MySQL vs PostgreSQL in 2026: Performance, Features, and When to Use Each

The State of Play in 2026

MySQL and PostgreSQL are both mature, production-grade relational databases. MySQL powers a huge share of the web through WordPress, Shopify, and countless PHP applications. PostgreSQL has been the fastest-growing database for several years running and is now the default choice on most cloud platforms.

The gap between them has narrowed significantly. MySQL 9.x brought features that PostgreSQL had for years, while PostgreSQL continued to improve its ease of use. But meaningful differences remain, and they matter for your choice.

This guide focuses on practical differences that affect real projects, not theoretical benchmarks. For the foundational comparison of syntax and features, see our MySQL vs PostgreSQL original guide.

Key Syntax Differences

The most immediate difference you encounter is syntax. Here are the conversions you will need most often:

-- AUTO_INCREMENT vs IDENTITY
-- MySQL
CREATE TABLE users (
    id INT AUTO_INCREMENT PRIMARY KEY,
    name VARCHAR(100)
);

-- PostgreSQL
CREATE TABLE users (
    id INT GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
    name VARCHAR(100)
);

-- String concatenation
-- MySQL: GROUP_CONCAT
SELECT department, GROUP_CONCAT(name ORDER BY name SEPARATOR ', ')
FROM employees GROUP BY department;

-- PostgreSQL: STRING_AGG
SELECT department, STRING_AGG(name, ', ' ORDER BY name)
FROM employees GROUP BY department;

-- NULL handling
-- MySQL: IFNULL
SELECT IFNULL(phone, 'N/A') FROM users;

-- PostgreSQL: COALESCE (works in both, recommended)
SELECT COALESCE(phone, 'N/A') FROM users;

-- Date formatting
-- MySQL
SELECT DATE_FORMAT(created_at, '%Y-%m-%d %H:%i') FROM orders;

-- PostgreSQL
SELECT TO_CHAR(created_at, 'YYYY-MM-DD HH24:MI') FROM orders;

-- Upsert
-- MySQL
INSERT INTO users (email, name) VALUES ('a@b.com', 'Alice')
ON DUPLICATE KEY UPDATE name = VALUES(name);

-- PostgreSQL
INSERT INTO users (email, name) VALUES ('a@b.com', 'Alice')
ON CONFLICT (email) DO UPDATE SET name = EXCLUDED.name;

For a complete conversion reference, see our MySQL to PostgreSQL converter.

JSON Handling: The Biggest Feature Gap

Both databases support JSON, but PostgreSQL's implementation is substantially more powerful.

-- PostgreSQL: JSONB with indexing and operators
CREATE TABLE events (
    id SERIAL PRIMARY KEY,
    data JSONB
);

-- GIN index for fast JSON queries
CREATE INDEX idx_events_data ON events USING gin(data);

-- Query nested JSON with operators
SELECT * FROM events
WHERE data @> '{"type": "purchase", "amount": 99.99}';

-- Extract and aggregate JSON fields
SELECT
    data->>'type' AS event_type,
    AVG((data->>'amount')::numeric) AS avg_amount
FROM events
GROUP BY data->>'type';

-- MySQL: JSON with functional indexes
CREATE TABLE events (
    id INT AUTO_INCREMENT PRIMARY KEY,
    data JSON
);

-- Functional index (MySQL 8.0+)
ALTER TABLE events ADD COLUMN event_type VARCHAR(50)
    GENERATED ALWAYS AS (JSON_UNQUOTE(data->'$.type')) STORED;
CREATE INDEX idx_event_type ON events(event_type);

-- Query JSON
SELECT * FROM events
WHERE JSON_EXTRACT(data, '$.type') = 'purchase';

-- The ->> shorthand works in MySQL 8.0+
SELECT data->>'$.type' AS event_type
FROM events;

PostgreSQL advantages:

• JSONB is stored in a decomposed binary format, faster for reads and indexing
• GIN indexes support containment (@>), existence (?), and path queries out of the box
• Rich operator set: ->, ->>, #>, @>, ?, ?|, ?&
• jsonb_path_query for SQL/JSON path expressions

MySQL improvements: MySQL has improved JSON support significantly, but it still stores JSON as text internally and lacks the depth of PostgreSQL's JSONB operators and indexing.

