Scale AI applications
Scale personalization, semantic search and chatbots in the cloud. Indexing and updating vector embeddings happen in full isolation of vector search queries for predictable performance at scale. Scale up and down on demand with an architecture that separates compute-storage and compute-compute.
Get fast, efficient results
Search across vector embeddings using metadata filtering and ANN algorithms. Rockset selects the most cost-effective indexing strategy to deliver millisecond-latency results, saving on compute and memory resources. Rockset indexes minimize the amount of data stored in-memory, enabling efficient search across billions of vectors.
Embrace flexibility and simplicity
Query across nested objects, vector embeddings, geospatial and time-series data using SQL. Rockset has a flexible data model and supports in-place updates, so you can make quick, efficient changes to your vector embeddings. Its fully-managed service simplifies application development and frees teams from indexing, servers, clusters and nodes.