Cosdata HNSW
High-Performance Vector Database
Industry-leading performance with up to 1,770+ queries per second on a single node. Combines hybrid search capabilities with unmatched scalability and precision for enterprise-grade vector search at any scale.
Key Features
Next-Generation Vector Database
Cosdata HNSW is a cutting-edge vector database designed for high performance, scalability, and ease of use. It combines advanced indexing algorithms with efficient resource utilization to deliver exceptional search performance for AI applications.
Explore the code
High Performance
Lightning-fast indexing and query responses with industry-leading concurrent requests per second (RPS), optimized for high-dimensional data at scale.
Hybrid Search
Enhanced search precision by combining sparse and dense vector searches to deliver highly relevant, context-rich results for complex queries.
Enterprise-Grade
Robust data isolation, security with role-based access control, multiple deployment options, and Git-like versioning for data integrity.
Scalable Architecture
Engineered to grow alongside your data and query demands with near-linear scalability, maintaining consistent performance even with massive datasets.
Additional Capabilities
Efficient Resource Utilization
Provably efficient data structures and algorithms ensure outstanding performance while providing increasingly relevant search results.
Advanced Quantization
Configure scalar quantization (2-bit, 3-bit) and product quantization for enhanced compression and improved recall trade-offs.
Intuitive APIs
Elegantly crafted HTTP RESTful APIs with client SDKs available in multiple programming languages for effortless integration.
Multi-Modal Support
Real-time querying and dynamic index updates for multi-modal data (text, images, audio) without downtime or delays.
Industry-Leading Performance
Cosdata's open-source HNSW vector database outperforms industry leaders across multiple key metrics. These results are from indexing DbPedia's 1M record, 1536-dimension dataset, using the same methodology as Qdrant's benchmarks.
| Vector DB | Indexing Time (m) | RPS | Precision | p50 (ms) | p95 (ms) |
|---|---|---|---|---|---|
| CosdataFastest | 16.32 | 1758 | .97 | 6.61 | 7.87 |
| Qdrant | 24.43 | 1238 | .99 | 3.54 | 4.95 |
| Weaviate | 13.94 | 1142 | .97 | 4.99 | 7.16 |
| Elastic Search | 83.72 | 716 | .98 | 22.10 | 72.53 |
Why We're Faster
Our implementation achieves superior performance through:
- Optimized HNSW graph construction
- Efficient memory management
- SIMD-accelerated distance calculations
- Parallel processing for multi-core utilization
- Optimized data structures for minimal overhead
Benchmarks conducted using the same methodology as Qdrant's benchmarks, on identical hardware with 8 vCPUs and 32GB RAM.
Ready to Implement Cosdata HNSW?
Our open-source vector database solution is ready for you to use in your applications. Check out our GitHub repository to get started with the fastest vector search available.