Lantern

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Lantern is a Postgres vector database that is scalable, cost-effective, and easy to use0
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What is Lantern?

Lantern is an open-source PostgreSQL database extension that offers advanced capabilities for storing vector data, generating embeddings, and performing vector search operations. With its new index type called lantern_hnsw, Lantern enhances query performance for ORDER BY and LIMIT queries. This AI product provides a seamless integration with PostgreSQL, ensuring compatibility with existing tools in the PostgreSQL ecosystem.


Key Features:

1. 💾 Vector Data Storage: Lantern allows you to store vector data efficiently in PostgreSQL, providing a reliable and scalable solution for managing large-scale vector datasets.

2. 🔍 Vector Search Operations: With Lantern, you can perform fast and accurate vector searches, enabling applications such as similarity matching, recommendation systems, and content-based image retrieval.

3. 📊 Embedding Generation: Lantern supports embedding generation for popular use cases, including CLIP model, Hugging Face models, and custom models. This feature empowers users to leverage powerful AI models for their specific needs.


Use Cases:

1. Retail Recommendation System: Lantern's vector search operations enable online retailers to build powerful recommendation systems that suggest relevant products based on user preferences and purchase history.

2. Image Similarity Matching: Lantern's embedding generation capability allows image processing applications to identify visually similar images, enabling tasks like image deduplication, content-based image retrieval, and visual search.

3. Natural Language Processing: Lantern's support for Hugging Face models and custom models makes it a valuable tool for NLP applications, such as sentiment analysis, text classification, and named entity recognition.


Conclusion:

Lantern offers a comprehensive solution for managing vector data, generating embeddings, and performing vector search operations within PostgreSQL. Its seamless integration with PostgreSQL ensures compatibility with existing tools, while its advanced features streamline various AI applications. Whether you need to build recommendation systems, perform image similarity matching, or enhance your NLP workflows, Lantern provides the capabilities and performance you require. Take advantage of Lantern's efficiency and explore its diverse use cases today.


More information on Lantern

Launched
2023-6
Pricing Model
Free
Starting Price
Global Rank
4516482
Follow
Month Visit
<5k
Tech used
Next.js,Vercel,Gzip,OpenGraph,Webpack,HSTS

Top 5 Countries

61.99%
35.27%
2.75%
India United States France

Traffic Sources

5.29%
1.09%
0.07%
7.29%
50.24%
36.01%
social paidReferrals mail referrals search direct
Source: Similarweb (Sep 24, 2025)
Lantern was manually vetted by our editorial team and was first featured on 2024-03-12.
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  3. pgvector: An open-source vector similarity search tool for Postgres. Store vectors with data, support exact & approximate search, and perform distance calculations. Suitable for recommendation systems, image/text retrieval, and anomaly detection.

  4. PGVecto.rs is a Postgres extension that enables scalable vector search, allowing you to build powerful similarity-based applications on top of your Postgres database.

  5. Build powerful AI applications with Supabase Vector. Store, query, and index vector embeddings using Postgres and Supabase's AI toolkit.