VectorDBBench - Actually Useful Vector Database Benchmarks

Look, I've been down this rabbit hole. You're trying to pick a vector database and every vendor shows you these beautiful benchmark charts where their database absolutely destroys the competition. Problem is, those benchmarks are testing toy scenarios that have nothing to do with your actual workload.

VectorDBBench exists because someone at Zilliz got as frustrated as I did with this bullshit. Version 1.0.8 released in September 2025 represents a major step forward and it's the first benchmarking tool that actually tries to break databases the way production will.

The Three Ways Traditional Benchmarks Lie to You

They Test Ancient Data Formats: Most benchmarks still use SIFT datasets with 128 dimensions. Meanwhile, your OpenAI embeddings are 1536D and Cohere's latest models push 4096D. The performance characteristics are completely different - what works for 128D vectors will fall over when you hit it with modern embedding sizes.

They Show You Vanity Metrics: "Look, 50,000 QPS!" Yeah, for 30 seconds with no concurrent writes and perfect cache conditions. Real production cares about P99 latency when your database has been running for 6 hours straight with users hammering it. Average latency is meaningless when half your queries take 2+ seconds.

They Ignore Production Chaos: Traditional benchmarks test the happy path - build index, run queries, done. Real systems have streaming ingestion happening while users are searching, metadata filters that eliminate 99.9% of vectors, and the kind of concurrent load that makes poorly designed databases shit themselves.

How VectorDBBench Actually Tests Reality

Instead of synthetic bullshit, VectorDBBench uses vectors from real embedding models: Wikipedia dumps processed through Cohere embeddings, BioASQ datasets at 1024D, and web-scale corpora with 138 million vectors. You know, the kind of data you'll actually be indexing.

It tests streaming workloads where data keeps coming in while queries are running. It throws high-selectivity filters at databases and watches them die. It measures sustainable throughput over hours, not peak performance over seconds.

Most importantly, it focuses on the metrics that actually matter in production: P95/P99 latencies, recall accuracy under load, and whether your database can handle Friday afternoon traffic without falling over.

I tested this against our production Pinecone setup and the results were scary accurate. If VectorDBBench says a database will handle 10k QPS sustained, it probably will. If traditional benchmarks say it'll handle 50k QPS, it definitely won't.

Okay, here's what this tool actually does that'll save you from making expensive mistakes in production:

Tests That Will Break Your Database (In a Good Way)

Capacity Reality Checks: Most databases claim they can handle "billions" of vectors. VectorDBBench throws GIST datasets with 960D vectors and large-scale embedding datasets at them until they break. I learned this the hard way when our "scalable" database crashed with SIGSEGV at 50M vectors despite the vendor claiming it could handle 1B+.

Filtered Search Hell: This is where most databases shit the bed. VectorDBBench tests metadata filtering that eliminates 99.9% of vectors - exactly what happens when users search for "red cars under $20k in California." Traditional ANN indexes fall apart here, and most vendors don't test this scenario.

Streaming Ingestion Chaos: Production systems don't get the luxury of "build index, then query." VectorDBBench simulates real-time ingestion while users are hammering the database with queries. This is where you find out which databases lock up during index rebuilds.

Datasets That Actually Represent Modern AI

Finally, someone uses realistic data:

• Cohere embeddings from Wikipedia (768D) - what most RAG systems actually use
• OpenAI text-embedding-3-large (1536D) - industry standard for semantic search
• MS MARCO with 138M vectors (1536D) - realistic scale testing
• BioASQ domain datasets (1024D) - specialized use cases

None of this 128D SIFT bullshit that tells you nothing about modern workloads.

Metrics That Actually Predict Production Pain

P99 Latency Focus: Average latency is a lie. VectorDBBench measures tail latency because that's what kills user experience. A system with 5ms average and 3s P99 will make users hate you.

Sustainable Load Testing: Peak QPS measured over 30 seconds is marketing bullshit. This tool runs sustained load tests for hours and measures performance degradation. You'll discover which databases maintain performance and which ones slowly die.

Recall vs Speed Trade-offs: Every speed optimization in vector search trades accuracy for performance. VectorDBBench shows you exactly what you're giving up, so you can make informed decisions instead of discovering your search results suck after deploying.

Web Interface That Doesn't Suck

The web interface actually works, unlike most open-source tools:

• No YAML Hell: Configure tests through forms instead of debugging Kubernetes manifests
• Live Results: Watch databases die in real-time with performance graphs
• Cost Analysis: For cloud services, see exactly how much that extra performance costs
• Export Everything: CSV exports for executives who want charts in PowerPoint

Custom Dataset Support (When You Need It)

The best part is testing with your actual data. Upload Parquet files with your embeddings and watch VectorDBBench tell you which database will handle your specific workload.

I tested our production Sentence Transformers embeddings (384D) with custom metadata filters. The results were eye-opening - the database we almost chose would have been a disaster for our specific use case, but benchmarks with public datasets made it look great.

Pro tip: If you're doing hybrid search or have complex filtering requirements, custom dataset testing is mandatory. Public benchmarks won't catch your edge cases.