The vector database built for hybrid search at scale

Build, iterate and scale AI apps in the cloud. Create relevant experiences with hybrid search, stay up to date with live data and serve tens of thousands of concurrent users on Rockset. 

Rockset as a vector database

Create vector embeddings using any machine learning model (Hugging Face, OpenAI, Cohere, etc.) and index them for fast vector search. Combine vector search results with text search, geospatial search and structured search to enhance relevance. Use Rockset for retrieval augmented generation (RAG), personalization engines, semantic search, anomaly detection and more.

Update indexes without impacting live search performance

With compute-compute separation, similarity indexing of vectors will not affect search performance. Indexing happens on a different virtual instance than search for predictable performance. Take your AI applications to production with confidence.
Compute-compute diagram

Scale out to support multi-tenant applications at high concurrency

Rockset scales out compute and memory across multiple virtual instances to achieve unlimited concurrency. It incorporates a multi-tenant search design for faster search performance. Performance scales linearly with compute for efficient AI applications.
Multi tenant apps image

Hybrid search as easy as a SQL WHERE clause

Store and index vectors, text, geospatial and relational data to create relevant AI experiences. Exploit the power of the search index with an integrated SQL engine so your searches are always executed quickly. Rank results using the Reciprocal Rank Function (RRF) or a linear combination with just a SQL ORDER BY clause.
SQL Console
We saw the immense power of real-time analytics and AI to transform JetBlue’s operations and stitching together 3-4 database solutions would have slowed down application development. With Rockset, we found a database that could keep up with the fast pace of innovation at JetBlue.
Sai Ravuru, Senior Manager of Data Science and Analytics
Read case study ->

How hybrid search works

Vector search diagramVector search diagram

Core features

Vector database

Run exact and approximate nearest neighbor searches (ANN) using Rockset for semantic search, personalization and chatbots. 

Retrieval Augmented Generation (RAG)

Augment the capabilities of LLMs by adding your own data to provide contextual results.

Large Language Models (LLMs)

Create vector embeddings using machine learning models of your choice-OpenAI, Hugging Face, Cohere and more. Rockset integrates with LangChain and LlamaIndex.


Build vector search applications using the Rockset console or SDKs- Python, Node.js, and Go.

Converged Index™

Rockset's vector search is fully integrated into its Converged Index Rockset chooses the most efficient approach to vector search without manual index management.

Production Grade

Rockset is secure and reliable with SOC 2 Type II, CCPA, GDPR and HIPAA compliance and is trusted by thousands of enterprises. 

Fully Managed

No managing indexes, servers, clusters and shards. Focus on building vector search applications.

Compute-compute separation

Rockset separates the compute for indexing vectors from the compute used for search.