MySQL vs MongoDB
MySQL
Free, open-source relational database management system with manual administration
Financial institutions, healthcare systems, e-commerce platforms with complex inventory, and applications requiring strict data consistency
MongoDB
NoSQL document database with distributed architecture and flexible schemas.
Content management systems, real-time analytics platforms, IoT applications with high write volumes, and startups with evolving product requirements
Short Answer
MySQL is a relational database using structured SQL queries and ACID transactions, while MongoDB is a NoSQL document database using flexible JSON-like documents. MySQL excels for structured data with complex relationships, while MongoDB prioritizes scalability and flexible schemas for unstructured data.
Our Verdict
AI-assistedChoose MySQL if you have structured data with complex relationships, require strict ACID compliance across transactions, or need established enterprise support with minimal learning curve. Choose MongoDB if you need rapid scaling, have semi-structured or frequently evolving data schemas, or prioritize developer flexibility with JSON-like document handling.
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Choose MySQL if
Financial institutions, healthcare systems, e-commerce platforms with complex inventory, and applications requiring strict data consistency
Choose MongoDB if
Content management systems, real-time analytics platforms, IoT applications with high write volumes, and startups with evolving product requirements
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Key Differences at a Glance
Key Facts & Figures
| Metric | MySQL | MongoDB | Diff |
|---|---|---|---|
| Simple Query Throughput(queries/sec) | ~28,000 (MySQL 8.4) | — | — |
| Complex Join Performance(ms response time) | ~450 (5-table join) | — | — |
| Base Memory Footprint(MB) | ~80 (minimal config) | — | — |
| AWS RDS Managed Cost($/month (db.t3.micro, single-AZ)) | $13.50 (MySQL 8.4) | — | — |
| Supported Versions (2026)(major versions) | 5 (8.0-8.4 active) | — | — |
| Replication Lag (typical)(ms) | 5-50 (binary log) | — | — |
| Concurrent Connections(connections) | 151 (default, configurable) | — | — |
| Max Database Size(TB) | 140 TB per table | — | — |
| Memory Footprint(MB) | 150-500 MB | — | — |
| Typical Query Response Time(ms) | 5-50 ms (indexed queries) | — | — |
| Average Query Latency (structured data)(ms) | 3-5ms for simple queries | 8-12ms for equivalent queries | -60% |
| Memory Usage (100GB dataset)(GB) | 8-12GB working set | 24-36GB working set | -67% |
| Years in Production(years) | 25+ years (MySQL 1.0 released 1995) | 19 years (MongoDB 1.0 released 2009) | +32% |
| Write Throughput (single server)(operations/second) | 10,000-50,000 ops/sec | 100,000-500,000 ops/sec | -88% |
| Community Popularity(% of developers) | 46% of web applications (Stack Overflow 2024) | 28% of web applications (Stack Overflow 2024) | +64% |
| Monthly Cost (10GB, 100K requests)(USD) | $50-200 (self-hosted) | — | — |
| Community Size (Stack Overflow tags)(thousand questions) | 600+ thousand | — | — |
| Read Throughput Improvement(x multiplier) | 1x baseline | — | — |
| Cost (On-Demand, Single Instance/Month)(USD) | $0 (open-source) | — | — |
| Deployment Platforms Supported(count) | 5+ (on-prem, cloud, edge, containers, hybrid) | — | — |
| Write Throughput (bulk inserts)(documents/second) | 50,000-100,000 docs/sec | 50,000-100,000 docs/sec | — |
| Query Language Complexity (learning curve)(months) | 2-4 weeks (MongoDB Query Language simpler syntax) | 2-4 weeks (MongoDB Query Language simpler syntax) | — |
| Memory Usage (100K documents)(MB) | 250-350 MB | 250-350 MB | — |
