Best-of roundups for AI backend developers
Fair, evidence-based comparisons across boilerplates and frameworks — including honest pros and cons for options other than our own.
Best FastAPI Boilerplates
"Best FastAPI boilerplate" means different things depending on what you're building. A general-purpose SaaS API has different needs than an AI product that calls LLMs and runs RAG pipelines. Here's an honest breakdown of the main options, including where each one is genuinely the better choice.
Best Python Boilerplates
Python boilerplates split mainly along two axes: Django vs FastAPI, and general-purpose vs AI-specific. Which one is "best" depends entirely on what you're building — a subscription SaaS app looks nothing like an LLM-powered API under the hood.
Best AI Boilerplates
"AI boilerplate" spans a wide range: some are backend-only APIs, some are full-stack chat UIs, some are just LangChain quickstart templates. Here's how the main categories compare, so you pick based on what layer of the stack you actually need help with.
Best Backend Frameworks for AI Products
The "best backend framework" answer changes when the product is AI-native. Streaming responses, async LLM calls, and access to Python's ML ecosystem matter more than they would for a typical CRUD API. Here's how the main options stack up specifically for AI products.
Best Python Frameworks for AI Backends
Within Python specifically, the framework choice for an AI backend comes down to async support and streaming ergonomics — since every meaningful AI feature involves calling an external LLM API and often streaming the result back.