Pinecone is a managed vector database service with built-in scaling and simplified operations, while pgvector is a PostgreSQL extension offering self-hosted vector search within your existing database. Pinecone requires vendor lock-in and monthly costs, whereas pgvector provides lower operational overhead but demands more infrastructure management.
Pinecone vs pgvector — Side-by-side comparison on A Versus B
Managed serverless vector database with global scale and enterprise features
Open-source PostgreSQL extension enabling vector similarity search in existing databases
Deployment Model. Setup Time to Production. Starting Monthly Cost. Vector Dimensions Supported. Query Latency (avg). Multi-tenancy Support. Vendor Lock-in Risk
Choose Pinecone if you prioritize rapid deployment, minimal operational overhead, and built-in scaling for production AI applications where cost per query is acceptable. Choose pgvector if you want to avoid vendor lock-in, prefer lower total cost of ownership, already use PostgreSQL, and have infrastructure expertise to manage deployments.
Pinecone: $0 (free tier limited to 1M vectors) | pgvector: N/A
Pinecone: 3 (pod, serverless, custom) | pgvector: 2 (HNSW, IVFFlat)
Pinecone: 0 (standalone database) | pgvector: N/A
Pinecone: Pay-per-usage (storage + queries) | pgvector: N/A
Pinecone: $200-400 (managed pricing) | pgvector: N/A
Pinecone: 5-10 minutes | pgvector: 45-120 minutes
Pinecone: No (SaaS only) | pgvector: N/A
Pinecone: Basic payload filtering | pgvector: N/A
Pinecone: 99.95% (enterprise tier) | pgvector: N/A
Pinecone: 15-30 minutes | pgvector: 120-300 minutes
Pinecone: $0.40-1.25 | pgvector: $0 (self-hosted only)
Pinecone: Up to 20,000 dimensions | pgvector: Up to 2,000 dimensions
Pinecone: 99.99% | pgvector: User dependent (no SLA)
Pinecone: No (separate system) | pgvector: Yes (unified via SQL)
Pinecone: ~2,500 (closed-source) | pgvector: 4,200+ stars
Pinecone: $70 (minimum pod + index) | pgvector: N/A
Pinecone: 100M+ (unlimited with multi-pod) | pgvector: N/A
Pinecone: 20,000 | pgvector: 2,000+ (practical limit)
Pinecone: 15-20 (account + API key setup) | pgvector: N/A
Pinecone: ~3,200 (closed source) | pgvector: ~10,800
Pinecone: ~$150-200 (pod + index + compute) | pgvector: N/A
Pinecone: 100,000 vectors | pgvector: N/A
Pinecone: Metadata filtering only | pgvector: N/A
Monthly Cost (1M vectors, 1K queries/day)
Pinecone: 5 billion (enterprise) | pgvector: N/A
Pinecone: 25+ (LangChain, LlamaIndex, OpenAI) | pgvector: N/A
Pinecone: Limited; JSON export only, subject to egress costs | pgvector: N/A
Pinecone: 3-5 minutes | pgvector: 1-4 hours
Pinecone: $50 (Starter tier minimum) | pgvector: N/A
Pinecone: 10B+ (unlimited) | pgvector: N/A
Pinecone: Advanced (AND/OR/NOT) | pgvector: N/A
Pinecone: 100,000 free vectors | pgvector: Unlimited (self-hosted)
Pinecone: $70 | pgvector: $0 (infra only)
Pinecone: 50-100ms | pgvector: 100-300ms
Pinecone: 99.9% | pgvector: Self-managed (variable)
Pinecone: Proprietary (not open-source) | pgvector: N/A
Pinecone: Limited (SaaS constraints) | pgvector: N/A
Pinecone: $0 (free tier with limits) | pgvector: N/A
Pinecone: $84 (Pro plan, 5M vectors) | pgvector: N/A
Pinecone: 1B+ (distributed) | pgvector: <1 billion (practical limit)
Pinecone: Yes (REST + gRPC) | pgvector: N/A
Pinecone: Yes (SOC 2 Type II, HIPAA) | pgvector: N/A
Pinecone: $4.00 + query fees | pgvector: N/A
Pinecone: SaaS only (managed) | pgvector: N/A
Pinecone: Yes (dense + BM25) | pgvector: N/A
Pinecone: Proprietary SDK + REST | pgvector: N/A
Pinecone: Yes, official integration | pgvector: N/A
Pinecone: 6+ languages (Python, Node.js, Go, Java, Rust, gRPC) | pgvector: N/A
Pinecone: SOC 2 Type II, HIPAA-ready, GDPR compliant | pgvector: N/A
Pinecone: N/A | pgvector: PostgreSQL extension module
Pinecone: N/A | pgvector: 2 methods (IVFFlat, HNSW)
Average Query Latency (1M vectors, 384-dim)
Pinecone: N/A | pgvector: None (requires external integration)
Minimum Monthly Infrastructure Cost (Self-hosted Production)
Pinecone: N/A | pgvector: No, application-level only
