MySQL vs PostgreSQL in 2026 — 7 key differences you should know before choosing

Choosing between MySQL and PostgreSQL isn't straightforward. Both are mature, production-ready databases used by companies of all sizes. But they solve problems differently and each has strengths that matter depending on your use case. This article breaks down the actual differences that affect day-to-day development and operations in 2026.

The comparison covers:

• SQL standards compliance and data integrity
• JSON and document handling
• Replication approaches
• Licensing and ownership
• Performance characteristics
• Extension ecosystems
• Community and tooling

1. SQL standards compliance and data integrity

PostgreSQL follows the SQL standard more strictly than MySQL. This matters more than it might seem at first. When PostgreSQL says a transaction is ACID compliant, it means it. MySQL has improved significantly over the years, but some default behaviors still surprise developers coming from other databases.

PostgreSQL enforces data types strictly. If you try to insert a string into an integer column, it fails. MySQL historically performed silent type conversions, which could lead to data corruption. MySQL 8.0+ has strict mode enabled by default, but many existing installations still run with older, more permissive settings.

Feature	PostgreSQL	MySQL
Default SQL mode	Strict, standards-compliant	Strict in 8.0+, permissive in older versions
Silent type conversions	Never	Depends on SQL mode
CHECK constraints	Fully enforced	Enforced since MySQL 8.0.16
Foreign key enforcement	Always with supported storage engines	Only with InnoDB
TRUNCATE in transactions	Transactional	Not transactional

For applications where data integrity is critical, PostgreSQL provides stronger guarantees out of the box. MySQL can be configured to behave similarly, but you need to verify your settings and understand which features require InnoDB specifically.

2. JSON and document handling

Both databases support JSON, but they approach it differently. PostgreSQL has native JSONB type that stores JSON in a binary format with full indexing support. MySQL added JSON support in version 5.7 and has improved it since, but the implementation has limitations.

PostgreSQL's JSONB allows you to create indexes on specific JSON paths, query nested structures efficiently and use JSON in complex queries alongside relational data. You can also use operators like @> (contains) and ? (key exists) that make JSON queries concise and readable.

-- PostgreSQL: Create index on JSON field
CREATE INDEX idx_users_metadata_country ON users USING GIN ((metadata->'country'));

-- PostgreSQL: Query with containment operator
SELECT * FROM users WHERE metadata @> '{"role": "admin"}';

MySQL's JSON functions work but feel more verbose. Indexing JSON in MySQL requires generated columns, which adds complexity:

-- MySQL: Requires generated column for indexing
ALTER TABLE users ADD COLUMN country VARCHAR(255) 
  GENERATED ALWAYS AS (JSON_UNQUOTE(metadata->'$.country')) STORED;
CREATE INDEX idx_country ON users(country);

If your application heavily uses semi-structured data or you're building something that mixes relational and document patterns, PostgreSQL handles this more elegantly. MySQL works fine for basic JSON storage and retrieval, but advanced querying gets awkward.

3. Replication and high availability

Both databases support replication, but they use fundamentally different approaches. Understanding these differences matters when planning for high availability and read scaling.

MySQL uses binary log replication. The primary server writes changes to a binary log, and replicas read from it. This approach is well-understood and has been battle-tested for decades. MySQL also supports Group Replication for multi-primary setups, though it comes with trade-offs around consistency.

PostgreSQL uses Write-Ahead Log (WAL) streaming replication. It's conceptually similar but operates at the storage level rather than the query level. PostgreSQL's logical replication (added in version 10) allows selective table replication and cross-version replication.

• MySQL binary replication is simpler to set up initially
• PostgreSQL logical replication offers more flexibility for complex topologies
• MySQL Group Replication provides multi-primary but with consistency caveats
• PostgreSQL synchronous replication guarantees zero data loss at the cost of latency

For most applications, both approaches work well. MySQL's tooling ecosystem for replication is more mature, with tools like Orchestrator and ProxySQL being widely used. PostgreSQL's tooling has caught up significantly with Patroni, pgBouncer and others.

Backup strategies differ too. Both support logical and physical backups, but the tools and workflows vary. For automated database backups with scheduling, encryption and multiple storage destinations, MySQL backup tools like Databasus handle both MySQL and PostgreSQL, providing a unified approach regardless of which database you choose.

4. Licensing and corporate ownership

This is often overlooked but increasingly important. MySQL is owned by Oracle. PostgreSQL is a community project with no single corporate owner.

MySQL uses a dual licensing model. The Community Edition is GPL-licensed, which means if you modify MySQL and distribute it, you must release your changes. Oracle also sells commercial licenses for those who want to avoid GPL obligations. Some MySQL forks exist (MariaDB, Percona Server) partly because of licensing concerns.

PostgreSQL uses the PostgreSQL License, which is similar to BSD/MIT. You can do essentially anything with it, including building proprietary products without releasing source code. There's no commercial entity that could change the terms or create uncertainty about the project's direction.

