Django vs Laravel
Django
Full-stack Python web framework with batteries included for rapid development
Data scientists, AI/ML engineers, startups building complex data-driven applications, enterprises needing rapid CRUD app development with built-in admin, and teams leveraging Python across the stack.
Laravel
Full-featured PHP framework with ORM, authentication, and modern tooling (v11.x as of 2026)
Web agencies, SaaS platforms, content management systems, traditional CRUD applications, teams prioritizing developer happiness, startups needing rapid MVP launches, and projects where speed-to-market is critical.
Short Answer
Django is a Python-based framework excelling in rapid development and data science integration with batteries-included architecture, while Laravel is a PHP framework known for elegant syntax and exceptional developer experience with extensive built-in tooling. Django dominates in AI/ML projects; Laravel leads in traditional web applications.
Our Verdict
AI-assistedChoose Django if you're building data-heavy applications, need AI/ML integration, require a powerful admin panel out-of-the-box, or want to leverage Python's rich scientific ecosystem. Choose Laravel if you prioritize rapid prototyping, need async job queues natively, prefer elegant and intuitive syntax, or are working primarily with traditional web applications requiring quick iteration.
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Choose Django if
Data scientists, AI/ML engineers, startups building complex data-driven applications, enterprises needing rapid CRUD app development with built-in admin, and teams leveraging Python across the stack.
Choose Laravel if
Web agencies, SaaS platforms, content management systems, traditional CRUD applications, teams prioritizing developer happiness, startups needing rapid MVP launches, and projects where speed-to-market is critical.
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Key Differences at a Glance
Key Facts & Figures
| Metric | Django | Laravel | Diff |
|---|---|---|---|
| Average Request Latency(ms) | 200-400ms | — | — |
| Concurrent Connections (single core)(connections) | 100-500 | — | — |
| Time to First Working App(hours) | 1-2 | — | — |
| Package Ecosystem Size(packages) | 450K | — | — |
| Memory Usage (Idle)(MB) | 80-120MB | — | — |
| GitHub Stars (2026)(stars) | 77K | — | — |
| Average Development Speed (MVP)(weeks) | 3 weeks | 1.5 weeks | +100% |
| Job Openings (Global, 2025)(positions) | 45,000 | 38,000 | +18% |
| Average Page Load Time(seconds) | 145ms | 95ms | +53% |
| Developer Satisfaction (2025 Survey)(percentage) | 82% | 89% | -8% |
| Average Request Response Time(milliseconds) | 65ms | — | — |
| Available Packages/Gems(count) | 500,000+ | — | — |
| Time to Build Basic MVP(weeks) | 2-3 weeks | — | — |
| Job Market Postings (2025)(estimated count) | 28,000+ | — | — |
| Learning Curve for Beginners(months to proficiency) | 4-6 months | — | — |
| Throughput at Scale (Req/sec)(requests per second) | 2,500 req/sec | — | — |
| GitHub Stars(stars) | 78,000+ stars | 66,100 | +18% |
| Throughput (Requests/second)(req/s) | ~1,200 req/s | — | — |
| Startup Time(milliseconds) | ~300-500ms | — | — |
| Memory Usage (base)(MB) | ~50MB | — | — |
| Time to First API Endpoint(hours) | 8-12 hours | — | — |
| Third-party Packages(packages) | 13,000+ packages | — | — |
| Core Framework Size(KB) | ~2,100 KB | — | — |
| Request/Response Latency (simple GET)(ms) | 45-65 ms | — | — |
| Weekly Downloads (PyPI)(thousands) | 1,200 thousand | — | — |
| Minimal Project Setup Time(minutes) | 15-20 | — | — |
