vector-index-tuning

Tune HNSW parameters for vector search by adjusting M, efConstruction, and efSearch. This balances graph connectivity and search depth to meet specific latency targets while maintaining required recall levels for your production workloads.

Reduce memory footprint for large vector indexes by applying quantization strategies like INT8, PQ, or binary quantization. These techniques compress vector data representations to fit within strict memory budgets without entirely sacrificing search performance.

Choose the right vector index type by evaluating dataset size and performance requirements against Flat, HNSW, and IVF+PQ options. Selecting the correct index structure ensures optimal memory usage and search latency for your specific workload scale.

Estimate memory and workload requirements before deploying vector search pipelines by calculating capacity plans based on index type, quantization settings, and data dimensions. This ensures your infrastructure meets real-world production demands.

Vector search latency increases on large collections when index parameters are misconfigured or memory limits are exceeded. Optimize HNSW settings and apply quantization to maintain fast query speeds as data scales up.

Use IVF+PQ instead of HNSW for vector indexing when operating under tight memory budgets on massive collections. IVF+PQ partitions data and compresses vectors, trading exact recall for lower memory consumption compared to graph-based HNSW indexes.