MySQL vs PostgreSQL(2026)
MySQL is better for teams that need extremely battle-tested. PostgreSQL is the stronger choice if free and open source. MySQL is free and PostgreSQL is free.
Full feature breakdown, pricing details, and pros & cons below.
By Bikram NathLast updated
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MySQL
Proven relational database. Powers WordPress, Shopify, Facebook. Simpler than PostgreSQL for basic CRUD apps.
Visit MySQLPostgreSQL
Most admired database (Stack Overflow 2023-2025). ACID-compliant, JSONB, pgvector for AI, 35+ years of production hardening.
Visit PostgreSQLHow Do MySQL and PostgreSQL Compare on Features?
| Feature | MySQL | PostgreSQL |
|---|---|---|
| Pricing model | free | free |
| Starting price | Free | Free |
| ACID compliance | ✓ | ✓ |
| Replication | ✓ | — |
| InnoDB storage | ✓ | — |
| Full-text search | ✓ | ✓ |
| JSON support | ✓ | — |
| PlanetScale managed option | ✓ | — |
| JSONB storage | — | ✓ |
| pgvector extension | — | ✓ |
| Partitioning | — | ✓ |
| Row-level security | — | ✓ |
MySQL Pros and Cons vs PostgreSQL
MySQL
PostgreSQL
Deep dive: MySQL
When to choose MySQL
MySQL is the right choice when the team is building on a platform where MySQL is the standard (WordPress, Laravel, and most shared hosting providers), when the project is inheriting an existing MySQL database, or when the team needs maximum compatibility with legacy hosting infrastructure. MySQL has the broadest hosting support of any database — every shared hosting plan, cPanel installation, and managed database service (AWS RDS, DigitalOcean, Google Cloud SQL) supports MySQL. For teams building content management systems, e-commerce platforms, or any application with a WordPress dependency, MySQL is the path of least resistance. PlanetScale (now via Xata or direct) offers a serverless MySQL-compatible option with git-style branching for schema changes, which is genuinely innovative for teams that find database migrations painful. MySQL is a weaker choice for new greenfield projects without legacy constraints — PostgreSQL's JSONB, window functions, full-text search, and pgvector are all more capable than MySQL's equivalents. The Oracle ownership of MySQL creates licensing uncertainty that has driven many teams to MariaDB (a MySQL fork with a fully open-source license) or PostgreSQL. Teams that need strict ACID compliance across distributed transactions, advanced JSON querying, or vector search should prefer PostgreSQL.
Real-world use case
A development agency inherits a 10-year-old e-commerce platform built on WooCommerce running MySQL 5.7 on a cPanel shared host. Their task is to add a recommendation engine and scale the platform for a 10x traffic increase. MySQL's EXPLAIN tool reveals several missing indexes on the products and orders tables that, once added, reduce page load time by 60% without changing any code. The agency migrates to a dedicated AWS RDS MySQL 8.0 instance, which adds read replicas for reporting queries. The WooCommerce plugin ecosystem assumes MySQL, so the agency never seriously considers migrating to PostgreSQL — the risk of plugin incompatibility outweighs the benefits. The tradeoff: when the agency tries to add vector-based product recommendations using embeddings, MySQL 8.0 does not have a pgvector equivalent. They add a separate Supabase PostgreSQL database just for embeddings, creating a two-database architecture that adds operational complexity. If the platform were being built from scratch today, PostgreSQL would have handled both use cases in one database.
Hidden gotchas
MySQL's default transaction isolation level (REPEATABLE READ) differs from PostgreSQL's (READ COMMITTED), which means SQL that works correctly in one database can produce phantom reads or locking issues in the other. Teams migrating between MySQL and PostgreSQL frequently discover subtle query behavior differences only after production traffic exposes them. The GROUP BY behavior in MySQL has historically been more permissive than the SQL standard, allowing non-aggregated columns in SELECT that are not in GROUP BY. MySQL 5.7+ in ONLY_FULL_GROUP_BY mode enforces the standard, but disabling this mode is common in legacy codebases. Code written under the permissive behavior produces incorrect results (non-deterministic column selection) that may go unnoticed for years. MySQL's JSON support (added in 5.7) is functional but materially less capable than PostgreSQL's JSONB — there is no gin index equivalent for JSON, so queries against JSON fields perform sequential scans. The JSON_TABLE function for extracting JSON into relational rows is verbose and error-prone compared to PostgreSQL's json_to_recordset. The FULLTEXT index in MySQL requires a minimum word length (default 4 characters), which means searches for three-character or shorter terms return no results. This is a common bug in search features that is not obvious from the query results alone. MySQL replication uses a binary log format that, in ROW-based replication mode, can produce very large log files for UPDATE statements affecting many rows, causing replication lag on the replica during bulk updates.
