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What is Lantern?

Lantern is an open-source Postgres vector database and toolkit designed for developers to build production-ready AI applications. Created by Lantern Systems, it integrates advanced vector search and BM25 text search capabilities directly within Postgres, eliminating the need for separate vector databases or search engines. Lantern supports scalable, serverless indexing that can handle millions of vectors with low latency, making it suitable for large-scale AI-powered search and retrieval tasks. It also simplifies AI workflows by enabling embedding generation and running large language models (LLMs) directly inside the database using simple SQL commands. Lantern fits developers and teams who want to leverage AI search and embedding capabilities seamlessly within their existing Postgres infrastructure and workflows, supporting a wide range of applications from code search to content recommendation.

AI Tool Review Summary

Performance Score

4.7/5

Content/Output Quality

Accurate, scalable, and reliable

Interface

Developer-friendly with SQL and ORM integration

AI Technology
LLMVector SearchNLP
Purpose of Tool

To provide scalable, integrated vector and text search capabilities within Postgres for AI applications.

Compatibility

Works natively with Postgres and integrates with popular ORMs like Sequelize, Knex, and Django.

Pricing

Free tier available with paid production plans starting at $44/month

Features

Features with the highest value for users are highlighted here.

Postgres vector search integration

BM25 text search support

Hybrid vector and text search

Serverless vector indexing

Embedding generation in database

LLM execution within Postgres

Support for 20+ embedding and LLM models

ORM compatibility

Who Is It For?

AI Developers

Data Engineers

Startups

Enterprise Teams

Backend Developers

Machine Learning Engineers

Product Managers

Open Source Enthusiasts

Content Platforms

Database Administrators

Pricing

Free

$0/free
  • Vector search and indexing
  • Vector and LLM generation
  • Automatic upgrades
  • Query insights
  • Community support
Popular

Production

$44/monthly
  • Everything in Free
  • Increased compute and storage
  • Automatic backups
  • Point-in-time recovery
  • Asynchronous tasks
  • Priority support

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Pros & Cons

Pros

  • Seamless integration with Postgres using SQL.
  • Scales efficiently to millions of vectors with low latency.

Cons

  • Some features are still in beta and may evolve.
  • Requires familiarity with Postgres and vector search concepts.

FAQs

Just Launched

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