Python is the dominant language for AI development, and for good reason, but "besides Python" is a genuinely useful question, because different parts of a real production AI system are often better served by a different language than the one used for model development itself. Understanding where each language actually fits avoids the mistake of assuming Python is the only, or always the best, choice for every part of the system.
Why Python dominates model development specifically
The AI and machine learning ecosystem, libraries, frameworks, research tooling, is overwhelmingly built in and for Python, which means it remains the practical default for model training, fine-tuning, and experimentation, not because it's technically superior at raw performance, but because the tooling ecosystem is simply built around it.
Where other languages genuinely fit better
JavaScript and TypeScript, for the application layer
If an AI feature is being integrated into a web application, building the actual user-facing product, the API layer, the frontend, in JavaScript or TypeScript, while calling out to model inference through an API, is often the more practical architecture than trying to build the whole application in Python. This is the common real-world pattern: Python for the model or the model-calling backend logic, JavaScript or TypeScript for the actual product experience.
Rust or Go, for performance-critical infrastructure
Parts of an AI system that need to handle high throughput with low latency and predictable performance, serving infrastructure, certain data pipeline components, some vector database implementations, are increasingly built in Rust or Go, languages that offer performance characteristics Python's interpreted nature doesn't match, even with optimization.
SQL, for the data layer underneath everything
Regardless of what language the AI system itself is written in, the data it depends on usually lives in a structured database, and SQL remains the practical, necessary skill for querying, preparing, and managing that underlying data, a skill that doesn't disappear just because the AI layer is elsewhere.
Swift and Kotlin, for on-device mobile inference
For AI features that run genuinely on-device on a phone rather than calling out to a cloud API, native mobile development languages, Swift for iOS, Kotlin for Android, are what's actually used to integrate on-device inference into a real mobile app, a different concern entirely from training the model that gets deployed there.
Why "what language" is really "what part of the system"
A real production AI system usually isn't written in a single language. It's Python (or occasionally another language) for the model itself, an application language for the product built around it, possibly a performance-focused language for specific infrastructure components, and SQL underneath all of it for the data layer. Asking "what language is best for AI" without specifying which part of the system misses that a real system is usually a composition of several languages, each doing what it's actually best suited for.
How we approach this
We choose the language for each part of an AI system based on what that specific part actually needs, Python for model work, an application language for the product layer, a performance-focused language where throughput genuinely matters, rather than forcing the whole system into one language regardless of fit.