MySQL vs PostgreSQL - Which Database Should You Choose in 2025?

Choosing a relational database management system (RDBMS) is one of the most fundamental architectural decisions in any project. MySQL and PostgreSQL are the two most popular open-source relational systems, yet they differ significantly in philosophy, capabilities, and ideal use cases. In this comprehensive comparison, we analyze both systems in terms of SQL standards compliance, data types, performance, extensibility, and ecosystem to help you make an informed decision.

History and Philosophy#

MySQL - Simplicity and Speed#

MySQL was created in 1995 by Michael Widenius and David Axmark at the Swedish company MySQL AB. From the very beginning, the priority was read speed and ease of use. MySQL quickly became the database of the internet - powering the Web 2.0 boom and serving as the cornerstone of the legendary LAMP stack (Linux, Apache, MySQL, PHP).

In 2008, Sun Microsystems acquired MySQL AB, and two years later Oracle Corporation acquired Sun. This raised concerns in the open-source community, ultimately leading to the creation of the MariaDB fork led by MySQL's original creator. Today, MySQL is developed under Oracle's umbrella with dual licensing - GPL for the community edition and commercial licensing for enterprises.

MySQL's philosophy: "Make it fast and simple." MySQL favors performance over full standards compliance, making it an ideal choice for web applications requiring fast reads.

PostgreSQL - Compliance and Extensibility#

PostgreSQL traces its roots back to 1986, when Professor Michael Stonebraker at the University of California, Berkeley, started the POSTGRES project as a successor to the Ingres system. The project became open source in 1996 under the name PostgreSQL, emphasizing its SQL support.

PostgreSQL is developed by the PostgreSQL Global Development Group - a distributed team of volunteers and sponsors. There is no single corporate owner, guaranteeing the project's independence. The PostgreSQL License (similar to MIT/BSD) is one of the most permissive in the database world.

PostgreSQL's philosophy: "Do it right." PostgreSQL prioritizes SQL standards compliance, data integrity, and extensibility. Often called "the world's most advanced open-source database," it has earned a reputation over the years as a system capable of replacing commercial databases like Oracle DB.

SQL Standards Compliance#

PostgreSQL is widely recognized as the most SQL-standards-compliant open-source system. It implements a vast portion of the SQL:2016 standard (and later), including:

• Common Table Expressions (CTE) - recursive and non-recursive
• Window Functions - full support with PARTITION BY, ORDER BY, ROWS/RANGE
• LATERAL JOIN - correlated subqueries in the FROM clause
• MERGE (since PostgreSQL 15) - standard UPSERT operation
• JSON/SQL Path - compliant with the SQL/JSON standard

MySQL implements many of these features, but historically with delays and sometimes with deviations from the standard:

-- PostgreSQL - full CTE support with materialized/not materialized control
WITH RECURSIVE category_tree AS (
    SELECT id, name, parent_id, 1 AS depth
    FROM categories
    WHERE parent_id IS NULL
    UNION ALL
    SELECT c.id, c.name, c.parent_id, ct.depth + 1
    FROM categories c
    JOIN category_tree ct ON c.parent_id = ct.id
)
SELECT * FROM category_tree ORDER BY depth, name;

-- MySQL - CTEs available since version 8.0 (2018)
-- Identical syntax, but no materialization control
WITH RECURSIVE category_tree AS (
    SELECT id, name, parent_id, 1 AS depth
    FROM categories
    WHERE parent_id IS NULL
    UNION ALL
    SELECT c.id, c.name, c.parent_id, ct.depth + 1
    FROM categories c
    JOIN category_tree ct ON c.parent_id = ct.id
)
SELECT * FROM category_tree ORDER BY depth, name;

MySQL historically had issues with non-standard behavior - for example, the default ONLY_FULL_GROUP_BY mode was disabled before version 5.7, allowing GROUP BY queries that violate the SQL standard. PostgreSQL has always enforced correctness.

