FastAPI vs Gin
FastAPI
Modern Python web framework for building REST APIs with automatic validation, serialization, and OpenAPI documentation.
Python teams, rapid API development, complex business logic, internal microservices, applications where time-to-market outweighs pure performance requirements
Gin
High-performance Go web framework optimized for speed and efficiency
High-throughput microservices, edge computing, serverless functions, performance-critical applications, teams with Go expertise, containerized deployments
Short Answer
FastAPI is a Python web framework emphasizing developer productivity with automatic API documentation, while Gin is a lightweight Go framework prioritizing raw performance and speed. FastAPI excels for rapid development and complex APIs, whereas Gin dominates in deployment efficiency and concurrent request handling.
Our Verdict
AI-assistedChoose FastAPI if you prioritize development velocity, need automatic API documentation, have a Python team, or are building complex business logic APIs with rapid iterations. Choose Gin if you need maximum throughput, minimal resource consumption, fast startup times, or are building microservices and edge applications where performance and deployment simplicity are critical.
Was this verdict helpful?
Choose FastAPI if
Python teams, rapid API development, complex business logic, internal microservices, applications where time-to-market outweighs pure performance requirements
Choose Gin if
High-throughput microservices, edge computing, serverless functions, performance-critical applications, teams with Go expertise, containerized deployments
Track this comparison
Get notified when prices change, new specs ship, or our verdict updates.
Triggers: price change new spec verdict update
No spam. Stop anytime.
Key Differences at a Glance
Key Facts & Figures
| Metric | FastAPI | Gin | Diff |
|---|---|---|---|
| Throughput (Requests/Second)(req/sec) | ~12,000 req/s | — | — |
| Startup Time(milliseconds) | ~50ms | — | — |
| Memory Usage (base)(MB) | ~10MB | — | — |
| Time to First API Endpoint(hours) | 1-2 hours | — | — |
| GitHub Stars | 75,000+ | 78,200 | -4% |
| Third-party Packages(packages) | 2,000+ packages | — | — |
| Latency (p99 response time)(ms) | 8-12 ms | — | — |
| Package Ecosystem Size(packages) | ~500K packages (PyPI) | — | — |
| Production Adoption Rate(%) | 22% (Stack Overflow 2024) | — | — |
| First Release Year | 2018 | — | — |
| Requests Per Second (Throughput)(req/sec) | ~12,000 | — | — |
| Related Packages (PyPI)(packages) | ~2,100 | — | — |
| Framework Requests Per Second(req/s) | 10,000 | 30,000 | -67% |
| Cold Start Latency(ms) | 175 | 7 | +2400% |
| Idle Memory Usage(MB) | 100 | 12 | +733% |
| Python/Go Package Ecosystem Size(packages) | 400,000+ | 150,000+ | +167% |
| Time to Production (Small API)(hours) | 4-8 | 12-24 | -67% |
| Package Size(KB) | ~100 KB | — | — |
| Average Latency (Hello World)(ms) | ~85 ms | — | — |
| PyPI Weekly Downloads(downloads) | ~2.8M (Jan 2026) | — | — |
| Time to Hello World API(minutes) | ~5 minutes | — | — |
| Throughput Performance(requests/second) | ~15,000 req/s | — | — |
| Memory Usage (Hello World)(MB) | ~40 MB | — | — |
| Throughput Benchmark (requests/sec)(req/s) | ~18,000 req/s | — | — |
| Framework Age(years) | 6 years (2018) | — | — |
| Stack Overflow Questions(thousands) | ~30,000 questions | — | — |
| Time to Build Basic CRUD App(minutes) | 3.5 hours (manual setup required) | — | — |
| Ecosystem Size (package repositories)(packages) | ~480,000 packages (PyPI) | — | — |
| Weekly NPM Downloads(millions) | ~1.2M (PyPI: ~2.8M) | — | — |
| Request Throughput(requests/second) | ~20,000 req/sec | — | — |
| Cold Start Time(milliseconds) | 300ms | — | — |
| Core Library Size(kilobytes) | 1,200KB (with uvicorn) | — | — |
| Available Packages/Libraries(count) | 450,000+ (PyPI) | — | — |
| Requests Per Second (Benchmark)(req/s) | ~20,000 req/s | ~20,000 req/s | — |
| Memory Usage (Single Instance)(MB) | 10 MB | 10 MB | — |
