Zilliz Adds Cost-Aware Benchmarking to VDBBench, the Open-Source Vector Database Benchmark
VDBBench already tests vector databases under real production workloads; The new release lets any team measure what a vector database actually costs to run in production — across data freshness, filtering, multitenancy, and cold starts — not just peak queries per second.
Zilliz, a leading AI data infrastructure company and the creator of Milvus, recently announced a major update to VectorDBBench (VDBBench), an open-source, vendor-neutral benchmark for vector databases, adding cost as a first-class dimension alongside performance under production conditions — not just how fast a system performs in an idealized test.
Most benchmarks optimize for a single headline number: peak queries per second (QPS) on static, fully indexed data. But teams rarely choose a vector database on speed alone. They need to know what it costs to hit a target QPS, when newly written data actually becomes searchable, how filters and payload size change the query surface, how a system holds up across thousands of tenants, and how it responds on the first query after sitting idle. VDBBench now measures those dimensions directly.
