedAt":216},["ShallowReactive",2],{"categories-init":3,"tool-details-pinecone":4,"tool-pricing-pinecone":12,"tool-rel-pinecone":42,"tool-pinecone":141,"tool-res-pinecone":214,"tool-stacks-pinecone":215},true,{"tool_id":5,"db_model":6,"query_language":7,"hosting_model":8,"acid_compliant":9,"replication_support":3,"updated_at":10,"created_at":10,"github_stars":11,"github_stars_checked_at":11},215,"vector","custom","cloud_managed",false,"2026-08-17T10:46:29.614360",null,[13,21,28,35],{"tier_name":14,"price":15,"billing_period":16,"features":17},"Starter",0,"monthly",[18,19,20],"2GB storage, up to 5 indexes with 100 namespaces each","On-demand database, Inference, and Assistant included","2M writes\u002Fmonth, 1M reads\u002Fmonth, community Discord support",{"tier_name":22,"price":23,"billing_period":16,"features":24},"Builder",20,[25,26,27],"Flat monthly price, 10 indexes per project, 1,000 namespaces per index","10GB storage, 5M writes\u002Fmonth, 2M reads\u002Fmonth","Up to 5 projects and 5 users, Prometheus\u002FDatadog monitoring",{"tier_name":29,"price":30,"billing_period":16,"features":31},"Standard",50,[32,33,34],"$50\u002Fmonth minimum with pay-as-you-go usage, 20 indexes per project","All cloud regions, Dedicated Read Nodes, backup and restore, RBAC, SSO","Storage at $0.33\u002FGB\u002Fmonth; HIPAA available as an add-on",{"tier_name":36,"price":37,"billing_period":16,"features":38},"Enterprise",500,[39,40,41],"$500\u002Fmonth minimum, 200 indexes per project","99.95% uptime SLA, Bring Your Own Cloud, private endpoints","Customer-managed encryption, audit logs, HIPAA, pro support",[43,76,102,126],{"relationship_type":44,"relationship_display_name":45,"relationship_description":46,"relationship_display_order":47,"tool":48,"strength":60,"notes":75},"works_with","Works well with","Tools commonly used together in the same stack.",1,{"tool_id":49,"name":50,"slug":51,"tooltip_description":52,"logo_url":53,"logo_bg":54,"pricing_model":55,"learning_curve_score":59,"popularity_score":60,"hosting_assignment_type":61,"hosting_provider_restriction":62,"hosting_target_restriction":62,"hosting_compatible_tool_ids":11,"parent_tool_id":11,"category":63,"subcategory":66,"categories":70,"subcategories":71,"flexibility_score":72,"performance_score":60,"portability_score":72,"is_featured":9,"tags":73,"score_reasonings":74,"published_date":11,"last_updated_date":11},214,"LangChain","langchain","The dominant open-source framework for building LLM-powered agents and applications — 100M+ monthly downloads, with LangGraph for stateful multi-agent orchestration and LangSmith for observability, tracing, and evaluation.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Flangchain.svg","dark",{"slug":56,"display_name":57,"description":58},"freemium","Freemium","A free tier is available; additional features, usage limits, or managed hosting require a paid plan.",3,4,"library","open",{"category_id":23,"name":64,"slug":65},"Agentic AI","agentic-ai",{"subcategory_id":67,"name":68,"slug":69},62,"Agent Frameworks","agent-frameworks",[],[],5,[],{},"Pinecone is one of the most commonly paired vector stores in LangChain tutorials and integrations, used as the retrieval backend for RAG applications built with LangChain.",{"relationship_type":44,"relationship_display_name":45,"relationship_description":46,"relationship_display_order":47,"tool":77,"strength":59,"notes":101},{"tool_id":78,"name":79,"slug":80,"tooltip_description":81,"logo_url":82,"logo_bg":83,"pricing_model":84,"learning_curve_score":60,"popularity_score":60,"hosting_assignment_type":88,"hosting_provider_restriction":62,"hosting_target_restriction":62,"hosting_compatible_tool_ids":11,"parent_tool_id":11,"category":89,"subcategory":93,"categories":97,"subcategories":98,"flexibility_score":72,"performance_score":59,"portability_score":72,"is_featured":9,"tags":99,"score_reasonings":100,"published_date":11,"last_updated_date":11},222,"AnythingLLM","anythingllm","Self-hosted chat UI built around RAG from the ground up: MIT licensed, with per-workspace