- Pick it if you are:
- Teams already on Postgres; Startups building RAG prototypes
- Top complaint:
- HNSW index build is very slow once the graph outgrows memory
- Price:
- Free plan, paid from $25/mo
- Pick it if you are:
- Performance-focused AI teams; Budget-conscious startups
- Top complaint:
- Steep learning curve for vector DB newcomers
- Price:
- Free plan
Score Comparison
Signals Comparison
pgvector
- pgvector error: extension "vector" is not available(6/10)
Qdrant
- Steep learning curve for vector DB newcomers(5/10)
pgvector
- HNSW index build is very slow once the graph outgrows memory(7/10)
- Filtered vector search returns fewer rows than LIMIT(7/10)
- Queries do a sequential scan and ignore the vector index(6/10)
- Recall drops and memory grows at tens of millions of vectors(7/10)
Qdrant
- Slow startup with large collections(6/10)
- gRPC slower than REST with payloads(5/10)
- Initial ingestion hiccups at large scale(5/10)
pgvector
- operator does not exist: vector <=> unknown or real[](6/10)
- Vectors over 2,000 dimensions cannot be indexed(7/10)
- Dimension mismatch errors when you change embedding model(6/10)
- IVFFlat gives poor results when built on little data(5/10)
Qdrant
- UI is limited - no bulk operations(5/10)
- No built-in visualization capabilities(4/10)
- UI randomly stuck on loading(4/10)
pgvector
- HNSW build fails on shared memory or runs out of RAM(7/10)
Qdrant
- Startup panic errors after version upgrades(7/10)
- Instance corruption after container restarts(8/10)
- Server stops responding under normal load(6/10)
pgvector
- Extension availability and version lag differ by managed host(5/10)
Qdrant
No signals
Pricing Comparison
pgvector
Users: Unlimited
Storage: Your own disk (non-partitioned table up to 32 TB in Postgres)
Limits: No managed backups or support SLA; community support; 2,000-dim index limit on vector
Users: 2 active projects
Storage: 500 MB database
Limits: 500 MB database and 500 MB RAM leave little room for large HNSW indexes, pausing after a week idle, 2 projects max
Storage: 8 GB per project, then $0.125/GB
Limits: Larger RAM tiers are needed for big vector indexes
Users: 100 projects
Storage: 1 GB per project, 20 GB total
Limits: 1 GB storage per project, scale to zero after 5 minutes
Storage: Billed $0.35 per GB-month
Limits: No minimum but costs grow with CU-hours
Limits: Version depends on engine minor version
Qdrant
Users: Unlimited
Storage: Limited by your infrastructure
Limits: No managed hosting, manual operations, community-only support
Users: Unlimited
Storage: 4 GB disk, 1 GB RAM, 0.5 vCPU
Limits: 1 GB RAM / 0.5 vCPU / 4 GB disk, single node, limited regions and providers, no dedicated resources
Users: Unlimited
Storage: Pay per usage
Limits: Standard SLA only (99.5%), premium tier for 99.9%
Users: Unlimited
Storage: Custom
Limits: Requires a minimum spend
Feature Comparison
| Feature | pgvector | Qdrant |
|---|---|---|
| Indexing | ||
| HNSW index | ||
| IVFFlat index | ||
| Indexing vectors above 2,000 dimensions Use halfvec (4,000) or reduce dimensions | ||
| core | ||
| Vector Similarity Search | ||
| Payload Filtering | ||
| Hybrid Search | ||
| Search | ||
| Cosine, L2, inner product, L1 distance | ||
| Iterative index scans for filters hnsw.iterative_scan and ivfflat.iterative_scan | ||
| Types | ||
| halfvec, bit, sparsevec types | ||
| performance | ||
| Scalar Quantization | ||
| Binary Quantization | ||
| Database | ||
| SQL joins and transactions | ||
| reliability | ||
| Distributed Mode | ||
| Snapshot & Backup | ||
| security | ||
| Multi-tenancy | ||
| integration | ||
| REST & gRPC APIs | ||
| ai | ||
| Cloud Inference | ||
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