# memsql.com > Markdown mirror of DialtoneApp's public top-site detail page for `memsql.com`. URL: https://dialtoneapp.com/top-sites/memsql.com/index.md Canonical HTML: https://dialtoneapp.com/top-sites/memsql.com ## Summary - Domain: `memsql.com` - Website: https://memsql.com - Description: ai readable | score 16 | purchase read only - Label: ai_readable - Payment surface: Not available - Purchase boundary: read_only - Control boundary: unknown - Rank: 688524 ## robots ~~~text User-agent: * Allow: / Disallow: /assets/ Disallow: /_redirects.json Disallow: /_ab_tests.json Sitemap: https://www.singlestore.com/sitemap-index.xml Host: https://www.singlestore.com ~~~ ## llms ~~~text # SingleStore > SingleStore is a distributed SQL database for real-time AI and analytics, unifying transactions, analytics, vector search, and full-text search in one engine. ## Product - [AI Features](https://www.singlestore.com/ai/): Overview of SingleStore's AI capabilities including vector search, AI functions, LLM integration, and real-time data access for AI applications. - [Product Overview](https://www.singlestore.com/product-overview/): Detailed breakdown of SingleStore's architecture, core capabilities, and differentiated features versus traditional databases. - [Ultra-Fast Queries](https://www.singlestore.com/platform/ultra-fast-queries/): How SingleStore achieves sub-second query performance on large datasets through columnar storage, in-memory processing, and compiled query execution. - [Multi-Model Data](https://www.singlestore.com/platform/multi-model/): SingleStore's support for relational, JSON, vector, time-series, and full-text data in a single engine, eliminating the need for multiple specialized databases. - [Search](https://www.singlestore.com/platform/search/): Full-text, vector, and hybrid search capabilities built into the database, enabling semantic and keyword search without an external search engine. - [Data Integration](https://www.singlestore.com/platform/data-integration/): Built-in connectors and pipeline tools for ingesting data from Kafka, S3, MySQL, and other sources directly into SingleStore without external ETL. - [High Availability](https://www.singlestore.com/platform/high-availability/): SingleStore's replication, failover, and durability architecture for production workloads requiring continuous uptime. - [Kai (MongoDB API Compatibility)](https://www.singlestore.com/connectors/kai/): SingleStore Kai enables existing MongoDB applications to connect to SingleStore using the MongoDB wire protocol without code changes. - [Pricing](https://www.singlestore.com/pricing/): Pricing tiers for SingleStore including cloud, self-managed, and free trial options with compute and storage details. - [Get Started](https://www.singlestore.com/blog/how-to-get-started-with-singlestore/): Free trial signup and quickstart guide for new SingleStore users. ## Solutions - [AI Solutions](https://www.singlestore.com/solutions/ai/): Use cases for AI workloads including vector search, real-time inference, and AI application backends. - [RAG with SingleStore](https://www.singlestore.com/solutions/retrieval-augmented-generation/): Architecture and implementation guidance for building retrieval-augmented generation systems. - [Real-Time Analytics](https://www.singlestore.com/solutions/real-time-analytics/): Real-time analytics on live operational data without ETL, covering architecture, use cases, and performance benchmarks. - [Financial Services](https://www.singlestore.com/solutions/financial-services/): Deployments for fraud detection, risk analytics, and real-time transaction processing. - [Government & Public Sector](https://www.singlestore.com/industries/government-public-sector/): Use cases for defense, civilian government, and public services data applications. ## Comparisons - [SingleStore vs. ClickHouse](https://www.singlestore.com/blog/singlestore-vs-clickhouse-why-consistent-vector-search-latency-matters/): Benchmark comparison focusing on consistent vector search latency, showing SingleStore's advantage for mixed workloads at production scale. - [SingleStore vs. Classic Data Stack](https://www.singlestore.com/blog/singlestore-vs-the-classic-data-stack-why-real-time-and-ai-break-patchwork-architectures/): Analysis of why patchwork architectures (data lake + warehouse + vector DB) fail for real-time AI and how a unified platform addresses the gaps. - [SingleStore vs. Elasticsearch](https://www.singlestore.com/elasticsearch/): Feature and performance comparison with Elasticsearch for hybrid search, operational analytics, and cost efficiency. - [SingleStore vs. PostgreSQL](https://www.singlestore.com/comparisons/postgresql/): Performance and feature