FastAPI (Python) vs Fiber (Go): raw performance vs AI ecosystem depth.
Fiber is a Go web framework built for extreme performance and low resource usage, modeled after Express.js. FastAPI is Python's async API framework, built where the AI/ML ecosystem actually lives. This comparison is fundamentally about a performance-first choice vs an ecosystem-first choice.
| Feature | FastAPI (Python) | Fiber (Go) |
|---|---|---|
| Raw throughput | Good, but Python has real overhead | Exceptional — Go compiles to native code |
| AI/ML ecosystem | Unmatched — Python owns AI tooling | Minimal — essentially none native to Go |
| LLM SDKs | Official, first-class Python SDKs | Community Go SDKs, smaller and newer |
| Memory footprint | Higher — Python interpreter overhead | Very low — compiled binary |
| Concurrency model | asyncio event loop | Goroutines — extremely lightweight |
| RAG/embedding libraries | LangChain, LlamaIndex, DSPy native | Essentially none — you'd call HTTP APIs directly |
| Type safety | Pydantic v2 + type hints | Native static typing (Go) |
| Where it excels | AI-native products with RAG/ML needs | Extremely high-throughput, low-latency proxies/gateways |
Our verdict
For a pure LLM API gateway with no RAG, no custom ML, and extreme throughput requirements, Fiber's performance is compelling. But the moment you need embeddings, RAG, fine-tuning, or any custom ML inference, you're back to calling out to a Python service anyway — at which point building in FastAPI directly is usually simpler than running two services.
The FastAPI AI Kit angle
FastAPI AI Kit is built for the common case: an AI product that needs RAG, LLM integration, and billing together, not a raw throughput proxy.
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