FastAPI is a Python web framework emphasizing automatic API documentation and async support, while Gin is a lightweight Go framework focused on performance and simplicity. FastAPI excels at rapid development with built-in validation, whereas Gin prioritizes raw speed and minimal overhead.
FastAPI vs Gin — Side-by-side comparison on A Versus B
Modern high-performance Python web framework with automatic API documentation and async support.
Lightweight Go web framework with minimal overhead and exceptional performance
Language. Startup Time (ms). Requests/Second (Benchmark). Built-in OpenAPI/Swagger. Data Validation. Development Speed. Memory Usage (MB)
Choose FastAPI if you prioritize rapid API development, automatic documentation, and built-in validation—ideal for startups, MVPs, and data-heavy applications. Choose Gin if you need maximum performance, minimal resource consumption, and prefer explicit control over your code—perfect for high-traffic services, microservices, and performance-critical systems.
FastAPI: Yes, built-in by default | Gin: N/A
FastAPI: Yes, OpenAPI/Swagger auto-generated | Gin: No (Manual)
FastAPI: 22% (Stack Overflow 2024) | Gin: N/A
FastAPI: Native (Swagger UI + ReDoc built-in) | Gin: N/A
FastAPI: Native (Python type hints) | Gin: N/A
FastAPI: Built-in (OpenAPI 3.0) | Gin: N/A
FastAPI: Native first-class support | Gin: N/A
FastAPI: Automatic (Swagger UI + ReDoc) | Gin: Manual setup required
FastAPI: Requires app server (Uvicorn) | Gin: Single compiled binary
FastAPI: Yes (Pydantic) | Gin: No (Manual)
FastAPI: 3.5 hours (manual setup required) | Gin: N/A
FastAPI: No (requires external library) | Gin: N/A
FastAPI: Native (async/await throughout) | Gin: N/A
FastAPI: ~480,000 packages (PyPI) | Gin: N/A
FastAPI: Manual setup required | Gin: N/A
FastAPI: Native, default behavior | Gin: N/A
FastAPI: 1,200KB (with uvicorn) | Gin: N/A
FastAPI: Native async/await with asyncio | Gin: N/A
FastAPI: High-level API framework (built on Starlette) | Gin: N/A
FastAPI: Pydantic (integrated) | Gin: N/A
FastAPI: Yes, Pydantic models with automatic validation | Gin: No (manual required)
FastAPI: 500,000+ PyPI packages (Python ecosystem) | Gin: N/A
FastAPI: 6 years (released 2018) | Gin: N/A
Production Applications (market estimate)
FastAPI: Yes (Swagger UI + ReDoc automatic) | Gin: N/A
FastAPI: Yes (Pydantic built-in) | Gin: N/A
FastAPI: No (requires FastAPI-Users, python-jose) | Gin: N/A
FastAPI: No (requires SQLAlchemy, Tortoise-ORM) | Gin: N/A
FastAPI: ~350,000 PyPI packages (FastAPI-specific: ~4,000) | Gin: N/A
FastAPI: 6 (starlette, pydantic, etc.) | Gin: N/A
FastAPI: Native async/await built-in | Gin: N/A
FastAPI: Automatic (Swagger UI + ReDoc) | Gin: N/A
Production Readiness Without External Server
FastAPI: Requires ASGI (Uvicorn) | Gin: Single Binary (Go)
FastAPI: N/A | Gin: None (use GORM separately)
FastAPI: N/A | Gin: Advanced (radix tree, middleware)
FastAPI vs Gin — Structured Comparison Data
Machine-readable comparison dataset for FastAPI vs Gin. Contains 119 attribute comparisons, 5 FAQ pairs.
FastAPI vs Gin — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for FastAPI vs Gin.
Which framework is faster: FastAPI or Gin?
Gin is significantly faster. Benchmarks show Gin handles 40,000-60,000 requests/second versus FastAPI's 8,000-12,000 req/s—a 4-6x difference. This is because Go compiles to native machine code with minimal runtime overhead, while Python requires interpretation. However, FastAPI is still sufficiently fast for most real-world applications (>10k req/s is enterprise-grade).
Does FastAPI automatically generate API documentation?
Yes, FastAPI automatically generates OpenAPI 3.0 (Swagger) documentation by visiting /docs or /redoc endpoints. This happens without any additional code due to FastAPI's use of Pydantic models and Python type hints. Gin has no built-in documentation—you must manually implement OpenAPI generation using third-party libraries like swag.
Can I deploy Gin and FastAPI the same way?
No. Gin compiles to a single binary executable that runs standalone (ideal for Docker/Kubernetes). FastAPI requires a Python ASGI server like Uvicorn or Hypercorn to run in production. Gin's approach is simpler for containerization and has no runtime dependencies, while FastAPI requires the Python runtime environment in your container.
Which is better for learning web development?
FastAPI is better for learning because it requires less boilerplate code, provides built-in validation, and generates documentation automatically. Gin forces you to write validation logic manually, which teaches more fundamentals but requires more code. If you're new to backend development, FastAPI gets you to a working API faster.
Should I use FastAPI or Gin for a production microservice?
For a high-traffic microservice expecting 10k+ req/s, Gin is superior due to 4-6x better throughput and lower memory footprint. For internal microservices with moderate traffic (<5k req/s), rapid development needs, or a Python-heavy stack, FastAPI is pragmatic. Consider Go if scale and performance are critical; choose Python if developer velocity and rapid iteration matter most.
Language: FastAPI has Python, while Gin has Go.
Startup Time (ms): FastAPI has 250-500ms, while Gin has 5-15ms.
Requests/Second (Benchmark): FastAPI has 8,000-12,000, while Gin has 40,000-60,000.
Built-in OpenAPI/Swagger: FastAPI has Yes (Automatic), while Gin has No (Manual Setup Required).
Data Validation: FastAPI has Pydantic (Automatic), while Gin has Manual Implementation.
Development Speed: FastAPI has Very Fast (High-level), while Gin has Fast (Low-level Control).
Memory Usage (MB): FastAPI has 50-100MB, while Gin has 10-20MB.