| Index Types Supported(count) | 10+ (single field, compound, geospatial, text, wildcard) | 10+ (single field, compound, geospatial, text, wildcard) | — |
| Free Tier Storage(GB) | 0 GB (Atlas free tier discontinued) | 0 GB (Atlas free tier discontinued) | — |
| Minimum Production Tier Cost(USD/month) | $57 | $57 | — |
| Annual npm Downloads (Drivers)(millions) | 21.7M | 21.7M | — |
| Baseline Latency(milliseconds) | 5-10ms (cluster dependent) | 5-10ms (cluster dependent) | — |
| Minimum Monthly Cost (Production)(USD) | $57 (M10 Atlas cluster) | $57 (M10 Atlas cluster) | — |
| Read Throughput (1TB dataset)(ops/second) | 10,000-50,000 ops/sec | 10,000-50,000 ops/sec | — |
| Document Size Limit(MB) | 16 MB | 16 MB | — |
| Minimum Monthly Cost (Managed)(USD) | $57 | $57 | — |
| Time to Production Deployment(hours) | 40-80 hours | 40-80 hours | — |
| Query Complexity (Aggregation Capability)(rating 1-10) | 9 (full aggregation pipeline) | 9 (full aggregation pipeline) | — |
| Setup DevOps Requirement(hours/month) | 20-40 hours | 20-40 hours | — |
| Real-time Sync Implementation Effort(days) | 5-10 days (custom code) | 5-10 days (custom code) | — |
| Startup Monthly Cost(USD) | $57/month | $57/month | — |
| Average Query Latency(ms) | 10-50 ms | 10-50 ms | — |
| Free Tier Database Storage(GB) | 512 MB | 512 MB | — |
| Monthly Cost (Entry Paid Tier)(USD) | $57 | $57 | — |
| Query Response Time (Median)(ms) | 38 ms (indexed query) | 38 ms (indexed query) | — |
| Maximum Database Size (Free Tier)(GB) | 0.512 GB | 0.512 GB | — |
| Max Write Throughput (Optimized)(writes/second) | ~50,000 (sharded cluster) | ~50,000 (sharded cluster) | — |
| Storage Overhead (vs Equivalent Relational)(multiple) | 2-3x larger (denormalization) | 2-3x larger (denormalization) | — |
| Community Size & Ecosystem(GitHub stars (thousands)) | ~25k stars | ~25k stars | — |
| Typical Setup Complexity(time to production (hours)) | 4-8 hours (managed cloud option available) | 4-8 hours (managed cloud option available) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
MySQL
Relational (tables, rows, columns)
MongoDB
Document-based (JSON/BSON documents)
MySQL
SQL (Structured Query Language)
MongoDB
MongoDB Query Language (MQL)
MySQL
Fixed schema required
MongoDB
Dynamic schema (schema-less)🏆
MySQL
Complex sharding, limited native support
MongoDB
Built-in sharding, native horizontal scaling🏆
MySQL
Full ACID support across tables🏆
MongoDB
Multi-document ACID (MongoDB 4.0+)
MySQL
Easier for developers familiar with SQL
MongoDB
Easier for developers familiar with JSON/JavaScript
MySQL
Highly optimized for structured queries🏆
MongoDB
Good for nested document retrieval
Full Comparison
| Attribute |  MySQL | MongoDB |
|---|---|---|
| ACID Compliance Level | Partial (InnoDB only) | — |
| Uptime SLA(%) | User-dependent (no guarantee) | — |
| Multi-row/Document Transactions(null) | ACID from v4.0 (slower than PostgreSQL) | — |
| Simple Query Throughput(queries/sec) | ~28,000 (MySQL 8.4) | — |
| Complex Join Performance(ms response time) | ~450 (5-table join) | — |
| Replication Lag (typical)(ms) | 5-50 (binary log) | — |
| Concurrent Connections(connections) | 151 (default, configurable) | — |
| Memory Footprint(MB) | 150-500 MB | — |
Show 13 more attributesTypical Query Response Time(ms) 5-50 ms (indexed queries) — Average Query Latency (structured data)(ms) 3-5ms for simple queries 8-12ms for equivalent queries Write Throughput (single server)(operations/second) 10,000-50,000 ops/sec 100,000-500,000 ops/sec Read Throughput Improvement(x multiplier) 1x baseline — Write Throughput (bulk inserts)(documents/second) 50,000-100,000 docs/sec — Memory Usage (100K documents)(MB) 250-350 MB — Baseline Latency(milliseconds) 5-10ms (cluster dependent) — Read Throughput (1TB dataset)(ops/second) 10,000-50,000 ops/sec — Maximum Concurrent Connections(connections) Unlimited (scales with cluster) — Real-time Capabilities(ms latency) Change Streams (enterprise feature) — Average Query Latency(ms) 10-50 ms — Query Response Time (Median)(ms) 38 ms (indexed query) — Max Write Throughput (Optimized)(writes/second) ~50,000 (sharded cluster) — | ||
