Lantern

Lantern: High-Performance Vector Database and Toolkit

Lantern is a vector database and toolkit designed to help developers build AI applications efficiently. It offers high performance, cost efficiency, and ease of use compared to other vector databases.

Key features:
- High-speed index creation, 30 times faster than pgvector
- Up to 94% cheaper than competitors like Pinecone
- Generates up to 2 million embeddings per hour
- Supports vector generation and search using SQL or ORMs like Sequelize, Knex, and Django
- One-click vector generation and index creation
- Supports over 20 embedding models, including OpenAI, Cohere, Jina AI, and open-source models
- High throughput and low latency for quick and efficient data processing

How it works:
Users interact with Lantern primarily through SQL commands or client libraries to:
- Create tables with vector columns
- Insert vectors
- Create indexes for faster queries
- Query the nearest vector or nearest vector to a text embedding

Integrations:
- SQL for direct database operations
- JavaScript libraries like Sequelize and Knex
- Python frameworks like Django

Use of AI:
Lantern supports a wide range of embedding models, enabling the generation of vector embeddings from unstructured data for efficient vector search and AI-driven applications.

AI foundation models:
Lantern supports models from OpenAI, Cohere, Jina AI, and other open-source models.

Target users:
Lantern is ideal for developers and organizations looking to build high-performance AI applications that require efficient vector search capabilities, suitable for both startups and large enterprises.

How to access:
Lantern is available as a web application and offers an API for developers. It provides a free tier with $250 in credits to get started without requiring a credit card.