Batching multiple queries in vector search impacts latency and throughput by trading off individual request speed for overall system efficiency. Processing a batch of queries together typically increases latency for individual queries because each must wait for the entire batch to be processed. For example, if a batch of 10 queries takes 50ms to process, each query experiences a 50ms latency, whereas a single query might take 20ms alone. However, throughput—the total number of queries handled per second—improves because hardware like GPUs can parallelize computations across the batch. Instead of processing 50 single queries sequentially (taking 1,000ms total), a system might process five batches of 10 queries each in 250ms, doubling throughput from 50 to 200 queries per second. This efficiency stems from reduced overhead (e.g., fewer data transfers between CPU and GPU) and optimized use of parallel compute resources.
Batch querying is beneficial in scenarios prioritizing throughput over individual latency. For example, offline processing tasks—such as generating embeddings for a large dataset or precomputing recommendations for users overnight—can leverage batching to maximize hardware utilization. Similarly, applications like search-as-a-service platforms handling high-volume concurrent requests (e.g., e-commerce product recommendations during peak traffic) benefit from batching to scale efficiently. GPUs and other accelerators excel here, as their architectures process batched matrix operations faster than sequential requests. Batching is also advantageous when network or I/O overhead dominates, such as cloud-based vector databases where combining queries minimizes round-trip delays.
However, batch querying becomes detrimental in low-latency, real-time applications. For instance, interactive applications like chatbots or real-time anomaly detection require immediate responses; waiting to accumulate a batch introduces unacceptable delays. Small batch sizes (or no batching) are preferable here. Additionally, systems with limited memory or inefficient batch handling may degrade under large batches—for example, edge devices with constrained GPU memory might face out-of-memory errors, causing crashes or slower fallback to CPU processing. Finally, uneven query arrival rates can lead to underfilled batches, wasting resources. If a system waits 10ms to gather 100 queries but only receives 10 in that window, the latency penalty outweighs throughput gains. In such cases, dynamic batching strategies or hybrid approaches are better suited.