The “should I use pgvector or a dedicated vector database” question comes up constantly. The answer isn’t about which is technically superior, it’s about understanding where your constraints actually lie. Through implementing both approaches in production, I’ve found that the right choice depends more on your existing infrastructure than on theoretical performance benchmarks.

Most teams already have PostgreSQL. Adding a new database type has real costs: operational complexity, network hops, team learning curves, and deployment pipelines. The question is whether those costs are justified by your requirements.

The pgvector Value Proposition

pgvector extends PostgreSQL with vector operations. Your relational data and vector data live in the same database, queryable together, using infrastructure you already operate.

This has significant advantages:

Transactional consistency. When you update a document and its embedding, both happen in one transaction. No eventual consistency between systems, no race conditions, no sync jobs.

Familiar operations. Your existing backup, monitoring, replication, and access control patterns work for vectors too. No new operational procedures to learn.

SQL power. Complex queries that combine vector similarity with relational filters, aggregations, and joins work naturally. One query does what might require multiple round-trips with separate databases.

For foundational understanding, my vector databases explained guide covers how vector search works regardless of implementation.

When pgvector Is Enough

pgvector handles many production use cases perfectly well:

Moderate Scale

Millions, not billions. pgvector with proper indexing handles single-digit millions of vectors efficiently. Most applications never exceed this scale.

Query frequency matters more than vector count. A database with 10 million vectors but 100 queries per minute is different from one with 1 million vectors and 10,000 queries per second. pgvector handles reasonable query loads on appropriate hardware.

Transactional Requirements

ACID matters for your use case. When vector updates must be atomic with relational updates, pgvector’s transactional model is an advantage, not a limitation.

Reference data alongside vectors. Your vectors reference rows in other tables. Joins and foreign keys provide consistency that separate systems can’t match.

Existing PostgreSQL Investment

You already run PostgreSQL. Adding an extension is simpler than adding a new database. Your DBA knows how to tune it, your monitoring works, your backup procedures apply.

Team expertise. SQL is a known skill. Training engineers on PostgreSQL patterns is easier than introducing a new query language.

Simpler Architecture

One less system. Every external service is a potential failure point, latency source, and operational burden. If pgvector meets your needs, you’ve eliminated a category of problems.

Deployment simplicity. One database to deploy, configure, secure, and maintain. Your CI/CD pipeline stays simpler.

When Dedicated Databases Win

Dedicated vector databases like Pinecone, Weaviate, or Milvus have advantages that matter for specific use cases:

Extreme Scale

Billions of vectors. Dedicated databases are architected for massive scale. Distributed indexing, sharding, and query routing are first-class concerns.

High concurrency. When thousands of users query simultaneously, purpose-built infrastructure handles the load more efficiently.

Advanced Features

Hybrid search optimization. Dedicated databases often have optimized sparse-dense vector support, making hybrid search more efficient.

Specialized indexing. GPU-accelerated indexing, advanced ANN algorithms, and workload-specific optimizations that PostgreSQL extensions can’t match.

Real-time indexing. Some dedicated databases optimize for immediate vector availability after ingestion, while pgvector may have indexing delays.

Managed Operations

Zero infrastructure concern. Services like Pinecone eliminate all operational overhead. For teams without database expertise, this has real value.

Global distribution. Managed services handle multi-region deployment, replication, and traffic routing that would require significant effort with self-hosted PostgreSQL.

Performance Comparison

Real-world performance depends on many factors. General patterns:

Query Latency

pgvector with HNSW indexing achieves sub-100ms queries on millions of vectors with appropriate hardware. For most applications, this is sufficient.

Dedicated databases can achieve lower latency at higher scales, especially with distributed architectures and specialized optimizations.

Throughput

pgvector shares resources with your relational queries. Heavy vector workloads can affect other database operations.

Dedicated databases isolate vector workloads, providing more predictable performance for both systems.

Index Build Time

pgvector HNSW indexing happens on your database server. Large indexes compete with production queries.

Dedicated databases often have distributed or background indexing that minimizes impact on query performance.

Memory Usage

pgvector indexes consume PostgreSQL’s shared memory. Balancing vector and relational workloads requires careful tuning.

Dedicated databases manage memory specifically for vector operations without shared-resource constraints.

