How do I balance accuracy and latency in vector search?

Balancing accuracy and latency in vector search requires understanding trade-offs and adjusting techniques based on your application’s needs. Accuracy often depends on how thoroughly the search algorithm explores the dataset, while latency is influenced by computational complexity and infrastructure. To strike a balance, start by evaluating your use case: Does it prioritize precise results (e.g., medical image retrieval) or fast responses (e.g., real-time recommendations)? Once the priority is clear, optimize parameters, algorithms, and infrastructure to align with those goals.

One approach is to adjust the parameters of approximate nearest neighbor (ANN) algorithms, which sacrifice some accuracy for speed. For example, in HNSW (Hierarchical Navigable Small World) graphs, increasing the ef parameter (the number of candidates considered during search) improves accuracy but increases latency. Similarly, in IVF (Inverted File Index) methods, raising the nprobe value (the number of clusters to scan) yields better results at the cost of slower queries. Experiment with these settings to find a sweet spot—like setting nprobe to 10-20% of total clusters for a 10-30ms response with acceptable accuracy. Additionally, consider hybrid approaches: use a fast ANN method for an initial rough search, then refine results with a slower exact algorithm for critical cases.

Infrastructure and data optimizations also play a role. Use vector quantization (e.g., PQ—Product Quantization) to reduce memory usage and speed up distance calculations, though this may slightly lower accuracy. Deploying GPU-accelerated libraries like FAISS or CUDA-enabled ANN implementations can drastically cut latency without changing algorithms. Pre-filtering data (e.g., removing low-quality vectors) or partitioning indexes (e.g., sharding by user or region) limits the search space. For example, an e-commerce app might partition product vectors by category, reducing search scope while maintaining relevance. Regularly benchmark with real-world data to validate adjustments and ensure latency stays within acceptable bounds as the dataset grows.