Deep dive: PostgreSQL
When to choose PostgreSQL
PostgreSQL is the right default for any new application that needs a relational database. It has been the most admired database in Stack Overflow's developer survey for three consecutive years (2023-2025), and with good reason: it combines ACID-compliant relational storage with JSONB for semi-structured data, full-text search, pgvector for AI embeddings, and 35 years of production hardening. Choose PostgreSQL when the data model is relational or hybrid (structured schemas plus JSON documents), when the team needs advanced features like row-level security for multi-tenant applications, or when the deployment needs the pgvector extension for storing and searching ML embeddings without a separate vector database. Managed PostgreSQL via Supabase, Neon, or Railway reduces operational overhead to near zero. PostgreSQL is a poor fit when the data model is purely document-oriented with no relational structure — MongoDB's query model is more natural for deeply nested, schema-less documents. It is also not the best choice for write-heavy workloads at extreme scale (100,000+ writes per second) where distributed databases like CockroachDB or TiDB handle horizontal scaling more naturally. For teams that only need a simple key-value cache, Redis is faster and simpler. For projects where SQLite's zero-server overhead is sufficient (single-user tools, edge deployments), PostgreSQL is overkill.
Real-world use case
A multi-tenant SaaS company builds their application on PostgreSQL with Supabase. They use row-level security policies to enforce tenant isolation at the database layer — no application-level filtering needed, which eliminates an entire class of data-leak bugs. The pgvector extension stores embeddings for their AI-powered search feature without requiring a separate Pinecone subscription. JSONB columns store flexible per-customer configuration without schema migrations every time a customer needs a new setting. At 500 concurrent users, the Supabase Pro plan handles all traffic without configuration. The tradeoff: as the company grows to 50,000 users and adds complex reporting queries, they hire a database engineer to tune indexes, analyze query plans with EXPLAIN ANALYZE, and manage autovacuum settings. PostgreSQL's performance at scale requires expertise that managed services partially abstract but do not eliminate. A team that wanted to avoid all database operations would find Firebase or PlanetScale's serverless model more hands-off, at the cost of SQL expressiveness and pgvector.
Hidden gotchas
Autovacuum is the single most common source of performance surprises in PostgreSQL deployments that grow beyond a few GB. PostgreSQL uses MVCC (Multi-Version Concurrency Control), which means deleted and updated rows accumulate as 'dead tuples' rather than being freed immediately. Autovacuum reclaims this space, but at default settings it runs too infrequently for write-heavy tables, causing table bloat and slower sequential scans. Monitoring pg_stat_user_tables for n_dead_tup and tuning autovacuum per-table is a standard production requirement that most tutorials skip. The default connection limit in PostgreSQL is 100. At high concurrency, applications exhaust the connection pool, causing requests to queue or fail. PgBouncer as a connection pooler is the standard fix, but setting it up requires additional infrastructure and is often overlooked until the first production incident. The JSONB type compresses and decompresses JSON on every read and write. For columns that store large JSON payloads (more than 1KB) and are read frequently, the CPU overhead of JSONB deserialization is measurable. Text columns storing pre-serialized JSON are faster to read but lose query-ability. The gin index on JSONB columns enables fast queries inside JSON documents but has a higher write overhead than a standard B-tree index — tables with frequent JSONB updates and gin indexes see noticeable write slowdowns under load. The lock escalation behavior around ALTER TABLE can cause significant downtime on large tables: adding a column with a non-null default in older PostgreSQL versions locked the table for the duration of the migration.
Should You Use MySQL or PostgreSQL?
For most teams, MySQL is the better default: it offers extremely battle-tested and is free. Choose PostgreSQL instead if free and open source matters more than weaker json than postgresql. There is no universal winner — the right pick depends on your budget, team size, and whether you value extremely battle-tested or free and open source more.
Choose MySQL if…
- •Extremely battle-tested
- •Simpler setup than PostgreSQL
- •Largest hosting support
Choose PostgreSQL if…
- •Free and open source
- •JSONB + relational in one
- •pgvector for AI embeddings