comparison with PostgreSQL covering scalability, real-time analytics, and operational workloads. - [SingleStore vs. MongoDB](https://www.singlestore.com/comparisons/mongodb/): Head-to-head comparison with MongoDB covering multi-model data, query performance, and total cost of ownership. - [SingleStore vs. Pinecone](https://www.singlestore.com/pinecone-vector-database/): Comparison with dedicated vector databases, covering total cost and operational complexity when replacing a standalone vector store. - [Migrating from MySQL to SingleStore](https://www.singlestore.com/migrating-from-mysql-to-singlestore/): Comparison and guide for migrating MySQL workloads to SingleStore, covering compatibility, migration steps, and performance benefits. ## Blog - [GraphRAG: Improving Multi-Hop Reasoning](https://www.singlestore.com/blog/rethinking-rag-how-graphrag-improves-multi-hop-reasoning-/): How Graph-based RAG outperforms standard RAG for complex multi-hop reasoning tasks. - [A Guide to Retrieval Augmented Generation (RAG)](https://www.singlestore.com/blog/a-guide-to-retrieval-augmented-generation-rag/): Foundational guide to RAG covering architecture, embedding strategies, retrieval methods, and practical implementation patterns. - [Context Engineering: A Definitive Guide](https://www.singlestore.com/blog/context-engineering-a-definitive-guide/): Structuring data context for LLM prompts, covering retrieval strategies, chunking, and relevance ranking. - [Hybrid Search Using Reciprocal Rank Fusion](https://www.singlestore.com/blog/hybrid-search-using-reciprocal-rank-fusion-in-sql/): Tutorial for combining vector similarity and BM25 full-text scores in SQL. - [Agentic RAG with SingleStore](https://www.singlestore.com/blog/agentic-rag-with-singlestore/): Building RAG pipelines that use agentic reasoning to dynamically select retrieval strategies and refine answers. ## Docs & Resources - [Documentation](https://docs.singlestore.com/): Official documentation covering SQL reference, cluster management, vector indexing, pipeline configuration, and security. - [Developers](https://www.singlestore.com/developers/): Quickstarts, code samples, API documentation, and the SingleStore developer community. - [Resources](https://www.singlestore.com/resources/): Whitepapers, datasheets, webinar recordings, and technical guides. - [SingleStore Spaces](https://www.singlestore.com/spaces/): Interactive notebook environment for building and prototyping AI applications in the browser. - [Training](https://www.singlestore.com/training/): Self-paced learning paths, certification programs, and instructor-led training. - [Free Database Tools](https://www.singlestore.com/free-database-tools/): JSON-to-SQL converter, dummy data generator, and PlanetScale migration tool. - [Support](https://www.singlestore.com/support): Technical support portal and resources for SingleStore customers. ## Company - [About SingleStore](https://www.singlestore.com/company/): Company history, mission and leadership. - [Careers](https://www.singlestore.com/careers/): Open roles across engineering, sales, marketing, and operations. - [Partners](https://www.singlestore.com/partners/): Technology and channel partner ecosystem including cloud providers and system integrators. - [Security](https://www.singlestore.com/security/): Security practices, certifications, and compliance documentation. - [Contact](https://www.singlestore.com/contact/): Sales inquiries, support contacts, and office locations. ## Key Facts - Founded in 2011 as MemSQL; rebranded to SingleStore in 2021 - Headquartered in San Francisco, California, USA - ~400 employees (as of early 2026) - Core product: distributed SQL database with native vector search, full-text search, JSON, time-series, and OLAP in a single engine - Key differentiator: unified HTAP platform — no separate OLTP + OLAP + vector databases required - Deployment: cloud-native (AWS, Azure, GCP) via SingleStore Helios; also self-managed on-premises or hybrid - MySQL wire-compatible: existing MySQL applications connect without code changes - Industry: Cloud database infrastructure, AI/ML data platform, real-time analytics - Open source: No (commercial product with free developer options) - GitHub: github.com/singlestore-labs (195+ repositories, actively maintained) ## Contact - Website: https://www.singlestore.com/ - Documentation: https://docs.singlestore.com/ - Support: https://support.singlestore.com/ - GitHub: https://github.com/singlestore-labs - LinkedIn: https://www.linkedin.com/company/singlestore - Twitter/X: https://twitter.com/singlestoredb - YouTube: https://www.youtube.com/singlestore - Reddit: https://www.reddit.com/r/SingleStoreCommunity/ ~~~ ## llms-full Not found.