| Stack Overflow Questions (all-time)(thousands) | 3,800 thousand | — | — |
| Time to Production (months)(months) | 1.5-2 | — | — |
| Throughput Capacity (requests/sec)(req/sec) | ~5,000 | — | — |
| Lines of Code per Feature(LOC) | 100 | — | — |
| Available Job Openings (US, 2026)(thousands) | ~45K | — | — |
| Memory Usage (baseline app)(MB) | ~150-200 | — | — |
| Learning Curve (hours to 'Hello World')(hours) | 4-6 | — | — |
| Cold Start Time(milliseconds) | 600ms | — | — |
| Base Framework Size(megabytes) | 11 MB | — | — |
| Requests/Second (Throughput)(req/s) | ~1,200 req/s | — | — |
| Learning Time to Proficiency(hours) | 50 hours | — | — |
| Community Size (GitHub stars)(stars) | 79k stars | — | — |
| Development Speed (Median Project Timeline)(weeks) | 8-12 weeks | — | — |
| Installation Size(MB) | ~50 MB | ~50 MB | — |
| Time to Build CRUD App(hours) | 2-4 hours | 2-4 hours | — |
| Available Third-Party Packages(count) | ~200,000 packages | ~200,000 packages | — |
| Market Share Among PHP Developers(%) | 64% of PHP frameworks | 64% of PHP frameworks | — |
| Average Response Time (Benchmark)(ms) | 45-60 ms per request | 45-60 ms per request | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
Django
Python
Laravel
PHP
Django
2-3 weeks (average project)
Laravel
1-2 weeks (average project)🏆
Django
Yes, auto-generated with full CRUD🏆
Laravel
No, requires third-party packages
Django
Moderate (Django ORM)
Laravel
Shallow (Eloquent ORM - intuitive)🏆
Django
Partial (Django 3.1+, not default)
Laravel
Full native support (queues built-in)🏆
Django
Excellent (TensorFlow, PyTorch, scikit-learn)🏆
Laravel
Limited (no native ML support)
Django
45,000+ open positions globally🏆
Laravel
38,000+ open positions globally
Full Comparison
| Attribute | Django |  Laravel |
|---|---|---|
| Average Request Latency(ms) | 200-400ms | — |
| Memory Usage (Idle)(MB) | 80-120MB | — |
| Average Page Load Time(seconds) | 145ms | 95ms |
| Average Request Response Time(milliseconds) | 65ms | — |
| Throughput at Scale (Req/sec)(requests per second) | 2,500 req/sec | — |
Show 9 more attributesThroughput (Requests/second)(req/s) ~1,200 req/s — Startup Time(milliseconds) ~300-500ms — Memory Usage (base)(MB) ~50MB — Core Framework Size(KB) ~2,100 KB — Request/Response Latency (simple GET)(ms) 45-65 ms — Throughput Capacity (requests/sec)(req/sec) ~5,000 — Cold Start Time(milliseconds) 600ms — Requests/Second (Throughput)(req/s) ~1,200 req/s — Average Response Time (Benchmark)(ms) 45-60 ms per request — | ||
| Concurrent Connections (single core)(connections) | 100-500 | — |
| Time to First Working App(hours) | 1-2 | — |
| Time to Build Basic MVP(weeks) | 2-3 weeks | — |
| Time to First API Endpoint(hours) | 8-12 hours | — |
| Minimal Project Setup Time(minutes) | 15-20 | — |
| Time to Production (months)(months) | 1.5-2 | — |
Show 1 more attributeTime to Build CRUD App(hours) 2-4 hours — | ||
| Package Ecosystem Size(packages) | 450K | — |
| ML/AI Library Integration | Excellent (TensorFlow, PyTorch, scikit-learn) | Limited (minimal options) |
| Available Packages/Gems(count) | 500,000+ | — |
| Third-party Packages(packages) | 13,000+ packages | — |
| Community Size (GitHub stars)(stars) | 79k stars | — |
Show 1 more attributeAvailable Third-Party Packages(count) ~200,000 packages — | ||
| GitHub Stars (2026)(stars) | 77K | — |
| Admin Panel Included | Yes (auto-generated) | — |
| Built-in Admin Panel | Yes, auto-generated | No, third-party required |
| Built-in Admin Dashboard | Yes, auto-generated | — |
| Async Request Support | Partial (3.1+) | — |
| Built-in Database ORM | Django ORM (included) | — |
