Vector-database infrastructure
15 systems in the vector-database infrastructure category of the AI Agent Infrastructure Landscape, grouped by maturity tier.
Tier 1 — battle-tested (13)
- Chroma Embedded vector DB (Rust core)
Limited native hybrid (users build RRF custom). Fast Rust core (v2.5).
- Databricks Vector Search Storage-optimised + Unity Catalog governance
Storage-optimised index scaling to billions of vectors. Hosted MCP servers for UC functions, Genie, Vector Search. Agent framework integrates retrieval with Unity Catalog governance.
- Elasticsearch / OpenSearch Mature hybrid search
Mature hybrid (BM25 + vector) implementations. RRF + weighted combination. Elastic ELSER sparse model.
- LanceDB Embedded Arrow-columnar vector DB
Embedded vector DB (Arrow columnar). RRF reranker. Petabyte-scale on disk.
- Milvus Multi-vector + hybrid + GPU
Multi-vector columns (10 simultaneous). Native hybrid search (v2.5). CAGRA + Vamana GPU/CPU (v2.6).
- MongoDB Atlas Vector Search Vector embedded in document DB
Agent memory store for both short-term (document) and long-term (vector). LangGraph checkpointer for stateful agents. Vector search extended to Community Edition (Sept 2025).
- pgvector PostgreSQL vector extension
Stores embeddings alongside relational + full-text data. HNSW + IVFFlat ANN indexes. Used as agent conversation memory via LangChain + MCP. Foundation of Supabase AI and many self-hosted RAG stacks.
- Pinecone Managed vector DB + cascading retrieval
Managed vector DB. Cascading sparse + dense + rerank pipeline; pinecone-rerank-v0 .
- Qdrant Vector DB + DBSF + RRF
Distribution-Based Score Fusion + RRF. Sparse vectors native; filtering via ANN graph modification.
- Snowflake Cortex Search Hybrid vector + keyword + semantic rerank
Hybrid vector + keyword + semantic reranking. Cortex Agents (GA Nov 2025) orchestrate over Cortex Search for unstructured data. No separate vector infra needed inside Snowflake.
- Turbopuffer Object-storage-first vector DB
Object-storage-first vector DB. Powers Notion and Cursor. SPFresh indexing; ~10× cost reduction vs. memory-resident peers.
- Vespa Unified vector + lexical + structured + ranking
Combines vector ANN, lexical, structured filtering, and ML-learned ranking in one distributed system. Integrated chunking + layered ranking for RAG. No separate embedding infra needed.
- Weaviate Hybrid vector DB + learned ranking
Hybrid Search 2.0 with learned ranking. BM25 + vector in unified index.
Tier 2 — production-ready (2)
- Activeloop Deep Lake Multimodal vector + serverless Postgres
Deep Memory feature optimises embedding space per use-case (+22% retrieval accuracy). Deep Lake PG unifies serverless Postgres (agent short-term state) + billion-scale vector search (long-term memory).
- Marqo Multimodal vector via single API
End-to-end multimodal (text + image) vector search via single API. Handles embedding generation internally. Ecommerce personalization with proprietary models.