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.
Best RAG Frameworks for Python
Building a RAG (Retrieval-Augmented Generation) pipeline means choosing between orchestration frameworks like LangChain and LlamaIndex, or building a custom pipeline with direct SDK calls. Each approach has real tradeoffs in complexity, performance, and flexibility.
Best LLM API Wrapper Libraries
Calling LLM APIs from Python means choosing between official SDKs (OpenAI, Anthropic), multi-provider abstraction libraries (LiteLLM, aisuite), or building your own thin wrapper. Each approach trades off simplicity, flexibility, and vendor independence.
Best FastAPI Extensions and Plugins
FastAPI's plugin ecosystem is growing rapidly. These are the extensions and libraries that actually matter for production AI backend development — not a comprehensive list, but the ones that solve real problems.
Best Vector Databases for AI Apps
Vector databases store embeddings and enable similarity search — the core of RAG pipelines, semantic search, and recommendation systems. The choice between managed services, self-hosted solutions, and PostgreSQL extensions has major implications for cost, performance, and operational complexity.
Best API Starter Kits (Cross-Framework)
API starter kits and boilerplates save weeks of setup time by providing pre-configured auth, billing, database access, and deployment. Here's an honest comparison across languages and frameworks — each has a genuine sweet spot.