SingleStore Matches Vector Search Performance of Pinecone and Zilliz — Plus Gives Benefits of a Modern SQL Database

The world has changed with the advent of ChatGPT, sparking a revolution in how we interact with AI. Large Language Models (LLMs) have ushered in a new era of applications like semantic search and Retrieval-Augmented Generation (RAG), which rely on vector search — a critical enabler for modern applications striving to deliver smarter, faster and more intuitive user experiences. Specialty databases like Pinecone and Zilliz have demonstrated the value of purpose-built vector databases in accelerating AI-driven workloads, while virtually all major SQL and NoSQL databases have responded by adding indexed vector search capabilities. However, achieving competitive queries per second (QPS) per dollar at a fixed level of recall is crucial for many applications, and SingleStore has validated that it delivers competitive QPS/$ for vector workloads while offering robust analytics and transactional capabilities. SingleStore's performance tests show cost-competitive performance with specialty vector databases like Pinecone and Zilliz, combining competitive vector search performance with fast SQL analytics, joins, and aggregations across petabytes of structured and semi-structured data to power intelligent applications.

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