Simor Consulting
Vector Database Performance Benchmark
Database Systems
Performance Analysis Overview
This comprehensive benchmark evaluates the performance characteristics of leading vector databases across key metrics including query latency, throughput, indexing performance, and resource efficiency. Our analysis covers real-world scenarios typical in AI data engineering workloads.
Key Findings
- • Pinecone leads in query latency for small datasets
- • Qdrant shows best throughput at scale
- • Weaviate offers optimal memory efficiency
- • Chroma provides best developer experience
Test Methodology
- • 1M to 100M vector datasets
- • 768-dimensional embeddings
- • Mixed read/write workloads
- • AWS c5.2xlarge instances
Performance Comparison Matrix
| Database | Query Latency (ms) | Throughput (QPS) | Memory Usage (GB) | Index Time (min) |
|---|---|---|---|---|
| Pinecone | 12.3 | 2,847 | 8.2 | 23 |
| Weaviate | 18.7 | 1,923 | 6.1 | 31 |
| Qdrant | 15.2 | 3,156 | 9.8 | 28 |
| Chroma | 22.1 | 1,445 | 5.7 | 19 |
Recommendations by Use Case
RAG Applications
Pinecone for sub-10ms latency requirements
Batch Processing
Qdrant for high-throughput workloads
Resource Constrained
Weaviate for memory efficiency
Cost Analysis
Monthly cost for 10M vectors, 1000 QPS
Pinecone $289
Weaviate $156
Qdrant $234
Chroma $89