Data Types - Detailed Comparison#

One of the biggest differences between MySQL and PostgreSQL is the richness of data types. PostgreSQL offers significantly more native types:

| Category | MySQL | PostgreSQL | |----------|-------|------------| | Integers | TINYINT, SMALLINT, MEDIUMINT, INT, BIGINT | SMALLINT, INTEGER, BIGINT | | Floating-point | FLOAT, DOUBLE, DECIMAL | REAL, DOUBLE PRECISION, NUMERIC | | Text | CHAR, VARCHAR, TEXT, BLOB | CHAR, VARCHAR, TEXT | | Date/Time | DATE, TIME, DATETIME, TIMESTAMP, YEAR | DATE, TIME, TIMESTAMP, INTERVAL, TIMESTAMPTZ | | JSON | JSON (since 5.7) | JSON, JSONB (binary, indexable) | | UUID | VARCHAR(36) or BINARY(16) | UUID (native) | | Network | none | INET, CIDR, MACADDR | | Geometric | limited Spatial types | POINT, LINE, POLYGON, CIRCLE, BOX, PATH | | Arrays | none | any type[] (e.g., INTEGER[], TEXT[]) | | Ranges | none | INT4RANGE, TSRANGE, DATERANGE, custom | | Enumerated | ENUM (as string) | ENUM (as custom type) | | Full-text search | no native type | TSVECTOR, TSQUERY | | Bit | BIT, BIT VARYING | BIT, BIT VARYING | | XML | no native type | XML |

PostgreSQL also allows creating composite types and domain types, enabling domain modeling at the database level:

-- PostgreSQL - composite type
CREATE TYPE address AS (
    street VARCHAR(200),
    city VARCHAR(100),
    postal_code VARCHAR(10),
    country VARCHAR(2)
);

CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    name VARCHAR(200) NOT NULL,
    billing_address address,
    shipping_address address
);

-- PostgreSQL - domain type with constraints
CREATE DOMAIN email AS VARCHAR(255)
    CHECK (VALUE ~ '^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$');

CREATE DOMAIN positive_amount AS NUMERIC(10,2)
    CHECK (VALUE > 0);

JSON Support - JSONB vs JSON#

Both systems support JSON data, but with fundamentally different approaches:

MySQL stores JSON as text with syntax validation. It offers extraction functions (->, ->>), but indexing capabilities are limited to generated virtual columns:

-- MySQL - working with JSON
CREATE TABLE products (
    id INT PRIMARY KEY AUTO_INCREMENT,
    name VARCHAR(200),
    attributes JSON
);

-- Indexing requires a virtual column
ALTER TABLE products
ADD COLUMN color VARCHAR(50) GENERATED ALWAYS AS (attributes->>'$.color') VIRTUAL,
ADD INDEX idx_color (color);

-- Query
SELECT * FROM products WHERE attributes->>'$.color' = 'red';

PostgreSQL offers two types: JSON (text) and JSONB (binary). JSONB is internally deconstructed into a binary format, enabling advanced indexing and operations:

-- PostgreSQL - JSONB with GIN index
CREATE TABLE products (
    id SERIAL PRIMARY KEY,
    name VARCHAR(200),
    attributes JSONB
);

-- GIN index on entire JSONB document
CREATE INDEX idx_attributes ON products USING GIN (attributes);

-- Index on specific path
CREATE INDEX idx_color ON products USING GIN ((attributes -> 'color'));

-- Advanced queries
SELECT * FROM products WHERE attributes @> '{"color": "red"}';
SELECT * FROM products WHERE attributes ? 'dimensions';
SELECT * FROM products WHERE attributes ->> 'weight' > '5';

-- Updating nested values
UPDATE products
SET attributes = jsonb_set(attributes, '{dimensions,height}', '25')
WHERE id = 1;

-- JSON aggregations
SELECT
    attributes ->> 'category' AS category,
    COUNT(*),
    AVG((attributes ->> 'price')::numeric) AS avg_price
FROM products
GROUP BY attributes ->> 'category';

PostgreSQL's JSONB is significantly more performant for querying semi-structured data and is one of the reasons PostgreSQL is often chosen as an alternative to NoSQL databases.

