ANN-Benchmark and VectorDBBench serve distinct but complementary roles in evaluating the performance of systems that handle vector-based data, such as those used in machine learning or similarity search applications. ANN-Benchmark focuses on comparing the raw performance of algorithms for approximate nearest neighbor (ANN) search, while VectorDBBench evaluates the end-to-end capabilities of full vector database systems. Together, they help developers make informed decisions by isolating different layers of performance analysis.
ANN-Benchmark is designed to test algorithmic efficiency under controlled conditions. It measures metrics like query speed (latency), accuracy (recall), and memory usage across different ANN algorithms, such as HNSW, Annoy, or FAISS. For example, it might show that HNSW achieves 95% recall at 1ms per query on a dataset of 1M vectors, while Annoy achieves 90% recall at 2ms. These comparisons help developers choose the best algorithm for their specific needs, such as prioritizing speed over accuracy in real-time applications. By standardizing datasets (e.g., MNIST or GloVe embeddings) and hardware configurations, ANN-Benchmark ensures fair, reproducible comparisons at the algorithmic layer, independent of database overhead.
VectorDBBench, in contrast, evaluates entire vector databases like Milvus, Pinecone, or Elasticsearch as complete systems. It tests operational factors such as ingestion throughput, scalability with increasing data volume, concurrent query handling, and resource utilization (CPU/GPU, memory, disk). For instance, it might reveal that Database A handles 10,000 queries per second at 50% CPU usage, while Database B maxes out at 5,000 queries but uses less memory. This broader scope helps developers assess real-world viability, such as how a database performs when scaling from 1M to 100M vectors or balancing read/write workloads. It also highlights system-level trade-offs, like indexing time versus query latency, which aren’t visible in algorithm-only tests.
The two tools address different stages of development. ANN-Benchmark is ideal for early-stage research or prototyping, where selecting the right algorithm is critical. For example, a team building a custom recommendation system might use it to confirm that FAISS is faster than ScaNN for their embedding size. VectorDBBench becomes essential when deploying a production system, where factors like fault tolerance, integration with existing infrastructure, and cost of cloud-based solutions matter. A developer might use it to compare whether Vespa’s distributed architecture outperforms Qdrant’s single-node setup for their workload. Together, they provide a layered approach: algorithm-first optimization with ANN-Benchmark, followed by system-level validation with VectorDBBench.