MySQL vs MongoDB: Which Database Is Right for Your Project?

Compare MySQL and MongoDB - relational vs document database. See pricing, features, and which fits your data structure needs.

Updated 2026-09 · 2026

MySQL

Open-source relational database with ACID compliance

0forever

Strengths

+Completely free and open-source
+Strong ACID compliance for data integrity
+Mature ecosystem with decades of tooling

Weaknesses

-Schema changes require migrations
-Vertical scaling can be expensive
-Less flexible for rapidly changing data models

Best for

Financial systems, e-commerce platforms, and applications requiring strict data consistency and complex relationships

MongoDB

Document database built for modern applications

0up to 512MB

Strengths

+Free tier includes 512MB storage
+Flexible schema for evolving data models
+Horizontal scaling built-in

Weaknesses

-Eventual consistency by default
-Higher memory usage than relational databases
-Paid tiers required for larger datasets

Best for

Content management, real-time analytics, IoT applications, and projects with rapidly evolving data structures

Feature Comparison

Feature	MySQL	MongoDB
Pricing	Free forever, self-hosted	Free up to 512MB, then $57/mo+
Data Model	Relational (tables, rows, columns)	Document-based (JSON-like documents)
Schema	Fixed schema, requires migrations	Flexible schema, dynamic fields
Query Language	SQL (standardized)	MongoDB Query Language (MQL)
ACID Compliance	Full ACID at all levels	ACID at document level, multi-doc since 4.0
Scaling	Primarily vertical, read replicas	Horizontal sharding built-in
Joins	Native, optimized joins	Limited, $lookup aggregation
Transactions	Mature, multi-table transactions	Multi-document since v4.0
Performance	Excellent for complex queries	Fast for simple reads/writes
Storage	Row-based, efficient for structured data	Document-based, more storage overhead
Hosting Options	Universal support everywhere	MongoDB Atlas, self-hosted, many providers
Learning Curve	SQL knowledge widely applicable	Easier for developers familiar with JSON

The Verdict

Choose MySQL if you need strict data consistency, complex relationships, and a proven relational model - it's completely free and battle-tested. Choose MongoDB if your data structure is evolving, you need horizontal scaling, or you're building modern apps with JSON-like data - but budget for paid tiers once you exceed 512MB.

How to switch from MySQL to MongoDB

1Export your MySQL data using mysqldump for a full SQL backup, or run SELECT ... INTO OUTFILE per table to generate CSV files you can transform for document storage.
2Design your MongoDB document structure by deciding which related tables should be embedded as nested documents versus kept as separate collections with references.
3Convert the exported CSV/SQL data into JSON documents matching your new schema, then load it into MongoDB using mongoimport or mongorestore.
4Rebuild any application queries, ORM models, and stored procedures to use MongoDB's query language (MQL) or an ODM like Mongoose instead of raw SQL.
5Recreate reporting, BI, and integration connections using the MongoDB Connector for BI or native MongoDB drivers, since JDBC/ODBC SQL connections won't work as-is.
6Run both databases in parallel for a short validation period, compare record counts and key queries, then cut over the team and decommission the old MySQL instance.

MySQL vs MongoDB: common questions

How do I export data from MySQL to import into MongoDB?+

Use mysqldump to export your tables as SQL, or better, export to CSV/JSON per table using SELECT ... INTO OUTFILE or a tool like mysql2json. Then use MongoDB's mongoimport utility to load each CSV or JSON file as a collection, mapping foreign keys into embedded documents or references manually.

What do I lose by moving from MySQL to MongoDB?+

You lose native joins, strict schema enforcement, and full multi-table ACID transactions unless you carefully use MongoDB's multi-document transactions (available since v4.0, with a performance cost). You also lose SQL as a universal query language, so reporting and BI tools that expect SQL will need MongoDB-specific connectors or a translation layer.

Is the MongoDB free tier enough for a small team?+

The M0 free tier gives 512MB storage and shared CPU/RAM, which is fine for prototypes, internal tools, or apps under a few thousand records. Most small production apps outgrow it within months and need to move to a paid M10 cluster starting around $57/mo.

Will my existing integrations still work after switching to MongoDB?+

Any integration that talks to MySQL over a JDBC/ODBC SQL connection will need to be rebuilt using MongoDB drivers or the MongoDB Connector for BI. Most ORMs (Prisma, Mongoose, Django) support both, but custom SQL queries, stored procedures, and reporting dashboards built on SQL will need to be rewritten.

Does MongoDB end up cheaper than MySQL over time?+

No - MySQL is free forever if self-hosted, while MongoDB's free tier caps at 512MB and paid Atlas clusters start around $57/mo and scale up with storage and compute. If cost is the main driver, self-hosted MySQL (or even self-hosted MongoDB Community Edition) stays cheaper than MongoDB Atlas long-term.