Elasticsearch and Qdrant benchmarks criticized in production case study

The author argues that public vector database benchmarks are often misleading or useless for real-world engineering decisions because they fail to test production-scale workloads, concurrent writes, and filtering under realistic conditions. A comparative evaluation of Elasticsearch and Qdrant using 1.2 million production items revealed that both systems easily handled high-throughput traffic, but each excelled under different operational demands such as freshness, ingest rate, and query complexity. Open article →