Aspect	PostgreSQL	MySQL
License	PostgreSQL License (BSD-like)	GPL + Commercial
Owner	Community project	Oracle Corporation
Major forks	None needed	MariaDB, Percona Server
Feature restrictions	None	Some features in Enterprise only

For companies evaluating long-term risk, PostgreSQL's licensing and governance model provides more predictability. Oracle has added features to MySQL Enterprise that aren't in the Community Edition, and this trend could continue.

5. Performance characteristics

Performance comparisons are tricky because results depend heavily on workload type, hardware configuration and tuning. Both databases can handle millions of transactions per day when properly configured. But their performance profiles differ in important ways.

PostgreSQL historically performed better on complex queries with many joins, subqueries and analytical operations. Its query planner is sophisticated and handles complicated query patterns well. The cost-based optimizer has decades of refinement.

MySQL traditionally excelled at simple read-heavy workloads with straightforward queries. If your application does mostly primary key lookups and simple filters, MySQL can be extremely fast. The InnoDB storage engine is highly optimized for these patterns.

In 2026, both databases have narrowed these gaps. PostgreSQL 17 and 18 have improved simple query performance. MySQL 8.x has better handling of complex queries than earlier versions. The differences are less dramatic than they were five years ago.

• Read-heavy OLTP workloads: Both perform well, slight edge to MySQL
• Complex analytical queries: PostgreSQL generally faster
• Write-heavy workloads: Depends on transaction patterns and indexing
• Mixed workloads: PostgreSQL handles variety better

The real performance factors are usually configuration, indexing and query design rather than the database engine choice. Both require tuning for production workloads. Neither works optimally with default settings.

6. Extension ecosystem

PostgreSQL's extension system is one of its biggest advantages. Extensions can add new data types, index types, functions and even modify core behavior. The ecosystem is rich and actively maintained.

Popular PostgreSQL extensions include:

• PostGIS — Spatial and geographic data support
• pg_stat_statements — Query performance monitoring
• TimescaleDB — Time-series data optimization
• Citus — Distributed PostgreSQL for horizontal scaling
• pgvector — Vector similarity search for AI applications

MySQL doesn't have an equivalent extension system. Feature additions require either MySQL updates or forking the source code. Some functionality exists through plugins (like authentication plugins) but the scope is limited compared to PostgreSQL.

This extensibility matters more than it might seem. If you need geographic queries, PostgreSQL with PostGIS is significantly better than trying to work around MySQL's limited spatial support. If you're building AI features that need vector search, pgvector is a mature solution while MySQL has no comparable option.

The extension ecosystem also means PostgreSQL can adapt to new use cases without waiting for core team priorities. When vector databases became important for AI applications, the community built pgvector. MySQL users had to wait for Oracle's roadmap or use a separate database.

7. Community and tooling

Both databases have active communities, but they feel different. MySQL's community is larger in raw numbers but fragmented across MySQL, MariaDB and Percona variants. PostgreSQL's community is more unified around a single codebase.

PostgreSQL's development is transparent. Mailing lists are public, design discussions happen in the open and anyone can propose patches. The code review process is rigorous and the community has high standards for what gets merged. Release cycles are predictable: one major version per year.

MySQL development is primarily done inside Oracle. While the code is open source, the roadmap and priorities are set internally. External contributions exist but aren't as central to the project's direction.

Tool availability:

• GUI clients: Both have excellent options (pgAdmin, DBeaver, TablePlus for PostgreSQL; MySQL Workbench, DBeaver for MySQL)
• ORMs and drivers: Comprehensive support for both in all major languages
• Cloud offerings: Both available as managed services (RDS, Cloud SQL, Azure Database)
• Monitoring tools: Mature options for both

For backup and disaster recovery, the tooling landscape varies. Both support native dump tools (pg_dump, mysqldump), but their capabilities differ. For automated backup management, Databasus is an industry standard supporting both PostgreSQL and MySQL with unified scheduling, encryption and storage options.

Making the choice

There's no universally correct answer. Both databases power successful applications at every scale. But certain patterns emerge when you look at typical use cases.

Choose PostgreSQL when:

• You need advanced data types (JSONB, arrays, custom types)
• Your queries are complex with many joins and subqueries
• Data integrity is non-negotiable
• You want extensibility (PostGIS, pgvector, TimescaleDB)
• Licensing simplicity matters to your organization

Choose MySQL when:

• Your workload is primarily simple CRUD operations
• You need maximum compatibility with existing tools and hosting
• Your team already has MySQL expertise
• You're building something that will run on shared hosting

Neither choice is wrong for most applications. The differences matter most at the extremes: very complex analytical workloads, very high write volumes or specialized data types. For a typical web application, both will work fine with proper setup.

What actually matters is understanding whichever database you choose deeply enough to configure it properly, design your schema correctly and troubleshoot problems when they occur. A well-tuned MySQL installation will outperform a misconfigured PostgreSQL one, and vice versa.

Start with whichever one you or your team knows better. Switch if you hit real limitations, not theoretical ones. Both databases have been solving real problems for decades and both will continue improving.