Feature Comparison

Feature	pgvector	Dedicated Vector DB
ACID transactions	Yes	Typically no
SQL integration	Native	Via connectors
HNSW index	Yes	Yes (usually)
IVF index	Yes	Varies
GPU acceleration	No	Some (Milvus)
Hybrid search	Via SQL	Native support
Managed cloud	Via managed PG	Yes
Multi-tenancy	Schema/row-level	Varies
Horizontal scale	With effort	Native

Architecture Patterns

pgvector Monolith

All your data in one place:

Application → PostgreSQL (relational + vectors)

Advantages:

• Simplest deployment
• Transactional consistency
• Familiar tooling

Best for: Applications where vectors are tightly coupled with relational data.

Hybrid Architecture

Relational data in PostgreSQL, vectors in dedicated store:

Application → PostgreSQL (relational)
            → Pinecone (vectors)

Advantages:

• Optimized for each workload
• Independent scaling

Challenges:

• Consistency between systems
• More complex operations

Best for: Scale beyond pgvector’s capabilities or when managed services reduce operational burden.

Read Replica Pattern

PostgreSQL for writes, dedicated database for read scaling:

Application → PostgreSQL (writes, consistency)
            → Qdrant (read replicas, scale)

Advantages:

• PostgreSQL as source of truth
• Scale reads independently

Challenges:

• Replication lag
• Sync infrastructure

Best for: High read throughput with moderate write loads.

For more architectural patterns, see my RAG architecture patterns guide.

Cost Analysis

pgvector Costs

• Infrastructure: Your existing PostgreSQL instances
• Scaling: Larger instances or read replicas
• Operations: Your existing DBA capacity

For moderate scale, pgvector often costs less because it uses existing infrastructure.

Dedicated Database Costs

• Service fees: Managed database pricing
• Infrastructure: Self-hosted compute
• Operations: New expertise required

Managed services trade infrastructure costs for service fees and reduced operational burden.

Total Cost of Ownership

Consider:

• Engineering time to operate and maintain
• Learning curve for new technology
• Integration complexity
• Failure handling and monitoring

Sometimes the “cheaper” option costs more in engineering time. See my cost-effective AI strategies guide for broader cost optimization.

Migration Paths

From pgvector to Dedicated

If you outgrow pgvector:

1. Export vectors using SQL queries
2. Transform to target format
3. Import to dedicated database
4. Update application to dual-write during migration
5. Switch reads to new database
6. Remove pgvector tables

The main work is adapting query logic to the new database’s API.

From Dedicated to pgvector

If you want to consolidate:

1. Export from dedicated database
2. Load into PostgreSQL tables
3. Build HNSW index
4. Update application queries
5. Retire dedicated database

This path is less common but sometimes valuable for simplification.

Decision Framework

Start with pgvector if:

1. You already run PostgreSQL
2. Scale is under 5 million vectors
3. Transactional consistency matters
4. Team has SQL expertise
5. You want to minimize infrastructure complexity

Start with Dedicated if:

1. Scale exceeds PostgreSQL’s comfortable range
2. Managed operations are valuable
3. Advanced vector features are required
4. You’re starting greenfield without PostgreSQL
5. High concurrency is a primary requirement

Evaluate Both if:

1. You’re uncertain about scale requirements
2. Cost optimization is critical
3. You want to defer the decision

Implementation Tips

Optimizing pgvector

If you choose pgvector:

Use HNSW indexes. They provide better query performance than IVF for most workloads.

Tune maintenance_work_mem. Index building benefits from larger memory allocation.

Consider partitioning. For large tables, time-based or tenant-based partitioning helps query performance.

Monitor carefully. Track query latency and index build times alongside your regular PostgreSQL metrics.

Designing for Migration

Regardless of choice:

Abstract the interface. Use a repository pattern that hides the database choice from application logic.

Store raw data. Keep source documents alongside vectors. Re-embedding is easier than reverse-engineering vectors.

Plan for change. Your first choice may not be your final choice. Design for migration from the start.

Beyond the Database Decision

The vector database is one component. What matters more:

• Embedding model selection affects retrieval quality
• Chunking strategy determines what gets retrieved
• Query patterns influence system design

Check out my production RAG systems guide for the full picture, or the hybrid database solutions guide for combining different data stores.

To see these concepts in action, watch the full video tutorial on YouTube.

Ready to implement RAG systems with hands-on guidance? Join the AI Engineering community where engineers share their experiences across different database choices.