Vespa

Embedded vector database for multimodal AI with petabyte scale

LanceDB is an open-source embedded vector database built on the Lance columnar format for multimodal AI. It delivers near in-memory performance from disk with zero-copy architecture, supporting vector search, full-text search, and SQL. Native SDKs for Python, TypeScript, and Rust integrate with LangChain, LlamaIndex, and DuckDB. Backed by a $30M Series A, used by Harvey AI and Runway, with 18,000+ GitHub stars.

Serverless vector and full-text search on object storage

turbopuffer is a serverless vector and full-text search engine built on object storage and vendor-positioned as roughly 10x cheaper than traditional vector databases. Used by Anthropic, Cursor, Notion, and Atlassian for production search workloads. Official site reports 4T+ documents, 10M+ writes/s, and 25k+ queries/s in production systems. Funded by Thrive Capital.

Fully managed RAG-as-a-Service platform for enterprise AI applications

Ragie is a managed retrieval-augmented generation platform that handles document ingestion, indexing, and retrieval so developers can build grounded AI applications without managing vector databases or chunking pipelines. It connects to Google Drive, Notion, Slack, Confluence, and other enterprise data sources with simple APIs for hybrid search and entity extraction.

One vector-search extension across PostgreSQL, DuckDB and SQLite

MIT-licensed vector-search extension for PostgreSQL, DuckDB and SQLite that shares one graph-index core with PQ/RaBitQ quantization, persistent indexes and metadata filtering; SQLite packages cover Linux, macOS, iOS, Android and WASM, so it runs inside existing databases instead of as a separate vector service.

Edge-native vector database for Workers and AI applications

Cloudflare Vectorize is Cloudflare’s managed vector database for Workers and edge AI applications. It is distinct from the existing Cloudflare Workers tool page: Workers is the compute runtime, while Vectorize is the embedding index and vector-query layer used to add semantic retrieval to Cloudflare-hosted apps.

Serverless vector database with pay-as-you-go API pricing

Upstash Vector is a managed serverless vector database for RAG, semantic search, and embedding lookup. It is separate from the existing Upstash platform record in the aicoolies catalog: this slug covers the Vector product line, not the broader Redis, Kafka, or QStash platform.

Open-source search engine with vector and hybrid retrieval

OpenSearch is an Apache-2.0 distributed search engine with native vector-search support for teams that want BM25, filters, aggregations, and k-NN retrieval in the same search stack. It is distinct from Elasticsearch in the aicoolies catalog: OpenSearch is the AWS-backed open fork with its own docs, plugin path, and serverless deployment options.

MCP server for connecting AI assistants to Supabase projects

Supabase MCP is Supabase's Apache-2.0 server for connecting AI assistants to Supabase projects. It can expose database, configuration, and project-management workflows to MCP clients such as Cursor, Claude, and Windsurf, while the official docs emphasize permission and security review before production use, SQL changes, or high-privilege database access.

AI data runtime for multimodal datasets and vector search

Deep Lake is an open-source AI data runtime from Activeloop for storing, versioning, and querying multimodal data and embeddings. It fits teams building RAG, training, evaluation, or dataset-heavy agent workflows that need a bridge between vector search, structured metadata, and large image, text, audio, or video collections.