- 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:
- AI startups building RAG applications; Teams wanting managed simplicity
- Top complaint:
- $50/month minimum nuked hobby projects
- Price:
- Free plan, paid from $20/mo
Score Comparison
Signals Comparison
pgvector
- pgvector error: extension "vector" is not available(6/10)
Pinecone
- Learning curve to fully leverage capabilities(4/10)
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)
Pinecone
No signals
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)
Pinecone
- No self-hosted or open-source option(6/10)
- Missing features and customization limits(5/10)
- Free tier limited to single AWS region(4/10)
pgvector
- HNSW build fails on shared memory or runs out of RAM(7/10)
Pinecone
- 264+ outages over 3 years on gcp-starter(8/10)
pgvector
- Extension availability and version lag differ by managed host(5/10)
Pinecone
- Severe vendor lock-in with no export tools(7/10)
pgvector
No signals
Pinecone
- $50/month minimum nuked hobby projects(8/10)
- Costs become expensive at scale(8/10)
- Cost predictability concerns for growing teams(5/10)
pgvector
No signals
Pinecone
- AWS Marketplace integration issues(5/10)
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
Pinecone
Users: Up to 2 users
Storage: 2 GB
Limits: Single region only, 1 project, 2 users max, no enterprise features, limited support
Users: Up to 5 users
Storage: 10 GB
Limits: 10 GB storage cap
Users: Unlimited
Storage: Pay per GB
Limits: $50/month minimum regardless of usage - steep for hobby projects
Users: Unlimited
Storage: Pay per GB
Limits: $500/month minimum commitment, annual contract preferred
Users: Unlimited
Storage: Custom
Limits: Contact sales, minimum spend likely very high, longer deployment time
Feature Comparison
| Feature | pgvector | Pinecone |
|---|---|---|
| Indexing | ||
| HNSW index | ||
| IVFFlat index | ||
| Indexing vectors above 2,000 dimensions Use halfvec (4,000) or reduce dimensions | ||
| core | ||
| Vector Similarity Search | ||
| Hybrid Search | ||
| Metadata Filtering | ||
| Namespaces | ||
| reliability | ||
| Serverless Scaling | ||
| Multi-Region | ||
| Search | ||
| Cosine, L2, inner product, L1 distance | ||
| Iterative index scans for filters hnsw.iterative_scan and ivfflat.iterative_scan | ||
| Types | ||
| halfvec, bit, sparsevec types | ||
| Database | ||
| SQL joins and transactions | ||
| security | ||
| RBAC & SSO | ||
| integration | ||
| LangChain Integration | ||
| RESTful API | ||
| ai | ||
| Inference API | ||
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