Pinecone is a fully managed cloud vector database service requiring no infrastructure setup, while pgvector is an open-source PostgreSQL extension that requires self-hosting and maintenance. Pinecone offers higher operational convenience at a cost, whereas pgvector provides lower costs with more control but demands technical expertise.
Pinecone vs pgvector — Side-by-side comparison on A Versus B
Fully managed cloud vector database with enterprise features and 99.95% uptime SLA.
Open-source PostgreSQL extension for vector similarity search within existing PostgreSQL databases.
Deployment Model. Setup & Infrastructure. Monthly Cost (1M vectors). Scalability. Query Latency (p99). Maintenance Required. Supported Vector Dimensions
Choose Pinecone if you prioritize ease of use, automatic scaling, and enterprise support for production applications where infrastructure management overhead is a concern. Choose pgvector if you have cost constraints, existing PostgreSQL infrastructure, or prefer open-source solutions with full control over your data and deployment.
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: Boolean operators, ranges, sparse-dense hybrid | 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: 50-100ms | pgvector: 100-500ms
Pinecone: 15-20 (account + API key setup) | pgvector: N/A
Pinecone: Not public (proprietary) | 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: 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: 10K dimensions | pgvector: N/A
Pinecone: Yes (sparse-dense vectors) | pgvector: N/A
Pinecone: Python, JavaScript, Go, Java, REST API | pgvector: N/A
Pinecone: <5 minutes | pgvector: 30-120 minutes
Pinecone: 99.95% | pgvector: Self-managed (varies)
Pinecone: Proprietary optimized (HNSW variant) | pgvector: HNSW, IVFFlat, Exact
Pinecone: Native, advanced filtering on metadata | pgvector: Limited (SQL WHERE clauses only)
Pinecone: None (fully managed) | pgvector: PostgreSQL instance (AWS RDS, self-hosted, etc.)
Pinecone: 5-10 minutes | pgvector: 45 minutes
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: Yes (full support)
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)
Pinecone: N/A | pgvector: Yes (full SQL support)
How to choose between Pinecone and pgvector
Pinecone vs pgvector — Structured Comparison Data
Machine-readable comparison dataset for Pinecone vs pgvector. Contains 128 attribute comparisons, 5 FAQ pairs.
Pinecone vs pgvector — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Pinecone vs pgvector.
Which is better for cost-sensitive projects?
pgvector is significantly more cost-effective, costing $10-50/month for infrastructure versus Pinecone's $150-300/month for comparable vector volumes. However, pgvector requires operational overhead and engineering time for maintenance, which can offset cost savings in small teams.
Can pgvector handle enterprise-scale deployments?
pgvector can handle up to ~100M vectors reliably in a single instance before performance degrades. Beyond that, you need to implement custom sharding strategies. Pinecone is purpose-built for billions of vectors with automatic sharding, making it the enterprise default.
Is pgvector suitable for production applications?
Yes, pgvector is production-ready and used by companies like OpenAI and Anthropic internally. However, you're responsible for high availability, backups, and disaster recovery. Pinecone includes these features automatically with a 99.99% SLA.
What are the main technical differences?
Pinecone uses a proprietary optimized HNSW variant optimized for cloud scale, supports up to 20,000 vector dimensions, and includes native sparse-dense hybrid search. pgvector offers HNSW and IVFFlat options, maxes out at ~2,000 dimensions, and relies on PostgreSQL's SQL engine for complex queries.
Can I migrate from pgvector to Pinecone later?
Yes, migration is straightforward: export vectors and metadata from PostgreSQL, transform to Pinecone's JSON format, and bulk ingest via their API. Most teams accomplish this in 1-2 days. The reverse (Pinecone to pgvector) is similarly simple.
How to Choose Between Pinecone and pgvector
Deployment Model: Pinecone has Fully managed cloud service (SaaS), while pgvector has Self-hosted PostgreSQL extension.
Setup & Infrastructure: Pinecone has Zero infrastructure needed, instant deployment, while pgvector has Requires PostgreSQL instance + manual configuration.
Monthly Cost (1M vectors): Pinecone has $150-300/month, while pgvector has $10-50/month (RDS) + operational overhead.
Scalability: Pinecone has Auto-scaling, handles billions of vectors, while pgvector has Limited by single PostgreSQL instance unless sharded.
Query Latency (p99): Pinecone has 50-100ms, while pgvector has 100-500ms (depending on index size).
Maintenance Required: Pinecone has Fully managed by Pinecone, while pgvector has User responsible for updates, backups, tuning.
Supported Vector Dimensions: Pinecone has Up to 20,000 dimensions, while pgvector has Up to 2,000 dimensions (practical limit).