11 Best Vector Databases For Artificial Intelligence Apps

Vector databases are an essential tool for building AI applications with embeddings. Vector databases have become increasingly popular in recent years due to their ability to handle complex data types(text, image, audio, video) and efficiently search high-dimensional data. In this article, we will discuss the top 11 vector databases in the market today.

1. Faiss

Facebook AI Similarity Search (Faiss) is an open-source library that provides efficient similarity search and clustering of large-scale datasets. It uses an index structure based on the product quantization technique to perform fast similarity searches. Faiss is written in C++ programming, and Python language binding is also available. Faiss is licensed under MIT license.

2. Pinecone

Pinecone is a production-ready cloud-based vector database. It provides a simple API and requires no infrastructure management, making it easy for engineers and data scientists to build vector-based applications. Pinecone offers a free single pod to help customers get comfortable with the product and perform a simple proof of concept. It also provides paid services.

Pinecone is already used by the following companies:

3. Milvus

Milvus is an open-source vector database that provides efficient vector search and analysis. It is designed to handle large-scale machine learning and deep learning applications that require similarity search and analysis of the large dataset

Milvus is Apache 2.0 licensed free vector database. Milvus used by the following companies :

4. Chroma

Chroma is an open-source embedding database designed to help developers leverage vector embeddings for building AI applications using LLMs (Large Language Models). It consists of a Python client SDK, JavaScript/TypeScript client SDK, and a server application, allowing it to run in-memory or client/server mode. Chroma’s main focus is on ease of use.

5. pgvector

Pgvector or Postgres Vector is an open-source vector similarity search for Postgres.

It supports to search for:

  • Approximate nearest neighbor search for L2 distance
  • Inner product
  • Cosine distance

6. Qdrant

Qdrant is an open-source vector similarity engine and vector database designed for working with high-dimensional vectors in AI applications. It is developed in Rust and provides a production-ready service with a convenient API to store, search, and manage points.

Qdrant has free and paid plans. The free plan is licensed under Apache 2.0 license.

7. Weaviate

Weaviate is also an open-source vector database.It allows us to store data objects and vector embeddings from your favorite ML models, and scale seamlessly into billions of data objects. It supports

  • Vector search
  • Hybrid search
  • Generative search

8. Vald

Vald is a distributed dense vector search engine.

  • Vald is based on Kubernetes and Cloud-Native architecture
  • It is highly scalable and fast approximate nearest neighbor dense vector search engine
  • Easily scale Vald by changing Vald’s configuration.
  • It uses the fastest ANN algorithm Neighborhood Graph and Tree(NGT) or Indexing High-dimensional Data
  • It is open source and licensed under Apache 2.0

Vald is already used by the following organizations:

9. Vespa

Vespa is a fully featured search engine and vector database. Vespa can be used for the following systems :

  • Search
  • Recommendation and personalization
  • Conversational AI
  • Semi-structured navigation

Vespa is already used by the following companies:

10. Supabase

Supabase is a cloud-based platform that provides a range of tools for building applications, including a vector database that supports vector search with OpenAI. Supabase’s vector database is built on top of PostgreSQL and uses the pgvector extension for storing embeddings and performing vector similarity search.

Supabase’s vector search with OpenAI allows developers to search for similar items based on their vector representations, which can be useful for applications such as recommendation systems.

11. Elasticsearch

Elasticsearch supports vector search, which leverages machine learning to capture the meaning and context of unstructured data, including text and images, transforming it into a vector representation that can be used for similarity search. Elastic vector search can be used for the following scenarios :

  • Semantic search
  • Recommendations
  • Question answering


There you have it, the best 11 vector databases for artificial intelligence applications.