document sets and vector DB settings, plus a desktop app that skips Docker entirely.","https:\u002F\u002Fassets.tekyous.dev\u002Flogos\u002Ftools\u002Fanythingllm.svg","white",{"slug":85,"display_name":86,"description":87},"open_source","Open Source","Source code is publicly available and free to use, modify, and distribute. No paid plans from the project itself.","deployable",{"category_id":90,"name":91,"slug":92},11,"APIs & Infrastructure","apis-infrastructure",{"subcategory_id":94,"name":95,"slug":96},64,"AI Chat Interfaces","ai-chat-interfaces",[],[],[],{},"AnythingLLM documents Pinecone as a supported vector database backend for its per-workspace RAG configuration.",{"relationship_type":103,"relationship_display_name":104,"relationship_description":105,"relationship_display_order":106,"tool":107,"strength":59,"notes":125},"alternative_to","Alternative to","These tools serve a similar purpose — typically you would pick one, not both.",6,{"tool_id":108,"name":109,"slug":110,"tooltip_description":111,"logo_url":112,"logo_bg":54,"pricing_model":113,"learning_curve_score":59,"popularity_score":60,"hosting_assignment_type":114,"hosting_provider_restriction":62,"hosting_target_restriction":62,"hosting_compatible_tool_ids":11,"parent_tool_id":11,"category":115,"subcategory":118,"categories":121,"subcategories":122,"flexibility_score":72,"performance_score":60,"portability_score":72,"is_featured":9,"tags":123,"score_reasonings":124,"published_date":11,"last_updated_date":11},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":56,"display_name":57,"description":58},"self_hostable",{"category_id":60,"name":116,"slug":117},"Databases","databases",{"subcategory_id":60,"name":119,"slug":120},"Vector Databases","vector-databases",[],[],[],{},"Both are vector databases used for RAG and semantic search. Pinecone is closed-source, fully managed SaaS only, while Qdrant offers a genuine self-hosted, open-source (Apache 2.0) option alongside its managed cloud — a common deciding factor between the two.",{"relationship_type":103,"relationship_display_name":104,"relationship_description":105,"relationship_display_order":106,"tool":127,"strength":59,"notes":140},{"tool_id":128,"name":129,"slug":130,"tooltip_description":131,"logo_url":132,"logo_bg":54,"pricing_model":133,"learning_curve_score":72,"popularity_score":60,"hosting_assignment_type":114,"hosting_provider_restriction":62,"hosting_target_restriction":62,"hosting_compatible_tool_ids":11,"parent_tool_id":11,"category":134,"subcategory":135,"categories":136,"subcategories":137,"flexibility_score":60,"performance_score":59,"portability_score":72,"is_featured":9,"tags":138,"score_reasonings":139,"published_date":11,"last_updated_date":11},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":56,"display_name":57,"description":58},{"category_id":60,"name":116,"slug":117},{"subcategory_id":60,"name":119,"slug":120},[],[],[],{},"Both are vector databases used for RAG and semantic search. Pinecone is closed-source, fully managed SaaS only, optimized for production scale, while Chroma is open-source and self-hostable with a minimal API optimized for fast prototyping.",{"tool_id":5,"name":142,"slug":143,"tooltip_description":144,"logo_url":145,"logo_bg":54,"pricing_model":146,"learning_curve_score":147,"popularity_score":60,"hosting_assignment_type":148,"hosting_provider_restriction":62,"hosting_target_restriction":62,"hosting_compatible_tool_ids":11,"parent_tool_id":11,"category":149,"subcategory":150,"categories":151,"subcategories":153,"flexibility_score":59,"performance_score":60,"portability_score":147,"is_featured":9,"tags":155,"score_reasonings":171,"published_date":177,"last_updated_date":11,"vendor":178,"website_url":180,"documentation_url":181,"github_url":11,"long_description":182,"tagline":183,"key_features":184,"pros":192,"cons":198,"social_links":203,"screenshots_urls":204,"pricing_tiers":205,"license_type":11,"community_size":11,"active_maintenance":3,"parent_tool":11},"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":56,"display_name":57,"description":58},2,"managed_only",{"category_id":60,"name":116,"slug":117},{"subcategory_id":60,"name":119,"slug":120},[152],{"category_id":60,"name":116,"slug":117,"is_primary":3,"display_order":15},[154],{"subcategory_id":60,"name":119,"slug":120,"category_id":60,"is_primary":3,"display_order":15},[156,161,166],{"tag_id":157,"name":158,"slug":159,"tag_type":160},13,"Free