| Time to 'Hello World'(minutes) | 15 minutes | 15 minutes | — |
| Available Extensions/Packages(count) | 3,000+ packages | 3,000+ packages | — |
| Recommended Learning Duration(weeks) | 4-6 weeks | 4-6 weeks | — |
| Job Postings (Global, 2025)(jobs) | 8,200 positions | 8,200 positions | — |
| Production Deployments (Est.)(years in market) | 9+ years | 9+ years | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
FastAPI
Python with 400k+ packages (PyPI)🏆
Gin
Go with 150k+ packages
FastAPI
~150-200ms cold start
Gin
~5-10ms cold start🏆
FastAPI
8,000-12,000 req/s
Gin
25,000-35,000 req/s🏆
FastAPI
~80-120 MB per instance
Gin
~10-15 MB per instance🏆
FastAPI
Automatic Swagger UI + ReDoc🏆
Gin
Manual setup required
FastAPI
Moderate (Python syntax friendly)🏆
Gin
Steep (requires Go knowledge)
FastAPI
Requires app server (Uvicorn/Gunicorn)
Gin
Single binary executable🏆
Full Comparison
| Attribute | FastAPI |  Gin |
|---|---|---|
| Throughput (Requests/Second)(req/sec) | ~12,000 req/s | — |
| Startup Time(milliseconds) | ~50ms | — |
| Memory Usage (base)(MB) | ~10MB | — |
| Latency (p99 response time)(ms) | 8-12 ms | — |
| Requests Per Second (Throughput)(req/sec) | ~12,000 | — |
Show 11 more attributesFramework Requests Per Second(req/s) 10,000 30,000 Cold Start Latency(ms) 175 7 Idle Memory Usage(MB) 100 12 Package Size(KB) ~100 KB — Average Latency (Hello World)(ms) ~85 ms — Throughput Performance(requests/second) ~15,000 req/s — Memory Usage (Hello World)(MB) ~40 MB — Throughput Benchmark (requests/sec)(req/s) ~18,000 req/s — Request Throughput(requests/second) ~20,000 req/sec — Cold Start Time(milliseconds) 300ms — Requests Per Second (Benchmark)(req/s) ~20,000 req/s — | ||
| Time to First API Endpoint(hours) | 1-2 hours | — |
| Time to Build Basic CRUD App(minutes) | 3.5 hours (manual setup required) | — |
| Built-in Admin Dashboard | No, requires build | — |
| Async Request Support | Full native support | — |
| Auto API Documentation | Native (Swagger UI + ReDoc built-in) | — |
| Built-in Data Validation | Pydantic included | — |
| Built-in Request Validation | Yes (Pydantic native) | — |
Show 5 more attributesBuilt-in ORM No (requires external library) — Automatic API Documentation Yes (Swagger UI + ReDoc built-in) — Native Async Support Native (async/await throughout) — Auto-generated API Documentation Yes (automatic) — Built-in ORM Support None (use GORM separately) — | ||
| GitHub Stars | 75,000+ | 78,200 |
| Third-party Packages(packages) | 2,000+ packages | — |
| Package Ecosystem Size(packages) | ~500K packages (PyPI) | — |
| Related Packages (PyPI)(packages) | ~2,100 | — |
| Python/Go Package Ecosystem Size(packages) | 400,000+ | 150,000+ |
| Ecosystem Size (package repositories)(packages) | ~480,000 packages (PyPI) | — |
Show 2 more attributesAvailable Packages/Libraries(count) 450,000+ (PyPI) — Available Extensions/Packages(count) 3,000+ packages — | ||
| Production Adoption Rate(%) | 22% (Stack Overflow 2024) | — |
| PyPI Weekly Downloads(downloads) | ~2.8M (Jan 2026) | — |
| First Release Year | 2018 | — |
| Framework Age(years) | 6 years (2018) | — |
| Production Deployments (Est.)(years in market) | 9+ years | — |
| Type Safety Support | Native Python type hints with validation | — |
| Auto-Documentation Support | Built-in (OpenAPI 3.0) | — |
| Built-in Documentation Generation | Automatic (Swagger UI + ReDoc) | Manual setup required |
| Time to Hello World API(minutes) | ~5 minutes | — |
| Time to 'Hello World'(minutes) | 15 minutes | — |
Show 1 more attributeRecommended Learning Duration(weeks) 4-6 weeks — | ||
| Native Async/Await Support | Full native support | — |
| Minimum Python Version(version) | Python 3.6+ | — |
| Deployment Model | Requires app server (Uvicorn) | Single compiled binary |
| Time to Production (Small API)(hours) | 4-8 | 12-24 |
| Python Version Support | 3.7+ | — |
| Stack Overflow Questions(thousands) | ~30,000 questions | — |
| Weekly NPM Downloads(millions) | ~1.2M (PyPI: ~2.8M) | — |
