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Try NowWhat 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
4.7/5
Accurate, scalable, and reliable
Developer-friendly with SQL and ORM integration
LLMVector SearchNLP
To provide scalable, integrated vector and text search capabilities within Postgres for AI applications.
Works natively with Postgres and integrates with popular ORMs like Sequelize, Knex, and Django.
Free tier available with paid production plans starting at $44/month
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
How It Works
1
Add Vector Columns
Create tables with vector columns in Postgres using simple SQL commands.
2
Insert Vectors
Insert dense or sparse vectors into your database for search and retrieval.
3
Create Indexes
Build efficient vector indexes with serverless indexing to enable fast queries.
4
Query and Generate Embeddings
Run vector similarity searches and generate embeddings or LLM outputs directly via SQL.
AI Developers
Data Engineers
Startups
Enterprise Teams
Backend Developers
Machine Learning Engineers
Product Managers
Open Source Enthusiasts
Content Platforms
Database Administrators
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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Weekly intelligence on AI strategy, operations, and market shifts. No noise. No narrative. Direct to your inbox.
Pros & Cons
Seamless integration with Postgres using SQL. Scales efficiently to millions of vectors with low latency.
Some features are still in beta and may evolve. Requires familiarity with Postgres and vector search concepts.
FAQs
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Rating
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