Tier","free-tier","feature",{"tag_id":162,"name":163,"slug":164,"tag_type":165},25,"Machine Learning","machine-learning","use_case",{"tag_id":167,"name":168,"slug":169,"tag_type":170},40,"Web","web","platform",{"learning_curve":172,"flexibility":173,"performance":174,"portability":175,"popularity":176},"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.","2026-09-27",{"vendor_id":179,"name":142,"slug":143,"website_url":180,"logo_url":145,"logo_bg":54},172,"https:\u002F\u002Fwww.pinecone.io","https:\u002F\u002Fdocs.pinecone.io","Pinecone is a fully managed, **serverless vector database** for storing and querying high-dimensional embeddings at scale. Serverless is the default for every new index: instead of provisioning and paying for idle compute pods, billing is based on read units, write units, and storage actually consumed, letting a project store billions of vectors without any server management. It supports dense, sparse, and full-text indexes side by side, with namespaces for multi-tenant isolation.\n\nBeyond raw vector storage, Pinecone bundles **Pinecone Inference**, hosted embedding and reranking models built into the query pipeline so a separate embedding provider is not required, and Pinecone Assistant, a higher-level toolkit for building chat and RAG (retrieval-augmented generation) applications on top of an index. Dedicated Read Nodes serve read-heavy production workloads, and a Bring Your Own Cloud option runs the data plane inside a customer's own cloud account for data-residency requirements.\n\nPricing has **four tiers**. The free Starter tier is usable for real prototypes, with a few gigabytes of storage, several indexes, and Inference and Assistant allowances included. Builder is a flat monthly plan for solo developers and small teams, Standard adds pay-as-you-go usage above a monthly minimum with all cloud regions, SSO, and RBAC, and Enterprise adds an uptime SLA, private endpoints, customer-managed keys, and audit logs. Pinecone is closed source with no self-hosted deployment, unlike open-source vector database alternatives.","The vector database for machine learning.",[185,186,187,188,189,190,191],"Serverless indexes billed on read units, write units, and storage","Dense, sparse, and full-text indexes side by side","Pinecone Inference: hosted embedding and reranking models in the query pipeline","Pinecone Assistant: toolkit for chat and RAG applications","Dedicated Read Nodes for read-heavy production workloads","Bring Your Own Cloud deployment on Enterprise","Available across AWS, Azure, and GCP regions",[193,194,195,196,197],"Usable free tier with Inference and Assistant included, not a short trial","Serverless architecture removes idle-compute cost and capacity planning","Bundled Inference and Assistant reduce the number of services a RAG pipeline needs","Broad integration coverage across LangChain, LlamaIndex, and other AI frameworks","Flat-priced Builder tier gives small teams a predictable bill",[199,200,201,202],"Closed source with no self-hosted deployment option","Usage-based pricing on Standard and above is harder to predict than flat-rate plans","SLA, private endpoints, and audit logs require the Enterprise minimum spend","Single-purpose vector store, so application data still needs a separate primary database",{},[],[206,208,210,212],{"tier_name":14,"price":15,"billing_period":16,"features":207},[18,19,20],{"tier_name":22,"price":23,"billing_period":16,"features":209},[25,26,27],{"tier_name":29,"price":30,"billing_period":16,"features":211},[32,33,34],{"tier_name":36,"price":37,"billing_period":16,"features":213},[39,40,41],[],[],1790518477054]