Full-Text Search#

MySQL Full-Text Search#

MySQL offers full-text search through FULLTEXT indexes, available for InnoDB and MyISAM tables:

-- MySQL - full-text search
CREATE TABLE articles (
    id INT PRIMARY KEY AUTO_INCREMENT,
    title VARCHAR(200),
    body TEXT,
    FULLTEXT INDEX ft_idx (title, body)
);

-- Natural language mode search
SELECT * FROM articles
WHERE MATCH(title, body) AGAINST('database optimization' IN NATURAL LANGUAGE MODE);

-- Boolean mode search
SELECT * FROM articles
WHERE MATCH(title, body) AGAINST('+database -nosql +optimization' IN BOOLEAN MODE);

MySQL's full-text search is functional but limited - it lacks stemming support for many languages, offers limited ranking configuration, and has no native support for synonym dictionaries.

PostgreSQL Full-Text Search#

PostgreSQL provides a significantly more advanced full-text search system with native multi-language support, custom dictionaries, and configuration:

-- PostgreSQL - advanced full-text search
CREATE TABLE articles (
    id SERIAL PRIMARY KEY,
    title VARCHAR(200),
    body TEXT,
    search_vector TSVECTOR
);

-- Automatic search vector update
CREATE OR REPLACE FUNCTION update_search_vector()
RETURNS TRIGGER AS $$
BEGIN
    NEW.search_vector :=
        setweight(to_tsvector('english', COALESCE(NEW.title, '')), 'A') ||
        setweight(to_tsvector('english', COALESCE(NEW.body, '')), 'B');
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER articles_search_update
    BEFORE INSERT OR UPDATE ON articles
    FOR EACH ROW EXECUTE FUNCTION update_search_vector();

-- GIN index on vector
CREATE INDEX idx_search ON articles USING GIN (search_vector);

-- Search with ranking
SELECT
    title,
    ts_rank(search_vector, query) AS rank,
    ts_headline('english', body, query, 'StartSel=<b>, StopSel=</b>') AS snippet
FROM articles, to_tsquery('english', 'database & optimization') AS query
WHERE search_vector @@ query
ORDER BY rank DESC;

PostgreSQL supports stemming for over 30 languages, field weighting, highlighted snippet generation (headlines), and custom dictionaries - making it a full-fledged alternative to Elasticsearch in many scenarios.

Stored Procedures and Triggers#

MySQL#

MySQL supports stored procedures and functions using SQL/PSM (SQL/Persistent Stored Modules):

-- MySQL - stored procedure
DELIMITER //
CREATE PROCEDURE calculate_order_total(IN order_id INT, OUT total DECIMAL(10,2))
BEGIN
    SELECT SUM(quantity * unit_price)
    INTO total
    FROM order_items
    WHERE order_items.order_id = order_id;

    UPDATE orders SET total_amount = total WHERE id = order_id;
END //
DELIMITER ;

CALL calculate_order_total(42, @total);
SELECT @total;

PostgreSQL#

PostgreSQL offers a much more comprehensive ecosystem of procedural languages:

• PL/pgSQL - native procedural language (similar to Oracle's PL/SQL)
• PL/Python - procedures in Python
• PL/Perl - procedures in Perl
• PL/V8 - procedures in JavaScript (V8 engine)
• PL/Rust - procedures in Rust (via pgx/pgrx)

-- PostgreSQL - PL/pgSQL function with exception handling
CREATE OR REPLACE FUNCTION transfer_funds(
    sender_id INTEGER,
    recipient_id INTEGER,
    amount NUMERIC
) RETURNS BOOLEAN AS $$
DECLARE
    sender_balance NUMERIC;
BEGIN
    -- Pessimistic locking
    SELECT balance INTO sender_balance
    FROM accounts WHERE id = sender_id
    FOR UPDATE;

    IF sender_balance < amount THEN
        RAISE EXCEPTION 'Insufficient funds: % < %', sender_balance, amount;
    END IF;

    UPDATE accounts SET balance = balance - amount WHERE id = sender_id;
    UPDATE accounts SET balance = balance + amount WHERE id = recipient_id;

    INSERT INTO transactions (from_id, to_id, amount, created_at)
    VALUES (sender_id, recipient_id, amount, NOW());

    RETURN TRUE;
EXCEPTION
    WHEN OTHERS THEN
        RAISE NOTICE 'Transfer error: %', SQLERRM;
        RETURN FALSE;
END;
$$ LANGUAGE plpgsql;

PostgreSQL also supports procedures (since version 11), which unlike functions can manage transactions (COMMIT/ROLLBACK inside the procedure body).

