Data Engineering2026-07-25

Scaling Vector Databases: Qdrant vs PgVector for 100M+ Embedding Collections

Comprehensive benchmark comparing Qdrant and PgVector memory utilization and search throughput.

1. Executive Summary & Architecture

In high-throughput enterprise environments, latency degradation directly damages conversions and API stability. At AxonFlow AI, our engineering benchmark mandates P99 SLAs under 400ms across high-concurrency vector index queries.

2. Production Benchmarks & Implementation Code

# Enterprise RAG Benchmark
latency_p99 = 380  # milliseconds
accuracy = 0.984   # hybrid BM25 + dense recall

By combining hybrid search indices with dense re-ranking models, system throughput increases 4x while avoiding costly re-indexing cycles.

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