Qdrant vs pgvector
A head-to-head comparison of two leading vector databases for AI-powered growth. See how they stack up on pricing, performance, and capabilities.
Qdrant
Pricing: Free tier (1GB), then $25/mo cloud; open-source self-hosted
Best for: Performance-sensitive workloads with complex filtering needs
pgvector
Pricing: Free (open-source PostgreSQL extension)
Best for: Teams already on PostgreSQL with under 5M vectors
Head-to-Head Comparison
| Criteria | Qdrant | pgvector |
|---|---|---|
| Setup Complexity | Low (cloud) to moderate (self-hosted) | Minimal — runs inside existing Postgres |
| Cost at 1M Vectors | ~$25/mo cloud; near-zero self-hosted | Incremental Postgres cost, often under $10/mo |
| Query Latency | ~1-10ms p99 | ~10-50ms p99; slower under heavy concurrent load |
| Hybrid Search | Native sparse + dense hybrid | Full SQL joins combine vector + relational data |
| Scaling Ceiling | Billions of vectors, purpose-built ANN | Best under 5M vectors without Postgres sharding |
The Verdict
The decision between Qdrant and pgvector hinges on whether you already have Postgres and how many vectors you need to store. pgvector requires no new infrastructure for Postgres shops and lets you combine vector search with SQL joins elegantly, but performance degrades noticeably above a few million vectors. Qdrant is a purpose-built engine with demonstrably faster ANN queries at any scale and native sparse-dense hybrid support. If you're starting a new service or your vector count will exceed 5M, Qdrant is the stronger choice.
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