Replication and Clustering#

MySQL - Mature Replication Ecosystem#

MySQL offers several replication models:

• Asynchronous Replication - classic master-slave (source-replica) with binary log
• Semi-Synchronous Replication - master waits for acknowledgment from at least one replica
• Group Replication - multi-master with Paxos consensus
• MySQL InnoDB Cluster - integrated HA solution with MySQL Shell, MySQL Router, and Group Replication
• MySQL NDB Cluster - distributed, fully synchronous shared-nothing cluster

# MySQL InnoDB Cluster - configuration
mysqlsh -- dba configure-instance root@mysql1:3306
mysqlsh -- dba configure-instance root@mysql2:3306
mysqlsh -- dba configure-instance root@mysql3:3306

# Create cluster
mysqlsh root@mysql1:3306 -- dba create-cluster 'myCluster'
mysqlsh root@mysql1:3306 -- cluster add-instance root@mysql2:3306
mysqlsh root@mysql1:3306 -- cluster add-instance root@mysql3:3306

PostgreSQL - Flexible Replication#

PostgreSQL offers:

• Streaming Replication - physical asynchronous or synchronous replication
• Logical Replication - table/publication-level replication (since PostgreSQL 10)
• Patroni - automatic failover with etcd/Consul/ZooKeeper
• Citus - extension for sharding and distributed database
• PgBouncer - connection pooling (not replication, but a critical scalability component)

-- PostgreSQL - logical replication setup
-- On source server (publisher)
CREATE PUBLICATION my_pub FOR TABLE orders, customers, products;

-- On target server (subscriber)
CREATE SUBSCRIPTION my_sub
    CONNECTION 'host=source_host dbname=mydb user=replicator'
    PUBLICATION my_pub;

MySQL has an advantage in out-of-the-box clustering solutions (InnoDB Cluster, NDB Cluster), while PostgreSQL offers greater flexibility through logical replication and a rich ecosystem of third-party tools.

Performance - Benchmarks in Context#

Comparing database performance is notoriously difficult because results depend on configuration, hardware, data schema, and query patterns. Nonetheless, here are general observations:

Read-Heavy Workloads#

| Scenario | MySQL (InnoDB) | PostgreSQL | |----------|---------------|------------| | Simple SELECT by primary key | Very fast | Very fast | | SELECT with JOINs (3-5 tables) | Fast | Fast, better optimizer | | Analytical queries (GROUP BY, Window Functions) | Good (since 8.0) | Excellent | | Full-text search | Basic | Advanced | | Queries on JSONB | Limited | Very fast with GIN | | Read from multiple replicas | Excellent | Excellent |

MySQL was historically faster for simple reads thanks to its lightweight connection model (thread-per-connection). However, PostgreSQL in recent versions (15, 16, 17) has significantly improved performance in these scenarios.

Write-Heavy Workloads#

| Scenario | MySQL (InnoDB) | PostgreSQL | |----------|---------------|------------| | Single INSERTs | Fast | Fast | | Bulk INSERT | Fast (LOAD DATA INFILE) | Fast (COPY) | | UPDATE with concurrency | Good | Excellent (MVCC) | | Multi-operation transactions | Good | Excellent | | UPSERT (INSERT ... ON CONFLICT) | Good | Excellent | | Table partitioning | Good (since 5.7) | Excellent (declarative) |

PostgreSQL generally handles intensive write operations better thanks to its more advanced MVCC implementation and intelligent query planner.