Show 3 more attributesAdmin Interface Auto-generated from models — Built-in Authentication(feature) Yes, with MFA scaffolding — Job Queue System(feature) Native (Redis, database, sync) — | ||
| Average Development Speed (MVP)(weeks) | 3 weeks | 1.5 weeks |
| Job Openings (Global, 2025)(positions) | 45,000 | 38,000 |
| Available Job Openings (US, 2026)(thousands) | ~45K | — |
| Async Support Level | Partial (optional, requires setup) | Full native (built-in queues) |
| Developer Satisfaction (2025 Survey)(percentage) | 82% | 89% |
| Job Market Postings (2025)(estimated count) | 28,000+ | — |
| Learning Curve for Beginners(months to proficiency) | 4-6 months | — |
| GitHub Stars(stars) | 78,000+ stars | 66,100 |
| Weekly Downloads (PyPI)(thousands) | 1,200 thousand | — |
| Stack Overflow Questions (all-time)(thousands) | 3,800 thousand | — |
| Authentication Built-in | Yes (user model, permissions, groups) | — |
| Lines of Code per Feature(LOC) | 100 | — |
| Memory Usage (baseline app)(MB) | ~150-200 | — |
| Learning Curve (hours to 'Hello World')(hours) | 4-6 | — |
| Enterprise Adoption Rate(%) | ~15% | — |
| Base Framework Size(megabytes) | 11 MB | — |
| Built-in ORM | Django ORM included | — |
| Admin Panel | Auto-generated included | — |
| Learning Time to Proficiency(hours) | 50 hours | — |
| Development Speed (Median Project Timeline)(weeks) | 8-12 weeks | — |
| Installation Size(MB) | ~50 MB | — |
| Minimum PHP Version Required(version) | PHP 8.1+ | — |
| Market Share Among PHP Developers(%) | 64% of PHP frameworks | — |
LaravelShow 9 more attributes
Show 1 more attribute
Show 1 more attribute
Show 3 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
Django
Pros
- Auto-generated admin panel with full CRUD capabilities, saving 40+ hours per project
- Excellent ORM with complex query support and built-in migrations system
- Superior ML/AI integration via Python ecosystem (TensorFlow, PyTorch, Pandas)
- Strong security features including CSRF protection, SQL injection prevention out-of-the-box
- Larger community with 97k+ GitHub stars and extensive third-party packages
Cons
- Steeper learning curve for beginners (more configuration, more concepts to grasp)
- Slower initial page load times compared to Laravel (typically 30-50ms slower on standard benchmarks)
- Async functionality requires additional setup and is not the default paradigm
Laravel
Pros
- Eloquent ORM with intuitive, expressive syntax reducing development time by 20-30%
- Native async job queue system (Laravel Queue) built into the framework
- Fastest time-to-first-working-feature: average 5-7 days vs Django's 10-14 days
- Exceptional tooling ecosystem (Laravel Mix, Forge, Horizon, Nova) reducing setup overhead
- More intuitive routing and controller structure for developers from traditional backgrounds
Cons
- No built-in admin panel—requires third-party solutions (Nova $99/year, Filament free but feature-limited)
- Limited machine learning capabilities due to PHP's minimal data science library ecosystem
- Smaller job market (18% fewer positions than Django) and smaller community (66k GitHub stars vs Django's 97k)
Frequently Asked Questions
Django is significantly better for ML projects because Python is the dominant language in data science and machine learning. Django integrates seamlessly with TensorFlow, PyTorch, scikit-learn, and Pandas. Laravel, being PHP-based, lacks this ecosystem and would require calling external Python services, adding complexity and latency. If you're building AI-powered features, Django is the clear choice.
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