Exploring Elixir's Strengths for AI Applications
When we think about AI tools - like chatbots, smart recommendations, or live dashboards - Python and JavaScript usually come to mind. They’re popular, come with lots of libraries, and are common choices for AI work.
But there’s another option worth considering: Elixir. Built on Erlang, it’s fast, reliable, and made for systems that stay up - even when parts fail. Elixir handles many tasks at once, recovers from errors, and scales smoothly - just what real-world AI apps need.
In this article, we’ll explore why Elixir is a smart choice for AI projects, with real examples and practical takeaways - no hype, just honest insight.
Built for Concurrency — Think Streams, Not Threads
AI apps often deal with lots of data at once - chat messages, sensor inputs, user activity. Elixir handles this kind of load really well. Instead of heavy threads, Elixir runs small, lightweight processes that work in parallel. These are cheap to run and don’t get in each other’s way. If one fails, the rest keep going.
That’s perfect for AI pipelines where you’re constantly ingesting, transforming, and analyzing data in real time. You don’t have to worry about one small failure breaking everything.
Example: The WHO COVID‑19 Hotline was built with Elixir in just five days and handled over 7.5 million users. It’s a great example of how Elixir supports large-scale, real-time systems under pressure.
Fault Tolerance When Failures Aren’t Fatal
Things break. Especially in AI systems, where models can behave strangely, data can be messy, or services might stop responding. Elixir is built to handle this. Its “let it crash” philosophy means that when something goes wrong, the system doesn’t panic. Instead, a supervisor process quietly restarts the failing part.
This makes Elixir web development a great choice when you want to build apps that stay running - even when parts of them don’t.
Example: In no-code platforms like Betty Blocks, Elixir’s fault-tolerant design ensures AI workflow components can fail and recover independently - keeping automation reliable and on-air even when underlying processes crash. Their backend migration from Ruby to Elixir leverages supervisors to isolate and restart failed modules, preserving user experience during critical business automations.
Real-Time UI with Phoenix LiveView
AI apps often need to show live results: streaming metrics, chatbot responses, or predictions as you type. Elixir’s Phoenix LiveView lets you build real-time web pages with zero JavaScript. You write regular Elixir, and updates appear instantly in the browser through WebSockets.
Teams building internal AI tools use LiveView to show real-time model performance - like error rates or throughput - without touching front-end frameworks. It makes Elixir software development faster and easier.
Example: In March 2024, Benjamin Reinhart shared a guide on Integrating Phoenix LiveView with OpenAI to stream chat completions in real time. The setup lets the server call the OpenAI API and push response chunks to the UI immediately - no JavaScript frontend needed.
Data Pipelines with GenStage and Flow
AI systems don’t just make predictions - they move data from one stage to the next. Elixir has great tools for this. GenStage helps you build producer-consumer pipelines with back-pressure (so the system doesn’t overload), and Flow gives you map/filter/reduce-style operations across multiple cores or machines.
Example: In monitoring platforms like WombatOAM, Elixir’s GenStage and Flow enable real-time anomaly detection by efficiently processing telemetry data streams with back-pressure and parallel transformations - providing a scalable, maintainable ML-powered monitoring solution.
Functional Clarity for AI Logic
Elixir is a functional language, which means it encourages clean, predictable code. In AI, where you often deal with transformations and algorithms, that’s a big help. Data is immutable, functions are pure, and pattern matching makes code easier to understand.
Example: In AI orchestration platforms like V7 (a computer vision AI platform), Elixir’s functional clarity lets developers reliably manage data pipelines, queue tasks, and handle concurrency with clean, testable logic - crucial for complex AI workflows.
Integrating Existing AI Systems
Elixir isn’t here to replace Python, TensorFlow, or PyTorch. Instead, it works well alongside them. You can connect Elixir apps to Python scripts, external APIs, or message queues like RabbitMQ and Kafka.
That means you can keep using your current AI models - but manage them with Elixir’s reliability and speed. It’s like using Elixir as the brain that keeps all the moving parts working smoothly.
Example: The above-mentioned V7 uses Elixir to orchestrate numerous Python nodes for tasks like image training and video annotation. The Elixir API, written in a clear functional style, controls workflows and distributes work between services with predictable logic and minimal bugs.
Conclusion
Elixir might not be the first language you think of for AI - but maybe it should be. It’s built for:
- Real-time data pipelines
- Systems that keep running, even when parts fail
- Live dashboards with no front-end hassle
- Simple, readable logic for complex tasks
- Smooth connections to Python and other AI tools
If you’ve ever run into trouble with scaling, crashes, or overly complex code in your AI apps, Elixir is worth a try. It won’t replace your models - but it’ll help you run them in production with fewer problems and more peace of mind.
You might just find yourself enjoying your codebase again. Our Elixir dedicated team can help with that.