Important Benchmark Caveats#

• Default configurations for both systems are not optimized for production
• PostgreSQL requires regular VACUUM (autovacuum), which affects performance
• MySQL with innodb_flush_log_at_trx_commit=0 is faster but at the cost of durability
• Connection pooling (PgBouncer for PostgreSQL, ProxySQL for MySQL) has a huge impact
• On modern hardware (NVMe SSD, plenty of RAM) differences are often marginal
• The biggest impact on performance comes from indexes, schema, and queries - not the database engine

MVCC Implementation (Multi-Version Concurrency Control)#

Both MySQL (InnoDB) and PostgreSQL implement MVCC, but in fundamentally different ways:

MySQL InnoDB - UNDO Log#

InnoDB stores the current version of a row in the table and older versions in the UNDO segment. When a transaction needs an older version of a row, InnoDB reconstructs it from the UNDO logs.

Advantages: lower disk space usage for current data, no need for VACUUM. Disadvantages: reading old versions is slower (requires processing the UNDO chain), long-running transactions can cause UNDO segment growth.

PostgreSQL - In-Table Versioning#

PostgreSQL stores all row versions directly in the table. Each row has hidden columns xmin (creating transaction) and xmax (deleting/updating transaction). Old versions marked as invisible remain in the table until removed by VACUUM.

Advantages: faster reading of all versions (no reconstruction from logs), better concurrency. Disadvantages: tables grow faster (table bloat), requires the VACUUM process to reclaim space.

-- PostgreSQL - checking bloat levels
SELECT
    schemaname || '.' || tablename AS table_name,
    pg_size_pretty(pg_total_relation_size(schemaname || '.' || tablename)) AS total_size,
    n_dead_tup AS dead_tuples,
    n_live_tup AS live_tuples,
    ROUND(n_dead_tup::numeric / NULLIF(n_live_tup, 0) * 100, 2) AS dead_ratio_pct
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;

Autovacuum in PostgreSQL is enabled by default and usually works well, but in systems with very intensive writes it requires tuning.

Extensions - PostGIS, pgvector vs MySQL Plugins#

PostgreSQL - Extensibility as a Foundation#

PostgreSQL was designed with extensibility in mind. The extension system allows adding new data types, operators, functions, indexes, and procedural languages without modifying the core:

PostGIS - the best GIS extension for relational databases:

-- PostGIS - geospatial queries
CREATE EXTENSION postgis;

CREATE TABLE stores (
    id SERIAL PRIMARY KEY,
    name VARCHAR(200),
    location GEOMETRY(Point, 4326)
);

-- Find stores within 5 km of a given point
SELECT name, ST_Distance(
    location::geography,
    ST_SetSRID(ST_MakePoint(-73.9857, 40.7484), 4326)::geography
) AS distance_meters
FROM stores
WHERE ST_DWithin(
    location::geography,
    ST_SetSRID(ST_MakePoint(-73.9857, 40.7484), 4326)::geography,
    5000
)
ORDER BY distance_meters;

pgvector - vector search for AI/ML applications:

-- pgvector - semantic search
CREATE EXTENSION vector;

CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding vector(1536)  -- OpenAI ada-002 dimensions
);

CREATE INDEX idx_embedding ON documents
    USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

-- Find 10 most similar documents
SELECT content, 1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 10;

Other popular PostgreSQL extensions:

• TimescaleDB - time-series data
• pg_cron - job scheduling within the database
• pg_stat_statements - query performance analysis
• pg_partman - automatic partition management
• Citus - sharding and distributed database
• pg_trgm - fuzzy matching with trigram similarity
• hstore - key-value storage in a column

MySQL - Plugins and Storage Engines#

MySQL offers a plugin system, but it is less flexible than PostgreSQL extensions:

• Spatial Extensions - basic GIS functions (weaker than PostGIS)
• InnoDB - default transactional storage engine
• NDB - cluster storage engine
• X Plugin - NoSQL interface (Document Store)
• MySQL Shell Plugins - administrative tools
• Group Replication Plugin - multi-master replication

MySQL offers nothing comparable to pgvector, which is a significant gap in the era of AI applications. The only option is integration with external vector search engines.

