Lantern

Lantern is a high-performance vector database and toolkit designed for developers to build AI applications efficiently. It offers significant advantages in speed, cost, and ease of use compared to other vector databases, enabling users to create, manage, and search vector embeddings for various AI-driven applications.

Key features:
- High-Speed Index Creation: Index creation is 30 times faster than pgvector, making it highly efficient for large-scale applications.
- Cost Efficiency: Up to 94% cheaper than competitors, offering substantial savings on cloud costs.
- Scalability: Capable of generating up to 2 million embeddings per hour, designed to handle demanding applications at scale.
- Ease of Use: Perform vector generation and search using SQL or popular ORM tools like Sequelize, Knex, and Django.
- One-Click Operations: Allows for one-click vector generation and index creation, simplifying the process of integrating vector search into applications.
- Support for Multiple Embedding Models: Supports over 20 embedding models, including those from OpenAI, Cohere, and Jina AI, as well as other open-source models.
- High Throughput and Low Latency: Offers best-in-class performance metrics, ensuring quick and efficient data processing.

How it works:
1. Create a table with vector columns using SQL commands.
2. Insert vectors into the table.
3. Create an index for faster queries.
4. Query the nearest vector using SQL commands.
5. Optionally, query the nearest vector to a text embedding.

Integrations:
SQL, JavaScript (Sequelize, Knex), Python (Django)

Use of AI:
Lantern leverages generative AI by supporting a wide range of embedding models. These models enable the generation of vector embeddings from unstructured data, which can then be used for efficient vector search and other AI-driven applications.

AI foundation model:
Lantern supports embedding models from providers such as OpenAI, Cohere, and Jina AI, as well as other open-source models. These models form the foundation for generating vector embeddings used in Lantern's vector search capabilities.

Target users:
- Developers building high-performance AI applications
- Organizations requiring efficient vector search capabilities
- Startups and large enterprises seeking cost-effective AI solutions

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.