AgentDB Performance Optimization

Vector quantization reduces memory consumption 4-32x by compressing embeddings into binary, scalar, or product formats while preserving search accuracy. Apply quantization types to your AgentDB store and benchmark memory metrics before and after to measure savings across your vector dataset.

HNSW indexing accelerates vector search up to 12,500x faster by organizing vectors hierarchically for sub-millisecond query response times. Configure HNSW parameters—M, efConstruction, and efSearch—to balance search speed and recall for your scale, from edge devices to millions of vectors.

Yes. Combine binary quantization for memory reduction with HNSW indexing and in-memory caching to deploy AgentDB on resource-constrained environments. This approach maintains sub-millisecond search latency while minimizing the memory footprint required for edge or mobile inference pipelines.

Batch insert and retrieval APIs reduce per-operation overhead and increase throughput for high-volume vector processing. Use batch operations alongside caching strategies to optimize database interactions when handling real-time inference pipelines or large-scale analytics workloads.

Binary quantization offers maximum compression; scalar quantization balances memory and accuracy; product quantization provides granular control. Choose based on your accuracy requirements and memory constraints—benchmark each type against your similarity search precision targets.