Integration with Popular Frameworks#

MySQL - The Natural Choice for PHP and WordPress#

MySQL is the default database for a huge portion of the web ecosystem:

• WordPress - MySQL/MariaDB exclusively (4 out of 10 websites on the internet)
• Laravel - default configuration, Eloquent ORM well-optimized for MySQL
• Drupal - MySQL support as the primary database
• Magento/Adobe Commerce - MySQL is a requirement
• Ruby on Rails - full support, popular choice

// Laravel - MySQL configuration (default)
// .env
DB_CONNECTION=mysql
DB_HOST=127.0.0.1
DB_PORT=3306
DB_DATABASE=myapp
DB_USERNAME=root
DB_PASSWORD=secret

// Eloquent - identical syntax for both databases
$users = User::where('active', true)
    ->with('orders')
    ->orderBy('created_at', 'desc')
    ->paginate(20);

PostgreSQL - The Choice for Enterprise Applications and Data Science#

PostgreSQL is preferred by modern frameworks and applications requiring advanced features:

• Django - PostgreSQL as the recommended database, django.contrib.postgres with dedicated fields (ArrayField, JSONField, HStoreField)
• .NET / Entity Framework Core - excellent support via Npgsql, recommended for new projects
• Ruby on Rails - full support, growing popularity
• Spring Boot / JPA - full support, popular in enterprise environments
• Node.js (Prisma, TypeORM) - full support, preferred for new projects

# Django - dedicated PostgreSQL fields
from django.contrib.postgres.fields import ArrayField, JSONField
from django.contrib.postgres.search import SearchVector, SearchQuery, SearchRank

class Product(models.Model):
    name = models.CharField(max_length=200)
    tags = ArrayField(models.CharField(max_length=50), default=list)
    metadata = models.JSONField(default=dict)
    search_vector = SearchVectorField(null=True)

    class Meta:
        indexes = [
            GinIndex(fields=['search_vector']),
            GinIndex(fields=['tags']),
        ]

# Full-text search in Django
results = Product.objects.annotate(
    rank=SearchRank('search_vector', SearchQuery('database optimization', config='english'))
).filter(search_vector=SearchQuery('database optimization', config='english')).order_by('-rank')

// .NET - Entity Framework Core with PostgreSQL (Npgsql)
// Program.cs
builder.Services.AddDbContext<AppDbContext>(options =>
    options.UseNpgsql(builder.Configuration.GetConnectionString("DefaultConnection"),
        o => o.UseNetTopologySuite()  // PostGIS support
              .UseVector()));          // pgvector support

// Model with PostgreSQL types
public class Product
{
    public int Id { get; set; }
    public string Name { get; set; }
    public string[] Tags { get; set; }          // PostgreSQL array
    public JsonDocument Metadata { get; set; }   // JSONB
    public NpgsqlTsVector SearchVector { get; set; } // tsvector
    public Vector Embedding { get; set; }        // pgvector
}

Managed Services Comparison#

MySQL Managed#

| Service | Provider | Features | |---------|----------|----------| | Amazon RDS for MySQL | AWS | Multi-AZ, Read Replicas, up to 128 TB | | Amazon Aurora MySQL | AWS | MySQL-compatible, 5x faster, distributed storage | | Azure Database for MySQL | Microsoft | Flexible Server, HA, up to 16 TB | | Cloud SQL for MySQL | Google Cloud | HA, replication, automatic backups | | PlanetScale | Independent | Serverless, branching (like Git), Vitess-compatible | | DigitalOcean Managed MySQL | DigitalOcean | Simple, affordable |

PostgreSQL Managed#

| Service | Provider | Features | |---------|----------|----------| | Amazon RDS for PostgreSQL | AWS | Multi-AZ, Read Replicas, extensions | | Amazon Aurora PostgreSQL | AWS | PostgreSQL-compatible, distributed storage | | Azure Database for PostgreSQL | Microsoft | Flexible Server, Citus (distributed), pgvector | | Cloud SQL for PostgreSQL | Google Cloud | HA, AlloyDB (PostgreSQL-compatible, 4x faster) | | Supabase | Independent | Firebase alternative, real-time, Edge Functions | | Neon | Independent | Serverless, branching, autoscaling to zero | | CrunchyData | Independent | Enterprise PostgreSQL, Kubernetes operator | | Tembo | Independent | PostgreSQL as a platform (stacks) |

PostgreSQL has a richer ecosystem of managed services, particularly among modern providers (Supabase, Neon) focused on developer experience.

