PostgreSQL vs MySQL: which database is right for your project?

When to choose PostgreSQL vs MySQL

That's the generic picture. Your schema, query patterns, and team will tip this one way or the other. ↓

PostgreSQL vs MySQL: performance and query complexity

MySQL's InnoDB storage engine has a well-earned reputation for high-concurrency read performance. On simple SELECT queries, single-table lookups, primary key fetches, paginated list endpoints, MySQL consistently performs at or above PostgreSQL in benchmark conditions. Sysbench and TPC-C benchmarks regularly show MySQL with a 10–20% throughput advantage on point reads under heavy concurrency, particularly at connection counts above 100. For the archetype of a read-heavy web application serving cached content, MySQL's architecture delivers measurably lower p99 latencies.

PostgreSQL's query planner, however, is widely regarded as more sophisticated for complex analytical queries. When queries involve CTEs, window functions, lateral joins, or multi-table aggregations, PostgreSQL's cost-based planner typically produces better execution plans. The EXPLAIN ANALYZE output in PostgreSQL is more detailed and easier to interpret, making query optimization more tractable for engineering teams. For reporting workloads or dashboards running against the same database that serves application traffic, this matters considerably.

In practice, the performance difference is largely irrelevant for tables under 100,000 rows or applications handling fewer than a few hundred requests per second. At that scale, network latency, connection pooling configuration, and index design will dwarf any engine-level difference. The teams most affected by the performance distinction are those running mixed workloads, transactional CRUD alongside analytical aggregations, on a single database cluster, or those already operating at significant scale.

Your query complexity and access patterns will determine whether performance is a deciding factor. Answer 5 questions below for a recommendation grounded in your situation.

PostgreSQL vs MySQL: JSON, extensions, and modern features

PostgreSQL's JSONB type is one of the most underrated features in the relational database space. Unlike a simple text column storing serialized JSON, JSONB stores data in a binary format that supports GIN indexes on arbitrary keys, meaning you can index and query deeply nested JSON fields with the same performance characteristics as structured columns. This makes PostgreSQL a genuine alternative to MongoDB for hybrid workloads where some entities are structured and others are semi-structured, without the operational complexity of running two separate database systems.

The extension ecosystem amplifies PostgreSQL's advantage for modern application stacks considerably. pgvector adds native vector similarity search, making PostgreSQL a viable embedding store for AI-powered features without a separate vector database. TimescaleDB extends PostgreSQL into a high-performance time-series engine, enabling metrics, event data, and IoT workloads on the same cluster as your relational data. PostGIS adds a comprehensive geospatial layer used by governments, logistics companies, and mapping applications worldwide. These capabilities are not available in MySQL without external systems.

MySQL's JSON type, introduced in MySQL 5.7, is functional for storing and querying JSON data but comes with meaningful limitations. You cannot create GIN indexes on arbitrary JSON keys, only generated columns with explicit indexes, which require schema changes for each new query pattern. The operator syntax is more verbose, and JSON path support, while present, is less expressive than PostgreSQL's operators.

If your application stores JSON, uses AI features, or has geospatial requirements, these capabilities often tip the decision toward PostgreSQL. The questions below ask about your data model specifically.

PostgreSQL vs MySQL: managed cloud options in 2025

Supabase and Neon have become the developer-favorite PostgreSQL platforms in 2025. Supabase combines PostgreSQL with a real-time subscription layer, auto-generated REST and GraphQL APIs, and built-in authentication, making it particularly attractive for startups and indie developers who want a backend-as-a-service feel without sacrificing SQL power. Neon offers serverless PostgreSQL with branching, instant database clones for CI/CD pipelines, and a scale-to-zero model that eliminates idle costs. Both have excellent developer experience and are genuinely production-grade.

On AWS, RDS PostgreSQL and Aurora PostgreSQL are the workhorses for production deployments that need fine-grained control over instance sizing, VPC placement, and Multi-AZ failover. Aurora PostgreSQL offers up to 5x the throughput of standard RDS at the cost of a more complex pricing model. RDS MySQL and Aurora MySQL offer equivalent managed infrastructure for MySQL workloads, with Aurora MySQL providing strong read replica scaling for high-concurrency applications. On the MySQL side, PlanetScale, built on Vitess, offers a serverless MySQL-compatible database with a deployment model centered on schema branching and non-blocking schema changes, particularly attractive for teams that have experienced painful migration-related downtime.

CockroachDB is worth mentioning for teams that need globally distributed SQL: it uses the PostgreSQL wire protocol, so existing PostgreSQL clients and ORMs work without changes, but it adds multi-region active-active writes and automatic horizontal sharding. The cost and operational model is different from a single-region PostgreSQL cluster, but the PostgreSQL compatibility makes migration straightforward if you eventually need global distribution.

The good news: managed services abstract most of the operational differences between PostgreSQL and MySQL. The choice of managed platform is increasingly independent of the underlying database engine, but your choice of engine will constrain which platforms are available to you.

Common questions about PostgreSQL vs MySQL