Latency benchmarks for leading AI databases vary based on workload, data size, and infrastructure, but general patterns emerge when comparing specialized systems. These databases are designed for tasks like vector similarity search, real-time inference, or hybrid transactional/analytical processing. Key factors influencing latency include dataset dimensions (e.g., vector size), query complexity (e.g., k-nearest neighbor search depth), hardware resources (e.g., GPUs vs. CPUs), and indexing strategies (e.g., hierarchical navigable small world graphs). Below, we’ll explore specific benchmarks for popular tools while emphasizing practical considerations for developers.
For vector search databases, Milvus and Pinecone provide well-documented latency metrics. Milvus, an open-source system, reports query times under 10 milliseconds for nearest neighbor searches on 1 million vectors with 100 dimensions when using an HNSW index and GPU acceleration. However, latency can increase to 20-50 ms for larger datasets (e.g., 100 million vectors) or when prioritizing high recall rates with methods like IVF. Pinecone, a managed service, emphasizes single-digit millisecond latency for similar workloads by optimizing distributed indexing and auto-scaling. For example, a 768-dimensional vector query on a 10-million-vector dataset might take 5-7 ms in Pinecone’s default configuration. Both systems allow tuning: sacrificing some recall for speed by adjusting index parameters (e.g., reducing HNSW layers) can cut latency by 30-50%. Redis with RedisSearch and vector support also achieves sub-millisecond latencies for smaller datasets stored in-memory, but performance degrades when queries involve complex joins or cross-index operations.
Inference-focused databases like Seldon or RedisAI prioritize low-latency model serving. RedisAI executes preloaded TensorFlow or PyTorch models with median response times of 1-2 ms for simple inferences (e.g., image classification) on GPU instances. Amazon Aurora ML and Google Vertex AI offer latencies between 50-200 ms for end-to-end prediction pipelines, influenced by network overhead and cold starts. For hybrid systems like SingleStore, which handles both vector search and SQL analytics, latency depends on workload isolation: queries on dedicated vector columns can achieve 5-10 ms response times, but mixed workloads (e.g., joining vector results with transactional data) may take 20-100 ms. Developers should test these tools against their specific data shapes—higher-dimensional vectors (e.g., 1024 dimensions) or complex filters can multiply latency. Ultimately, choosing a database involves balancing latency requirements with scalability, maintenance costs, and integration complexity, as no single system dominates all benchmarks.