Licensing#

MySQL#

MySQL is available in two variants:

• Community Edition - GPL v2 (open source, copyleft)
• Enterprise Edition - Oracle commercial license (paid)

The GPL license means that if you distribute software containing MySQL, you must release the source code under the same terms (unless you purchase a commercial license). In practice, for SaaS applications (without distributing binaries), GPL is not an issue.

PostgreSQL#

PostgreSQL uses the PostgreSQL License (similar to MIT/BSD):

• Completely free - no fees, no restrictions
• You can use, modify, and distribute for any purpose (including commercial)
• No requirement to release source code
• No legal risk associated with corporate ownership

The PostgreSQL License is one of the most favorable in the open-source world and eliminates all legal concerns related to Oracle's ownership of MySQL.

Comparison Table - Summary#

| Criterion | MySQL | PostgreSQL | Comment | |-----------|:-----:|:----------:|---------| | SQL standards compliance | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - compliance leader | | Data types | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - far richer | | JSON support | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL's JSONB is unmatched | | Full-text search | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - multilingual, configurable | | Stored procedures | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - multiple procedural languages | | Performance (reads) | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | MySQL slightly faster for simple reads | | Performance (writes) | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL better for complex operations | | Replication | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | MySQL - more mature out-of-the-box solutions | | Extensibility | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostGIS, pgvector - unrivaled | | Web ecosystem | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | MySQL - WordPress, Laravel ecosystem | | Enterprise / Data Science | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - Django, .NET, AI/ML | | Managed services | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - Supabase, Neon, AlloyDB | | License | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ | PostgreSQL - permissive, risk-free | | Ease of setup | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | MySQL simpler initial configuration | | Documentation | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | Both excellent, PostgreSQL more complete |

Conclusion - When to Choose Which Database?#

Choose MySQL when:#

• Building on WordPress, Drupal, or Magento - MySQL is required or the default
• You need a simple, proven solution for a typical web application
• Your team knows MySQL and doesn't need PostgreSQL's advanced features
• You require mature clustering - InnoDB Cluster, NDB Cluster
• You're in the PHP/Laravel ecosystem and don't need PostGIS or pgvector
• Maximum simple read performance at massive scale is the priority

Choose PostgreSQL when:#

• Building a modern application requiring JSONB, full-text search, or geospatial data
• Creating an AI/ML application with vector search (pgvector)
• You need full SQL standards compliance and advanced data types
• Building an enterprise system with Django, .NET, or Spring Boot
• You require advanced analytics - Window Functions, CTEs, partitioning
• You value a permissive license with no legal risk tied to Oracle
• You plan to use modern services like Supabase or Neon

In 2025, PostgreSQL is increasingly the default choice for new projects. Its versatility, extensibility, and permissive license make it a universal solution - from simple web applications to advanced analytical systems and AI applications. MySQL remains strong in the PHP/WordPress ecosystem and where simplicity and team familiarity are priorities.

Need Help Choosing the Right Database?#

At MDS Software Solutions Group, we have years of experience with both MySQL and PostgreSQL. We help companies choose the optimal technology stack and build applications that effectively leverage the capabilities of the chosen database - from simple CRUD applications to advanced systems with vector search, geospatial data, and real-time analytics.

Whether you need to migrate from MySQL to PostgreSQL, optimize an existing database, or design an architecture from scratch - our team will deliver a solution tailored to your business requirements.

Contact us and let's discuss your project. The first consultation is free.