"Top of the table is FastAPI, a modern, high-performance web framework for building APIs with Python 3.8+. It was designed to combine Python's type hinting, asynchronous programming, and OpenAPI standards into a single, developer-friendly package. Built on top of Starlette (for the web layer) and Pydantic (for data validation), FastAPI offers automatic request validation, serialization, and interactive documentation, all with minimal boilerplate."
"Great for AI/ML: FastAPI is widely used to deploy machine learning models in production. It integrates well with libraries like TensorFlow, PyTorch, and Hugging Face, and supports async model inference pipelines for maximum throughput. Asynchronous by default: Built on ASGI, FastAPI supports native async/await, making it ideal for real-time apps, streaming endpoints, and low-latency ML services. Type-safe and modern: FastAPI uses Python's type hints to auto-validate requests and generate clean, editor-friendly code, reducing runtime errors and boosting team productivity."
Yearly surveys of Python developers capture ecosystem evolution across tooling, languages, frameworks, and libraries. Long-standing frameworks such as Django and Flask remain strong, while FastAPI has rapidly gained ground in AI, ML, and data science. FastAPI reached 38% usage in 2024, up 9% from 2023. FastAPI combines Python type hinting, async programming, and OpenAPI standards. FastAPI is built on Starlette and Pydantic, offering automatic request validation, serialization, and interactive documentation with minimal boilerplate. FastAPI integrates with TensorFlow, PyTorch, and Hugging Face, supports async model inference pipelines, and uses ASGI for native async/await and low-latency services.
Read at The JetBrains Blog
Unable to calculate read time
Collection
[
|
...
]