The open-source vector database Endee.io, that is well known for its Ultra High performance with 10x lower Infra, is now available as a fully managed cloud
Endee Labs today announced the launch of Endee Cloud, a fully managed, serverless vector database built for production AI workloads. Endee Cloud makes the fastest and most cost-efficient open-source vector database available as a zero-ops managed service, with a free Starter plan and paid Pro and Scale tiers for teams running mission-critical AI search, retrieval-augmented generation (RAG), and recommendation systems at scale.
Endee is the vector database for production AI. The fastest, most accurate, most cost-efficient, and now the easiest to deploy.”
— Vineet Dwivedi, Founder, Endee Labs
Endee is an open-source, Apache 2.0-licensed vector database. On VectorDBBench and other independent vector database benchmarks, Endee delivers the highest throughput (QPS), the highest recall, the lowest P99 latency, and the lowest cost per query of any tested vector database, outperforming Pinecone, Qdrant, Milvus, Weaviate, and others across all metrics simultaneously at 10x lower infrastructure.
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BENCHMARK PERFORMANCE
Highest throughput (QPS): Endee delivers more queries per second than any competing vector database, making it the highest-throughput option per dollar for high-traffic AI search, real-time recommendations, and production RAG pipelines.
Highest recall (accuracy): Endee achieves the highest recall of any tested vector database. For RAG pipelines and AI agents, higher recall means fewer hallucinations and more grounded, reliable AI output.
Lowest P99 latency: Even at the 99th percentile, Endee returns results faster than any competing vector database. Sub-10ms tail latency makes it the right choice for real-time AI applications, interactive search, and latency-sensitive retrieval workflows.
Lowest cost per query: Endee’s C++ core, SIMD acceleration, filter-aware HNSW indexing, and multi-precision quantization deliver dramatically better cost efficiency, running production workloads on modest hardware where other vector databases require expensive, memory-heavy clusters.
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