What Is The Best Language 🏆for AI Engineering❓

#ai #python #rust #go

I have had a few folks ask me about language choices in AI Engineering. Now before I get into this, there is never a one-size fits all answer, so much of it is based on many nuances, with a key one being the fluency of the individual or team in the choice of language.

We have also seen AI become commoditized where you don't need to understand complex math or neural networks, and instead it's about slinging requests around, handling concurrency and other common web-like programming paradigms.

Having said that , here are my views and why as someone who has historically been more of a static typed enthusiast - Python is coming out on top and its why I am building AgentUp in Python.

1 - Python

Massive AI/ML ecosystem (transformers, pytorch, OpenAI libs, etc.)
Performance when it matters - heavy computation happens in optimized C/CUDA libraries under the hood, so you get Python's simplicity for App logic + C's speed for AI operations
Decent async support for concurrent agents (I love FastAPI)
Yes, its dynamically typed, but Pydantic helps lots with data validation (and v2's Rust core from a performance standpoint)
Lots of strong frameworks, langchain, autogen, langgraph, pydantic AI and more.
Extensive libraries for common agent needs (web scraping, APIs, databases)
It's been the go to data science lang for years, so has a strong pipeline of research to product - e.g. jupyter -> prototype -> product.

No 2. Go

Go would be my second choice, especially if performance and deployment simplicity are priorities:

Excellent concurrency model with goroutines (ideal for multi-agent systems)
Single binary deployments with no dependency management headaches
Strong performance for I/O-heavy operations (common in agents)
Growing ecosystem for AI integrations
Excellent native networking (net/http is phenomenal and the gRPC ecosystem strength for microservices architectures)
No native CUDA bindings (I am not a fan of CGo).

No. 3 Rust

Rust ranks third , another strong statically typed language, with great tooling support (cargo ftw!)

Exceptional performance and memory safety
Excellent for systems-level work
But smaller AI ecosystem and steeper learning curve, but huggingface are building out more libraries there.
Development velocity would likely be slower
Better suited if you're building performance-critical infrastructure

Worthy Mention, Typescript!

TypeScript/Node.js/Deno deserves a mention for teams already in that ecosystem! Vercel's AI SDK is decent framework!

Of course there are many more nuances though. Certain languages can be applied where they are strong. If you're building Agents that deal with web APIs all day, you're not too concerned about gpu inference etc.

As with any x vs y post, I am sure I have opened a can of worms! My next topic will be vim vs emacs, just kidding