Lantern – a PostgreSQL vector database for building AI applications
We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications. Install and use our extension here: https://github.com/lanterndata/lantern We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search. There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding…
In plain words
Lantern is a PostgreSQL vector database extension that enables developers to build AI applications directly within their database. It uses HNSW algorithm technology for vector search and offers a complete feature set for embedding generation and semantic search capabilities. The extension is designed to match or exceed the performance of competing PostgreSQL vector solutions across index creation time, query throughput, and query latency metrics.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications. Install and use our extension here: https://github.com/lanterndata/lantern We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search. There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics. ** Here’s what we support today ** - Creating an AI application end to end without leaving your database (example: https://github.com/ezra-varady/lanterndb-semantic-image-sear...) - Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model) - Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern - Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows. ** Here’s what’s coming soon ** - Cloud-hosted version of Lantern - Templates and guides for building applications for different industries - Tools for generating embeddings (support for third party model API's, more local models) - Support for version control and A/B test embeddings - Autotuned index type that will choose appropriate index creation parameters - 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support ** Why we started Lantern today ** There's dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it's super important to build on top of PostgreSQL - Developers know how to use PostgreSQL. - Companies already store their data on PostgreSQL. - Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data. We are open source and excited to have community contributors! Looking forward to hearing your feedback!
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