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Designed for intelligent applications, SingleStore is the world's only real-time data platform that can read, write and reason on petabyte-scale data in a few milliseconds.

Streaming ingestion + fast writes
10M upserts/sec

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High concurrency
100k+ connections

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Fast vector search
2-3x faster

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Record lookups
1-15 ms

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everything-you-needEverything you need,
all in one data platform


Power mission-critical applications with a horizontally scalable, ACID-compliant SQL database.

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High-throughput data ingestion and on-disk columnstore deliver real-time performance, even on complex analytics involving JOINs over hundreds of tables.

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Vector search, full-text search, relational filters and aggs in a single query engine.

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global-leaders-chooseGlobal leaders choose

building-real-time-applications-analytics-with-single-storeBuilding real-time applications + analytics with SingleStore

Streaming data ingestion

High throughput, parallel, lock-free streaming ingestion – up to millions of events per second – from diverse data sources including Apache Kafka, blob stores (S3, Azure blob storage, GCS) or HDFS.

Low-latency queries

Millisecond query performance that outstrips the performance of top data warehouses on popular analytical benchmarks, including those derived 
from TPC-H and TPC-DS.


Drive blazing-fast analytics on more than just relational data by combining JSON, time-series, vector, full-text search, geospatial and more. And now with SingleStore Kai™, achieve 100-1500x faster JSON/BSON analytics for appllications built on MongoDB®.

Universal Store

SingleStore's unique Universal Storage combines the fast scan performance
of a columnstore and the CRUD performance of a rowstore database to deliver high-performance analytics on operational data in real time — without the need for data movement.

Read replicas

SingleStore compute workspaces allow independent scaling of compute for different workloads while utilizing a shared database.

Separation of storage and compute

Get bottomless storage to scale compute more cost-efficiently and meet the needs of dynamically changing workloads.