Vector Databases

Fully managed, developer-friendly, and easily scalable vector database built for production AI applications.

Open-source vector database that lets you store data objects and vector embeddings from your favourite ML models, scaling seamlessly into billions of objects.

Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning models.

An AI Search Platform for large-scale applications combining big data, vector search, machine-learned ranking, and real-time inference.

Build intelligent, AI-powered applications with Redis Enterprise — delivering ultra-low latency vector similarity search at scale.

A vector database and vector similarity search engine deployed as an API service, providing fast nearest-neighbour search for high-dimensional vectors.

Delivers built-in similarity search on vectors to add persistent memory for generative AI applications — no separate vector store needed.

The AI-native open-source embedding database. Makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs.

Unique approach to vector indexing that addresses the limitations of single-node PostgreSQL systems when dealing with large-scale vector datasets.

A highly scalable distributed vector search engine designed for large-scale approximate nearest-neighbour search workloads.

Fully managed Milvus service by its creators. Simplifies deploying and scaling vector search by eliminating complex infrastructure management.

Facebook AI Similarity Search — a library for fast search of embeddings of multimedia documents that are similar to each other, optimised for billion-scale datasets.

Designed for multimodal, built for scale. From agents to models, from search to training — one platform for all your AI data and workloads.