SingleStore

What is SingleStore?

SingleStore Kai™ is a technology designed to speed up analytics on JSON data within existing MongoDB applications — up to 100 times faster. It provides an easy-to-use API, so developers can use MongoDB commands they already know to get real-time analytics for their apps. The goal is split-second response times and elastic scalability, achieved by running the MongoDB API directly on SingleStore. It also supports creating embeddings and performing semantic searches on the SingleStore platform through MongoDB APIs.

What is SingleStore used for?

• Real-time applications and analytics: Get insights as data arrives.
• Streaming data ingestion: Handles data from many sources, ingesting millions of events per second with parallel, lock-free streaming.
• Low-latency queries: Millisecond response times; record lookups return results in 1–15ms.
• Multi-model analytics: Handles relational, JSON, time-series, vector, full-text search, and geospatial data in one place.
• Faster JSON/BSON analytics: For MongoDB-based apps, SingleStore Kai™ offers 100–1500x faster analytics.
• Universal Store: Acts as a central hub for high-performance analytics on operational data without moving it.
• Read replicas and compute workspaces: Scale compute independently for different tasks while using a shared database.
• Separation of storage and compute: Makes scaling more cost-efficient and adaptable to changing workloads.
• High concurrency: Supports over 100,000 connections.
• Vector search: Quickly find relevant vector data.
• Embeddings and semantic search: Create these using MongoDB APIs on SingleStore.

Who is SingleStore for?

SingleStore is useful for:

• Developers
• Data Analysts
• Data Scientists
• Database Administrators
• DevOps Engineers

Key Takeaways

SingleStore Kai™ lets teams add real-time analytics to existing MongoDB applications using familiar MongoDB commands, with claimed speedups of up to 100x on JSON analytics. Beyond JSON, the platform supports relational, time-series, vector, full-text, and geospatial data, with high-throughput ingestion, low-latency queries, and independent scaling of compute and storage.