Pinecone vs pgvector
Pinecone vs pgvector for vector search in AI applications. Covers managed vs self-hosted, scalability, cost, query performance, and which vector database to choose for your RAG pipeline or semantic search feature.
Specs Comparison
| Feature | Pinecone | pgvector |
|---|---|---|
| Type | Managed vector database (SaaS) | PostgreSQL extension (open source) |
| Hosting | Fully managed cloud service | Self-hosted or managed Postgres (Supabase, Neon, RDS) |
| Max Vectors | Billions (enterprise tier) | Millions (limited by Postgres resources) |
| Dimensions | Up to 20,000 | Up to 2,000 |
| Query Speed | <50ms p99 at scale | <100ms for small-medium datasets |
| Filtering | Metadata filtering with vector search | Full SQL WHERE clauses with vector search |
| Namespaces | Yes (multi-tenant isolation) | Tables / schemas (standard Postgres) |
| Hybrid Search | Sparse-dense vectors (keyword + semantic) | Combine with tsvector (full-text search) |
| Replication | Multi-AZ automatic replication | Standard Postgres replication |
| Free Tier | 100K vectors, 1 index | Free with Supabase (500MB) or self-hosted |
| Pricing | Starting ~$70/mo (Standard), usage-based | Free (open source) + Postgres hosting costs |
| SDKs | Python, Node.js, Go, Java, REST | Any Postgres client (pg, prisma, drizzle) |
Pinecone
Pros
- Purpose-built for vector search with optimized indexing algorithms
- Zero infrastructure management — fully managed SaaS
- Scales to billions of vectors with consistent low-latency queries
- Metadata filtering enables combining vector search with structured filters
- Multi-tenant namespaces for SaaS applications
- Hybrid search combines keyword and semantic matching
Cons
- Expensive at scale ($70+/mo even for moderate usage)
- Vendor lock-in with proprietary API
- Data leaves your infrastructure (compliance concerns)
- No SQL — vector-only operations
- Cold starts on serverless tier can affect latency
- Separate service adds architectural complexity
Best for
Large-scale AI applications (1M+ vectors) that need consistently fast vector search, multi-tenant isolation, and teams that prefer managed infrastructure over self-hosting.
pgvector
Pros
- Free and open source — no additional service costs
- Vectors live alongside relational data in the same database
- Full SQL power for combining vector search with complex joins and filters
- Works with existing Postgres hosting (Supabase, Neon, RDS, self-hosted)
- No vendor lock-in — standard PostgreSQL extension
- Simple setup — just CREATE EXTENSION vector
Cons
- Performance degrades beyond ~1M vectors without careful tuning
- Lower max dimensions (2,000) compared to Pinecone (20,000)
- No built-in multi-AZ replication optimized for vector workloads
- Requires Postgres knowledge for tuning (HNSW indexes, maintenance)
- Not purpose-built — vector search is an add-on, not the core
- Limited to IVFFlat and HNSW indexing algorithms
Best for
AI applications starting out or at small-to-medium scale that want vectors alongside relational data. Best when using Supabase or existing Postgres infrastructure and you want to avoid adding a separate service.
Verdict
Start with pgvector — it is free, lives in your existing PostgreSQL database, and handles small-to-medium scale (up to ~1M vectors) well. For most AI apps, especially those using Supabase, pgvector eliminates the need for a separate vector service entirely. Move to Pinecone when you hit scale limitations (1M+ vectors), need sub-50ms p99 latency at high throughput, or require multi-tenant namespace isolation for a SaaS product.
Frequently Asked Questions
Is pgvector good enough for production AI apps?
Yes, for most applications. pgvector handles up to ~1M vectors with good performance when using HNSW indexes. Many production RAG applications and semantic search features run on pgvector, especially those built on Supabase. Only consider Pinecone when you consistently need sub-50ms queries at very large scale.
How much does Pinecone cost compared to pgvector?
pgvector is free (open source extension). Your only cost is Postgres hosting, which can be free with Supabase or Neon. Pinecone starts at ~$70/month for the Standard tier and scales up significantly with usage. For startups and MVPs, pgvector is dramatically more cost-effective.
Can I use pgvector with Supabase?
Yes. Supabase includes pgvector as a pre-installed extension. You can enable it with a single SQL command (CREATE EXTENSION vector) and immediately start storing and querying embeddings alongside your regular data. Supabase also provides helper functions for common vector operations.
When should I switch from pgvector to Pinecone?
Consider switching when you have more than 1 million vectors, need consistent sub-50ms p99 latency under high throughput, or are building a multi-tenant SaaS that needs isolated namespaces. For most AI apps, pgvector will serve you well throughout the MVP and growth stages.
Need help choosing?
AI 4U Labs builds with both Pinecone and pgvector. We'll recommend the right tool for your specific use case and build it for you in 2-4 weeks.
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