The open-source option is 75% cheaper at scale.
Independent benchmarks show no single winner for all workloads.
The right choice depends on your filter complexity and scale.
- Does it handle filtered search without dropping recall?
- What is the p95 latency under concurrent load?
- How does cost scale at 50M vectors?
🔴 S TIER
pgvector (HNSW) (PostgreSQL)
- p50 latency ~5ms at 1M vectors
- 75% lower cost than Pinecone at 50M vectors
🟠A TIER
Qdrant
- 48% better p99 latency than pgvector
- 3.4x faster index building speed Pinecone Serverless
- Zero-ops managed service
- BYOC GA on AWS, GCP, Azure
🟡 B TIER
OpenSearch (AWS)
- Lacks TurboQuant and 1.5-bit options
- Verbose SDK compared to Qdrant
What this means for your team:
- Use pgvector if you already run Postgres and need SQL joins.
- Pick Qdrant if tail latency (p99) is your primary bottleneck.
- Choose Pinecone for prototyping or when you lack DBA resources.
Run a filtered recall test on your actual data before committing to a vendor.
P.S. pgvector 0.8.7 fixes a critical buffer overflow; upgrade if using IVFFlat.
Originally shared on LinkedIn.
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