Popular Databases AI tools

17 category leaders in Databases, selected from the full directory.

Pinecone

  • Managed vector database for production similarity search.
  • Hybrid dense-sparse retrieval with metadata filtering.
  • Serverless indexes plus Bring Your Own Cloud deployment.
  • Used by Microsoft, OpenAI, Workday, and Cisco.

Weaviate

  • Open-source vector database with GraphQL API.
  • Native hybrid vector plus BM25 retrieval.
  • Multi-modal embeddings and modular plugins.
  • Cloud and self-host for production RAG stacks.

Milvus

  • Open-source vector database from LF AI & Data.
  • 44k+ GitHub stars; billion-vector scale design.
  • Hybrid search, dynamic schema, and GPU indexing.
  • Zilliz Cloud offers fully managed Milvus hosting.

Turbopuffer

  • Serverless vector and full-text search on object storage.
  • Anthropic, Cursor, Notion, and Atlassian cited customers.
  • Thrive seed; hybrid search at AI-scale document volume.
  • Low-cost alternative to in-memory vector infrastructure.

Qdrant

  • Open-source vector database with Rust performance.
  • $50M Series B in 2026; cloud, hybrid, and edge deployments.
  • Filtered ANN search, quantization, and distributed clusters.
  • Common choice for production RAG and recommendation stacks.

Chroma

  • Developer-first open-source embedding database.
  • One-line Python start for RAG prototypes.
  • Chroma Cloud for managed vector collections.
  • Default choice for many LLM app tutorials.

LanceDB

  • Multimodal AI lakehouse on the Lance columnar format.
  • Object-storage native for cheap large corpora.
  • Multimodal retrieval without heavy cluster ops.
  • Strong pick for local-first and cold-path RAG.

Zilliz

  • Managed Milvus vector database (Zilliz Cloud).
  • Billion-scale similarity and hybrid search.
  • Commercial home of the Milvus open-source project.
  • Enterprise alternative to self-hosting Milvus.

FAISS

  • Meta similarity-search library for dense vectors.
  • GPU and CPU indexes at billion-vector scale.
  • Default ANN building block under many vector DBs.
  • Open source; powers production retrieval stacks worldwide.

Redis

  • In-memory vector database for AI search and RAG.
  • Hybrid vector and keyword retrieval at real-time latency.
  • Redis Query Engine powers production recommendation stacks.
  • Household cache and vector layer for AI applications.

S3 Vectors

  • Native vector storage and query inside Amazon S3.
  • Large-scale embeddings without a separate vector DB.
  • Cost-efficient path for RAG and semantic retrieval.
  • AWS default for object-store-native vector search.

Agent Platform Vector Search

  • Google Cloud managed billion-scale vector search.
  • Agent Retrieval adds collections and auto embeddings.
  • Enterprise RAG and recommendation deployments.
  • Default GCP vector layer for production AI apps.

Vespa

  • AI search and vector ranking at large scale.
  • Combines lexical, vector, and machine-learned rankers.
  • Powers high-traffic recommendation and search apps.
  • Open source with Vespa Cloud for production.

MongoDB Atlas Vector Search

  • Vector indexes inside MongoDB Atlas clusters.
  • Hybrid filters with operational document data.
  • Common RAG path for MongoDB-backed apps.
  • Household database brand with native vectors.

Elasticsearch

  • Vector and hybrid search on Elasticsearch.
  • Dense kNN plus BM25 in one widely used engine.
  • Elastic Cloud and self-managed deployments.
  • Default search stack for many AI retrieval apps.

Astra DB

  • Serverless Cassandra with native vector search.
  • Multi-region cloud for GenAI and RAG workloads.
  • IBM DataStax enterprise distribution and ops.
  • Major managed path for Cassandra vector apps.

Oracle AI Vector Search

  • Vector similarity search inside Oracle Database.
  • Embeddings next to enterprise relational data.
  • Built for Oracle-centric RAG and semantic search.
  • Hyperscale enterprise database with AI vectors.