| JSON Query Capability | JSON functions only | — |
| Full-Text Search | Limited, basic support | — |
| Time-Series Optimization | Standard table partitioning | — |
| Transaction Support(consistency level) | Full ACID across multiple tables (since v5.7) | Multi-document ACID (since v4.0, snapshot isolation) |
| Auto-Scaling Capability | Manual configuration required | — |
Show 4 more attributesIndex Types Supported(count) 10+ (single field, compound, geospatial, text, wildcard) — Built-in Authentication(boolean) No (third-party only) — Auto-generated API Manual API layer required — Query Language Complexity Support(capability level) Aggregation pipeline (moderate for complex queries) — | ||
| Base Memory Footprint(MB) | ~80 (minimal config) | — |
| Vector Similarity Support | Via third-party extensions | — |
| AWS RDS Managed Cost($/month (db.t3.micro, single-AZ)) | $13.50 (MySQL 8.4) | — |
| Supported Versions (2026)(major versions) | 5 (8.0-8.4 active) | — |
| Enterprise Support Availability(availability) | Oracle, multiple vendors | — |
| Enterprise Support(null) | MongoDB Atlas managed cloud (SaaS); community edition free | — |
| Max Database Size(TB) | 140 TB per table | — |
| Maximum Read Replicas(instances) | Unlimited (with sharding complexity) | — |
| Horizontal Scaling | Manual sharding configuration required | — |
| Max Horizontal Scalability | Unlimited (sharding across clusters) | — |
| Native Horizontal Scaling | Yes (automatic sharding) | — |
| Network Access | Yes - TCP/IP protocol | — |
| Horizontal Scalability | Manual sharding (theoretical unlimited) | Native automatic sharding across nodes |
| Horizontal Scaling Method | Manual sharding + Atlas auto-scaling | — |
| Built-in Replication | Yes - master-slave, group replication | — |
| Schema Flexibility | Fixed schema, requires migration for changes | Schema-less/flexible |
| Memory Usage (100GB dataset)(GB) | 8-12GB working set | 24-36GB working set |
| Years in Production(years) | 25+ years (MySQL 1.0 released 1995) | 19 years (MongoDB 1.0 released 2009) |
| Community Popularity(% of developers) | 46% of web applications (Stack Overflow 2024) | 28% of web applications (Stack Overflow 2024) |
| Setup Time(minutes) | 240-480 minutes | — |
| Required DevOps Knowledge | High (infrastructure management needed) | — |
| Monthly Cost (10GB, 100K requests)(USD) | $50-200 (self-hosted) | — |
| Cost (On-Demand, Single Instance/Month)(USD) | $0 (open-source) | — |
| Free Tier Storage(GB) | 0 GB (Atlas free tier discontinued) | — |
| Minimum Monthly Cost (Production)(USD) | $57 (M10 Atlas cluster) | — |
| Minimum Monthly Cost (Managed)(USD) | $57 | — |
Show 1 more attributeStartup Monthly Cost(USD) $57/month — | ||
| Maximum Storage per Database(TB) | Unlimited | — |
| Maximum Storage Capacity(TB) | 64TB (hardware dependent) | — |
| Database Branching Support | Third-party tools only | — |
| Community Size (Stack Overflow tags)(thousand questions) | 600+ thousand | — |
| Community Size & Ecosystem(GitHub stars (thousands)) | ~25k stars | — |
| Backup Automation | Manual configuration required | — |
| Operational Complexity | Moderate to high (backups, sharding, monitoring) | — |
| Setup DevOps Requirement(hours/month) | 20-40 hours | — |
| Deployment Platforms Supported(count) | 5+ (on-prem, cloud, edge, containers, hybrid) | — |
| ACID Compliance | Full ACID support with InnoDB | — |
| Transaction ACID Support | Multi-document (v4.0+) | — |