Performance: Where Each Wins

MySQL wins: simple read-heavy workloads

For basic SELECT queries with simple WHERE clauses on indexed columns, MySQL's simpler query planner produces results marginally faster. This matters most for high-throughput web applications serving thousands of simple queries per second.

-- MySQL is marginally faster for simple lookups
SELECT name, email FROM users WHERE id = 42;
SELECT * FROM products WHERE category = 'electronics' LIMIT 20;

PostgreSQL wins: complex analytical queries

PostgreSQL's cost-based optimizer handles complex queries much better. CTEs, window functions, lateral joins, and multi-way joins with subqueries all benefit from PostgreSQL's more sophisticated planning.

-- PostgreSQL excels at complex analytics
WITH monthly_revenue AS (
    SELECT
        DATE_TRUNC('month', order_date) AS month,
        SUM(amount) AS revenue
    FROM orders
    GROUP BY 1
)
SELECT
    month,
    revenue,
    LAG(revenue) OVER (ORDER BY month) AS prev_month,
    ROUND((revenue - LAG(revenue) OVER (ORDER BY month))
        / LAG(revenue) OVER (ORDER BY month) * 100, 1) AS growth_pct
FROM monthly_revenue;

Both: proper indexing matters more

For 95% of applications, the choice of database matters far less than proper indexing, query design, and connection pooling. A well-indexed MySQL database will outperform a poorly-indexed PostgreSQL database and vice versa. See our SQL indexing guide for best practices that apply to both.

Advanced Data Types

PostgreSQL has a significant advantage in native data types:

-- PostgreSQL: arrays
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    name TEXT,
    tags TEXT[]
);
INSERT INTO users (name, tags) VALUES ('Alice', ARRAY['admin', 'developer']);
SELECT * FROM users WHERE 'admin' = ANY(tags);

-- PostgreSQL: ranges
CREATE TABLE reservations (
    id SERIAL PRIMARY KEY,
    room_id INT,
    during TSRANGE
);
INSERT INTO reservations (room_id, during)
VALUES (1, '[2026-03-23 09:00, 2026-03-23 17:00)');
-- Find overlapping reservations
SELECT * FROM reservations
WHERE during && '[2026-03-23 14:00, 2026-03-23 16:00)';

-- PostgreSQL: ENUM types
CREATE TYPE mood AS ENUM ('happy', 'sad', 'neutral');

-- PostgreSQL: network types
CREATE TABLE servers (ip INET, mac MACADDR);
SELECT * FROM servers WHERE ip << '192.168.1.0/24';

MySQL does not have native support for arrays, ranges, network types, or custom enum types (MySQL's ENUM is a column-level definition, not a reusable type). You can work around this with JSON columns or junction tables, but the PostgreSQL approach is cleaner and faster.

Replication and Scaling

MySQL replication

-- MySQL: built-in replication options
-- 1. Async replication (default, most common)
-- 2. Semi-sync replication (waits for at least one replica)
-- 3. Group Replication (multi-primary, automatic failover)
-- 4. InnoDB Cluster (Group Replication + MySQL Shell + Router)

-- Check replication status
SHOW REPLICA STATUS\G

PostgreSQL replication

-- PostgreSQL: streaming replication options
-- 1. Async streaming replication (default)
-- 2. Synchronous replication (guaranteed consistency)
-- 3. Logical replication (selective table replication)

-- Check replication status
SELECT * FROM pg_stat_replication;

MySQL advantage: InnoDB Cluster provides a more turnkey clustering solution. Group Replication handles automatic failover with less configuration.

PostgreSQL advantage: Logical replication is more flexible (replicate specific tables, cross-version replication). Patroni and Citus provide production-grade HA and horizontal scaling. Neon and Supabase offer serverless PostgreSQL with automatic scaling.