Pinecone: N/A | pgvector: 1-3 (read replicas)
Query Latency (1M vectors, 768-dim, 10th percentile)
Pinecone: N/A | pgvector: No (external only)
Pinecone: N/A | pgvector: Integrated (no new deployment)
Pinecone: N/A | pgvector: Full SQL WHERE clauses (unlimited)
Pinecone: N/A | pgvector: PostgreSQL License (permissive)
Pinecone: N/A | pgvector: Requires schema/RLS workarounds
Pinecone: N/A | pgvector: Full SQL + vector operators
Pinecone: N/A | pgvector: 10-50 before latency issues
Pinecone: N/A | pgvector: Very Low (2/10)
Pinecone: N/A | pgvector: No (SQL-only via PostgreSQL client)
Single-Vector Search Latency (1M vectors)
Pinecone: N/A | pgvector: Full SQL with vector operators
Pinecone: N/A | pgvector: No (external preprocessing required)
Pinecone: N/A | pgvector: Yes (PostgreSQL License)
Pinecone: N/A | pgvector: $20-80 (AWS RDS)
Pinecone: N/A | pgvector: Native (single database)
How to choose between Pinecone and pgvector
Pinecone vs pgvector — Structured Comparison Data
Machine-readable comparison dataset for Pinecone vs pgvector. Contains 105 attribute comparisons, 5 FAQ pairs.
Pinecone vs pgvector — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinecone vs pgvector.
Can I migrate from Pinecone to pgvector?
Yes, but with effort. You'll need to export vectors and metadata from Pinecone's API, transform them into PostgreSQL format, and re-index. pgvector uses standard SQL, so once data is loaded, applications can use standard PostgreSQL clients. The main challenge is updating application code from Pinecone's client library to PostgreSQL queries. Migration typically takes 2-4 weeks depending on dataset size.
Which is better for production AI applications?
Pinecone excels for production systems requiring guaranteed uptime (99.9% SLA), sub-100ms latency at scale, and multi-tenant isolation. pgvector is suitable for production if you can manage infrastructure or use managed PostgreSQL services (AWS RDS, Google Cloud SQL). At 10M+ vectors, Pinecone's managed scaling becomes advantageous; below 1M vectors, pgvector's cost advantage dominates.
At 10M vectors with 100k daily queries: Pinecone costs ~$500-1,000/month (index + queries). pgvector on managed PostgreSQL (AWS RDS) costs ~$200-400/month for equivalent compute, plus operational overhead. For 100M+ vectors, Pinecone's per-query model can exceed $5,000/month, while pgvector's PostgreSQL instance remains flat-rate, making pgvector 3-4x cheaper at massive scale.
Does pgvector support real-time updates like Pinecone?
Yes, pgvector updates vectors in real-time via standard SQL INSERT/UPDATE commands. However, rebuilding HNSW indices (the faster algorithm) after updates requires offline maintenance. Pinecone handles index updates automatically in the background. For applications with frequent updates (>1k/second), Pinecone's automatic index management provides an operational advantage.
Which has better documentation and community support?
Pinecone has comprehensive official documentation, dedicated support tiers, and active Discord community (~15k members). pgvector has excellent community support on GitHub, Stack Overflow, and PostgreSQL forums, but fewer official resources. Pinecone wins on structured support; pgvector wins on community problem-solving due to larger PostgreSQL ecosystem.
How to Choose Between Pinecone and pgvector
Deployment Model: Pinecone has Fully managed cloud service, while pgvector has Self-hosted PostgreSQL extension.
Setup Time to Production: Pinecone has 15-30 minutes, while pgvector has 2-4 hours.
Starting Monthly Cost: Pinecone has $0 free tier, then $70-500+, while pgvector has $0 (open source).
Vector Dimensions Supported: Pinecone has Up to 20,000 dimensions, while pgvector has Unlimited (hardware dependent).
Query Latency (avg): Pinecone has 50-100ms (P99), while pgvector has 100-300ms (P99).
Multi-tenancy Support: Pinecone has Native namespaces, while pgvector has Row-level security required.
Vendor Lock-in Risk: Pinecone has High (proprietary API), while pgvector has Low (standard SQL).