| Foreign Key Support | Not enforced natively | — |
| Query Language Complexity (learning curve)(months) | 2-4 weeks (MongoDB Query Language simpler syntax) | — |
| Minimum Production Tier Cost(USD/month) | $57 | — |
| Maximum Connections Per Database(connections) | Unlimited (sharding dependent) | — |
| Query Language Standard | MongoDB Query Language (MQL) | — |
| Annual npm Downloads (Drivers)(millions) | 21.7M | — |
| Query Complexity Support | 200+ query operators, joins, aggregation pipeline | — |
| Transaction Scope | Multi-document ACID across collections | — |
| Document Size Limit(MB) | 16 MB | — |
| Query Complexity (Aggregation Capability)(rating 1-10) | 9 (full aggregation pipeline) | — |
| Time to Production Deployment(hours) | 40-80 hours | — |
| Real-time Sync Implementation Effort(days) | 5-10 days (custom code) | — |
| Read Operations (Free Tier Monthly)(operations) | No free tier | — |
| Free Tier Database Storage(GB) | 512 MB | — |
| Monthly Cost (Entry Paid Tier)(USD) | $57 | — |
| Maximum Database Size (Free Tier)(GB) | 0.512 GB | — |
| ACID Transaction Support | Multi-document (v4.0+, with limitations) | — |
| Storage Overhead (vs Equivalent Relational)(multiple) | 2-3x larger (denormalization) | — |
| Schema Enforcement | Optional/flexible (schema-on-read) | — |
| Typical Setup Complexity(time to production (hours)) | 4-8 hours (managed cloud option available) | — |
MySQLShow 13 more attributes
Show 4 more attributes
Show 1 more attribute
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
MySQL
Pros
- Full ACID compliance across multiple tables ensures data integrity for financial and transactional systems
- Mature ecosystem with 25+ years of development, extensive tooling, and comprehensive documentation
- Superior performance for complex JOIN operations on structured data (typically 30-40% faster than document queries)
- Proven at scale: powers Facebook, Twitter, and Wikipedia with petabyte-scale deployments
- Low resource footprint: runs efficiently on modest hardware with minimal memory overhead
Cons
- Vertical scaling limitation: sharding is complex and requires significant architectural planning
- Schema inflexibility: schema changes require migrations that can lock large tables during production updates
- Poor fit for semi-structured data: storing nested objects requires extensive normalization or JSON columns
MongoDB
Pros
- Native horizontal scalability: automatic sharding distributes data seamlessly across servers without complex configuration
- Schema flexibility: add/modify fields without downtime or schema migrations, ideal for rapidly evolving applications
- Developer-friendly: JSON-like BSON format aligns naturally with JavaScript/Python object models, reducing impedance mismatch
- Embedded documents: stores related data together, eliminating costly JOINs for hierarchical/nested structures
- High throughput: handles 1M+ operations/second on standard hardware for write-heavy workloads
Cons
- Memory overhead: typical deployments consume 2-3x more RAM than equivalent MySQL setups due to BSON overhead
- Eventual consistency: distributed transactions have latency costs and eventual consistency windows increase complexity
- Join penalties: queries across multiple collections are significantly slower (5-10x) than native SQL JOINs
Frequently Asked Questions
Use MySQL if you have well-defined, structured data with clear relationships (e.g., user accounts, orders, inventory). Use MongoDB if your schema is evolving, you need rapid horizontal scaling, or you're building real-time applications with high write volumes. MongoDB has faster time-to-market; MySQL has better long-term data integrity guarantees.
Resources & Learn More
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