Managed vector database for production semantic search, RAG, and hybrid retrieval
Pinecone documents a fully managed vector database at docs.pinecone.io for storing, indexing, and querying high-dimensional embeddings at production scale. Serverless indexes support document schemas mixing dense vectors, sparse vectors, and full-text search fields with metadata filtering per docs.pinecone.io/guides/get-started/concepts. Official SDKs include Python, Node.js, Java, and Go; REST API access uses documented rate limits and plan tiers (Starter, Standard, Enterprise). Pinecone also documents Pinecone Assistant, Dedicated Read Nodes, BYOC, and Nexus offerings on pinecone.io alongside MCP integrations (Pinecone MCP Server and Pinecone Docs MCP Server) for agent workflows.
Use cases
- Production RAG with namespaces and metadata filters
- Recommendation engines over billions of embeddings
- Hybrid lexical + semantic search in a single Pinecone index
- Agent retrieval paired with pinecone-mcp for index inspection
- Enterprise deployments requiring BYOC or dedicated read nodes
Key features
- Serverless indexes with dense, sparse, and FTS ranking fields in one schema
- Hybrid and multi-method search via score_by per query
- Managed scaling with documented API rate limits and 429 self-throttle headers
- Python/Node/Java/Go SDKs and REST API
- Pinecone Assistant managed knowledge layer and BYOC deployment options
Who Is It For?
- Teams wanting managed vector search without self-hosting infrastructure
- ML engineers building RAG and recommendation pipelines
- Developers evaluating hybrid dense-sparse-FTS in one database
Frequently Asked Questions
- Can Pinecone be self-hosted?
- Pinecone is a managed cloud service; BYOC runs in your AWS/GCP/Azure account per pinecone.io product pages.
- How do agents connect?
- Pinecone documents MCP servers at docs.pinecone.io/guides/operations/mcp-server; see also pinecone-mcp in this catalog.
- What about hybrid search?
- Document-schema indexes can mix dense_vector, sparse_vector, and full_text_search string fields—see docs.pinecone.io concepts.
Related
Related
3 Indexed items
Milvus
Milvus documents a high-performance vector database at milvus.io/docs for storing, indexing, and searching embedding vectors with metadata filtering and hybrid search. Deployment options include Milvus Lite (`pip install pymilvus` for notebooks/edge), Milvus Standalone (single Docker image), and Milvus Distributed on Kubernetes per milvus.io/docs/v2.6.x/install-overview. Official SDKs include PyMilvus, Go, Java, Node.js, and C#; Zilliz Cloud offers managed Milvus. Architecture separates access, coordinator, worker, and storage layers with object storage backends (MinIO, S3, Azure Blob) per milvus.io/docs/architecture_overview.
Weaviate
Weaviate documents an open-source vector database at docs.weaviate.io/weaviate for storing objects and vector embeddings with semantic, keyword, and hybrid search, RAG, reranking, and agent workflows. The ecosystem includes self-hosted Docker/Kubernetes installs, Weaviate Cloud (console.weaviate.cloud), Query Agent, and Weaviate Embeddings for managed inference. Client libraries include Python (`weaviate-client` v4, requires Weaviate 1.23.7+), TypeScript, Go, and Java with REST, gRPC, and GraphQL APIs per the official documentation.
Qdrant
Qdrant documents an AI-native vector search engine at qdrant.tech/documentation for storing, indexing, and querying high-dimensional vectors with optional payloads, supporting dense, sparse, and multi-vector configurations. Official guides cover Docker/Kubernetes self-hosting, Qdrant Cloud on AWS/GCP/Azure, Hybrid Cloud, Private Cloud, and Qdrant Edge for embedded retrieval. Client libraries include Python (`qdrant-client`), JavaScript/TypeScript (`@qdrant/js-client-rest`), Rust, Go, Java, and .NET with REST and gRPC APIs per the API reference at api.qdrant.tech.