VexDB-Lite

Vector similarity search for PostgreSQL

pgvector is an open-source PostgreSQL extension with 22K+ GitHub stars adding vector similarity search to your existing Postgres database. Store embeddings alongside relational data, perform exact and approximate nearest neighbor search using L2, inner product, cosine, and L1 metrics. Supports HNSW and IVFFlat indexes for fast similarity queries at scale. Eliminates the need for a separate vector database by bringing vector capabilities into existing PostgreSQL infrastructure.

Vector search extension for SQLite that runs anywhere

sqlite-vec is a lightweight vector search extension for SQLite written in pure C with zero dependencies. It brings nearest-neighbor search capabilities directly into SQLite databases, enabling AI applications to store and query embeddings without running a separate vector database. The extension works everywhere SQLite runs including Linux, macOS, Windows, WebAssembly in browsers, and even Raspberry Pi devices. Sponsored by Mozilla Builders, Fly.io, and Turso.

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.

Fast embeddable vector search engine

USearch is a high-performance vector search engine implementing HNSW algorithms for approximate nearest neighbor queries across C++, Python, JavaScript, Rust, Java, Go, and more. It supports user-defined distance metrics, memory-mapped persistence for datasets larger than RAM, and filtered search with predicates. Used by YugabyteDB and ScyllaDB as their production vector indexing backend.

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.

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.

AI-native state store with hybrid vector and full-text search

SeekDB is an open-source AI-native state store from the OceanBase ecosystem that combines MySQL-compatible data access with hybrid vector and full-text retrieval. It targets agent and AI application teams that need embedded or server deployment, copy-on-write style sandboxes, and searchable state without gluing together several separate storage layers.

DiskANN-powered vector search extension for PostgreSQL

pgvectorscale is an open-source PostgreSQL extension from Timescale that complements pgvector with DiskANN-based approximate vector search. It is useful for teams that want faster embedding retrieval while keeping vectors, filters, and application data inside the Postgres ecosystem instead of adopting a separate hosted vector database.