Cloud Ecosystem in 2026

Both databases are first-class citizens on all major clouds:

Cloud	MySQL	PostgreSQL
AWS	RDS MySQL, Aurora MySQL	RDS PostgreSQL, Aurora PostgreSQL
Google Cloud	Cloud SQL MySQL, AlloyDB	Cloud SQL PostgreSQL, AlloyDB
Azure	Azure Database for MySQL	Azure Database for PostgreSQL
Serverless	PlanetScale	Neon, Supabase
AI Integration	Limited	pgvector, pgml, AlloyDB AI

PostgreSQL has a clear edge in AI/ML integration. The pgvector extension enables vector similarity search for AI embeddings directly in your database. AlloyDB combines PostgreSQL with Google's AI infrastructure. MySQL has no equivalent native vector search capability.

AI and Vector Search

One of the biggest differentiators in 2026 is vector search for AI applications:

-- PostgreSQL with pgvector
CREATE EXTENSION vector;

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(1536)  -- OpenAI embedding dimension
);

-- Create an index for fast similarity search
CREATE INDEX idx_docs_embedding ON documents
USING ivfflat (embedding vector_cosine_ops);

-- Find similar documents
SELECT content, embedding <=> '[0.1, 0.2, ...]'::vector AS distance
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 5;

MySQL does not have a native vector type or similarity search extension. If you are building AI-powered applications with retrieval-augmented generation (RAG), semantic search, or recommendation engines, PostgreSQL with pgvector is the clear choice.

Decision Framework: Which Should You Choose?

Choose MySQL when:

• You are building a WordPress site or using MySQL-specific CMS/frameworks
• Your workload is primarily simple reads with straightforward schemas
• Your team has deep MySQL expertise and established tooling
• You need PlanetScale's serverless MySQL branching workflow
• You are maintaining an existing MySQL application that works well

Choose PostgreSQL when:

• You are starting a new project with no legacy constraints
• You need advanced data types (JSONB, arrays, ranges, vectors)
• Your queries involve complex analytics, CTEs, and window functions
• You are building AI/ML features that need vector similarity search
• You want the broadest extension ecosystem (PostGIS, pgvector, TimescaleDB)
• You value standards compliance and data integrity

Either works well for: Standard CRUD web applications, REST APIs, e-commerce, SaaS products, and most business applications. At this level, developer experience and team expertise matter more than database differences.

Write SQL for both MySQL and PostgreSQL with AI. AI2SQL generates dialect-specific SQL for 8+ databases, handling syntax differences automatically.

Frequently Asked Questions

Is PostgreSQL better than MySQL in 2026?

Neither is universally better. PostgreSQL excels at complex queries, advanced data types (JSONB, arrays, ranges), extensibility, and standards compliance. MySQL excels at simple read-heavy workloads, ease of setup, and has a massive ecosystem. PostgreSQL has been growing faster and is the preferred choice for new projects in 2026, but MySQL remains dominant in existing web applications and WordPress sites.

Which is faster: MySQL or PostgreSQL?

For simple read queries (SELECT with basic WHERE), MySQL is slightly faster due to its simpler query planner. For complex queries involving CTEs, window functions, subqueries, and JSON operations, PostgreSQL is significantly faster due to its more sophisticated optimizer. In practice, the performance difference is negligible for most applications. Proper indexing and query design matter far more than the database engine choice.

Can I switch from MySQL to PostgreSQL?

Yes, but it requires work. The main changes are: AUTO_INCREMENT becomes SERIAL or GENERATED ALWAYS AS IDENTITY, backtick quoting becomes double quotes, IFNULL becomes COALESCE, GROUP_CONCAT becomes STRING_AGG, LIMIT syntax is the same, and date functions differ. Tools like pgloader can automate the data migration, and AI2SQL can help convert your queries between dialects.

Should I use MySQL or PostgreSQL for a new project in 2026?

For most new projects in 2026, PostgreSQL is the recommended choice. It has better JSON support (JSONB), native array and range types, superior full-text search, better concurrency with MVCC, and is the default on major cloud platforms. Choose MySQL if you are building a WordPress site, need compatibility with MySQL-specific tools, or your team has deep MySQL expertise.

Can AI help me write SQL for both MySQL and PostgreSQL?

Yes. AI2SQL supports both MySQL and PostgreSQL (plus SQL Server, SQLite, Oracle, and more). You describe what you need in plain English, select your target database, and AI2SQL generates dialect-specific SQL. It handles the syntax differences automatically, so you get correct MySQL or PostgreSQL syntax without memorizing the differences.