While dedicated vector databases get the spotlight, pgvector offers compelling advantages for many production scenarios. Through building RAG systems and semantic search with pgvector, I’ve identified patterns that make PostgreSQL a serious contender for vector workloads. For comparison with dedicated solutions, see my pgvector vs dedicated vector DB comparison.

Why pgvector

pgvector extends PostgreSQL with vector similarity search, bringing unique advantages.

Unified Database: Store vectors alongside relational data. No separate vector database to manage. Transactions include both traditional and vector operations.

Familiar Tooling: Use existing PostgreSQL expertise. Same backup, monitoring, and management tools. No new infrastructure to learn.

ACID Guarantees: Vector operations participate in transactions. Consistent updates across relational and vector data.

Cost Efficiency: Often cheaper than dedicated vector databases at moderate scale. Leverage existing PostgreSQL infrastructure.

Mature Ecosystem: Connect with existing applications. Standard PostgreSQL drivers work. ORM integration available.

Installation and Setup

Getting pgvector running requires PostgreSQL extension installation.

Extension Installation: Install the pgvector extension. Most cloud PostgreSQL providers support it. Self-hosted requires building from source.

Enabling: Create extension in your database with CREATE EXTENSION vector. One-time setup per database.

Version Considerations: Newer pgvector versions offer better performance. Upgrade when possible. Check compatibility with PostgreSQL version.

Cloud Options: AWS RDS, Google Cloud SQL, Supabase, and others support pgvector. Managed options simplify operations.

Schema Design

Design schemas that combine vectors with relational data effectively.

Vector Column: Add vector columns to existing tables. Specify dimension explicitly. Dimension must match embedding model.

Related Data: Keep vectors with related data in same table. Document content, metadata, and embeddings together.

Normalization Decisions: Denormalize for query performance when needed. Join operations add latency to vector searches.

Index Planning: Plan indexes based on query patterns. Vector indexes and B-tree indexes work together.

For RAG architecture decisions, see my building production RAG systems guide.

Index Configuration

pgvector supports multiple index types with different trade-offs.

IVFFlat Index: Faster to build, good for moderate datasets. Requires choosing number of lists. More lists improve recall but slow queries.

HNSW Index: Better query performance, slower to build. Recommended for production workloads. Self-tuning with fewer parameters.

Index Selection: Use HNSW for production unless build time is critical. IVFFlat works for datasets that change frequently.

Probes and ef_search: Tune search parameters for recall vs latency trade-off. Higher values improve recall at performance cost.

Query Patterns

Write effective vector queries with pgvector.

Distance Operators: Use <-> for L2 distance, <=> for cosine distance, <#> for inner product. Match operator to how embeddings were trained.

Similarity Queries: Order by distance, limit results. Basic pattern for nearest neighbor search.

Combining with SQL: Join vector results with relational data. Filter before or after similarity search based on selectivity.

Hybrid Queries: Combine full-text search with vector similarity. PostgreSQL’s tsvector and pgvector work together.

Learn more about hybrid approaches in my hybrid search implementation guide.

Performance Optimization

Optimize pgvector for production workloads.

Work Memory: Increase work_mem for vector operations. Vector queries benefit from more memory.

Maintenance Work Memory: Increase for faster index builds. Critical when rebuilding indexes on large tables.

Parallel Query: Enable parallel query for better throughput. Vector scans parallelize well.

Connection Pooling: Use pgbouncer or similar. Vector queries can hold connections longer.

Embedding Integration

Connect embedding pipelines to pgvector.

Insert Workflow: Generate embeddings, insert with vector data. Batch inserts for efficiency.

Update Workflow: Re-embed when source content changes. Update vector column along with content.

Query Workflow: Embed query text, search with resulting vector. Same model for indexing and querying.

Batch Operations: Use COPY or batch inserts for bulk loading. Much faster than individual inserts.

Scaling Strategies

Scale pgvector as data and traffic grow.

Vertical Scaling: Larger instances handle more vectors. Memory is typically the constraint.

Read Replicas: Add replicas for read-heavy workloads. Vector queries can run on replicas.

Partitioning: Partition large tables by tenant or time. Queries can target specific partitions.

Index Partitioning: Separate indexes for partitions. Smaller indexes query faster.

For deployment patterns, see my AI deployment checklist.

Transaction Patterns

Leverage PostgreSQL’s transaction support.

Atomic Updates: Update content and embeddings in same transaction. No inconsistent states.

Batch Processing: Process documents in transaction batches. Commit periodically for large imports.

Rollback Support: Failed operations roll back cleanly. Includes vector operations.

Isolation Levels: Standard PostgreSQL isolation applies. Choose appropriate level for workload.

Monitoring and Maintenance

Monitor pgvector deployments effectively.

Query Analysis: Use EXPLAIN ANALYZE for vector queries. Verify index usage. Identify sequential scans.

Index Health: Monitor index size and fragmentation. Rebuild indexes when performance degrades.

Storage Monitoring: Track vector column storage size. Vectors consume significant space.

Query Logging: Log slow queries. Identify optimization opportunities.

Hybrid Search Implementation

Combine full-text and vector search in PostgreSQL.

Full-Text Indexes: Create GIN indexes on tsvector columns. Fast keyword matching.

Combined Scoring: Blend full-text rank with vector distance. Weight based on use case.

Query Structure: Search full-text first for filtering, then rank by vector similarity. Or combine scores directly.

Use Cases: Product search, documentation, support systems. Users search with both keywords and concepts.

Security Considerations

Secure pgvector deployments appropriately.

Row-Level Security: Apply RLS to tables with vectors. Tenant isolation at database level.

Column Permissions: Grant appropriate access to vector columns. Embeddings can reveal information about content.

Encryption: Use PostgreSQL encryption options. At-rest and in-transit encryption protect vector data.

Connection Security: Require SSL connections. Standard PostgreSQL security applies.

Migration Strategies

Migrate to or from pgvector effectively.

From Other Databases: Export embeddings, import to pgvector. Match dimensions and distance metrics.

Schema Migrations: Add vector columns to existing tables. Backfill embeddings for existing rows.

Index Building: Build indexes after data loading. Faster than incremental index updates.

Testing: Verify query results match expectations. Compare against previous system.

Limitations to Consider

Understand pgvector’s constraints.

Scale Limits: Performance degrades with very large datasets. Dedicated vector databases scale further.

Feature Depth: Fewer vector-specific features than dedicated solutions. Basic similarity search works well, advanced features limited.

Resource Competition: Vector operations compete with regular queries. Resource isolation requires planning.

Index Rebuild: Some operations require index rebuilds. Plan for maintenance windows.

Real-World Implementation

Here’s how these patterns combine:

A document search system stores documents with their embeddings in PostgreSQL. The schema keeps document content, metadata, and vector in one table. HNSW index enables fast similarity search.

Queries combine metadata filters with vector similarity. Filter by document type and date, then rank by semantic relevance.

Full-text search handles exact keyword matches. Hybrid scoring blends keyword and semantic relevance for final ranking.

Transactions ensure content and embedding updates are atomic. No orphaned vectors or stale embeddings.

This architecture handles millions of documents with acceptable latency while maintaining data consistency guarantees.

pgvector offers a pragmatic choice for teams already invested in PostgreSQL who want vector capabilities without operational complexity.

Ready to add vector search to PostgreSQL? Watch my implementation tutorials on YouTube for detailed walkthroughs, and join the AI Engineering community to learn alongside other builders.