| Built-in Dependency Injection(included) | Manual setup required | — |
| Async-First Support | Native, default behavior | — |
| Core Library Size(kilobytes) | 1,200KB (with uvicorn) | — |
| Async Support Quality | Native async/await with asyncio | — |
| Memory Usage (Single Instance)(MB) | 10 MB | — |
| Job Postings (Global, 2025)(jobs) | 8,200 positions | — |
GinShow 11 more attributes
Show 5 more attributes
Show 2 more attributes
Show 1 more attribute
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
FastAPI
Pros
- Automatic interactive API documentation (Swagger UI + ReDoc) with zero configuration
- Native async/await support for concurrent request handling without threading
- Built-in data validation using Pydantic with automatic OpenAPI schema generation
- Lower time-to-market for MVP and rapid prototyping
- Massive ecosystem with 400k+ third-party Python packages
Cons
- Significantly slower performance (8-12k req/s vs Gin's 25-35k req/s)
- Requires additional app server (Uvicorn/Gunicorn) and deployment complexity
Gin
Pros
- Exceptional performance: 25-35k req/s with minimal resource overhead
- Instant startup time (~5-10ms) suitable for serverless and edge computing
- Single compiled binary deployment with no runtime dependencies
- Minimal memory footprint (~10-15 MB idle) for cost-effective scaling
- Built-in middleware ecosystem and simplified routing with clear syntax
Cons
- No automatic API documentation; requires manual Swagger/OpenAPI setup
- Steeper learning curve for teams without Go expertise
Frequently Asked Questions
Gin is significantly faster, handling 25-35k requests per second compared to FastAPI's 8-12k req/s. Gin also has a cold start time of ~7ms versus FastAPI's ~175ms. However, for typical business applications (under 5k req/s), both frameworks perform adequately.
Resources & Learn More
Dive deeper with these curated resources
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more
Wikipedia
Related Comparisons
Django vs FastAPI
software
Flask vs FastAPI
software
FastAPI vs Express.js
software
FastAPI vs Flask
software
FastAPI vs Starlette
software
Rails vs FastAPI
software
FastAPI vs NestJS
software
Flask vs Gin: Python Web Frameworks
software
Express vs FastAPI
software
WordPress vs Wix
software
Slack vs Microsoft Teams
software
Canva vs Photoshop
software
Related Articles
Best Streaming Services in 2026: Top Picks for Every Budget & Interest
Navigating the crowded streaming landscape in 2026 can be overwhelming. We've tested and ranked the best streaming services that offer the most value, from Netflix's massive library to budget-friendly options like Tubi, helping you cut cable and find your perfect entertainment solution.
Best Live TV Streaming Services & Plans for Spring 2026: Complete Buyer's Guide
Tired of overpaying for cable? Discover the best live TV streaming services and plans for Spring 2026, including YouTube TV's new genre-based packages starting at $55/month. Our comprehensive guide breaks down pricing, channels, and features to help you cut the cord.
Philo in 2026: Streaming TV Service Review, Pricing & Reddit Community Insights
Explore Philo's evolution heading into 2026, including pricing tiers, channel lineup, and how it compares to competitors like Sling TV. Discover what the r/PhiloTV Reddit community thinks about the service's current offerings and future prospects.
Best US Fighter Jets 2026: Top American Combat Aircraft Ranked
Discover the most advanced US fighter jets dominating the skies in 2026. From the legendary F-22 Raptor to the versatile F-35 Lightning II, we rank America's best combat aircraft based on performance, stealth, and air superiority capabilities.
Philo in 2026: Pricing, Lineup & How It Compares to Sling TV
As we head into 2026, Philo continues to position itself as an affordable streaming alternative for cable TV lovers. Discover what Philo offers, how its pricing stacks up against competitors like Sling TV, and what the Reddit community thinks about its future.