Why Elixir Provides a Perfect Stack for TaskLift
At TaskLift, we're building a new app & platform for simple AI chatting & assistance, with huge flexibility & convenience when it comes to model and tool selection, and a few surprisingly powerful features such as all-around API support and sandboxed computer hosting with persistent drives and multi-language code execution.
We knew that building an app like this - whose main goal is to provide a simple & unified UI, but with ambition to pack some serious features underneath, in the AI market that's possibly the most dynamic in history - requires extra care when it comes to picking a technology stack. We had to move fast, we desired to look fine, and we wanted to grow with ambition.
After diving deep into the ecosystem and working with many technologies across the years, we've picked Elixir as a perfect foundation for TaskLift. Here's why this functional powerhouse has become our technology of choice.
Rapid Development With Phoenix, LiveView And Tailwind
Let's be honest: most web development involves way too much ceremony. Here's the recap:
- You write backend APIs, then frontend code to consume them, then spend hours wrestling with contract inconsistencies, state synchronization or versioning.
- You jump hoops to ensure your frontend is secure even though all the keys to the kingdom live on the backend, or leave some pieces secured by a duct tape in a hurry.
- You pull in library after library, even framework after framework, to fill all the gaps and find the perfect combo, that nobody else is using in the same shape.
- Then you kickstart test automation only to discover how much overhead it is to write, run, debug and integrate tests with JS-enabled browser.
- And sometimes, depending on your choices, you figure out that your final app weights tens of megabytes and lacks server-side rendering so it's a SEO nightmare.
Phoenix LiveView throws this entire dance out the window.
With LiveView, your server-rendered HTML becomes genuinely interactive without the typical React/Vue complexity. Everything updates in the real-time even though you write it once - on the server side, together with business logic and database access. As such it also can often be tested, including UI, on the server side as well. Add Tailwind to the mix, and you're styling directly in your templates fast & smooth, without fighting CSS syntax or context switching.
This stack lets us iterate lightning-fast with a small team, which is a huge win at the bootstrapping phase because it enabled us to minimize organizational overhead, cutting our costs and time to market. And all of this without sacrificing design or security - so important when you want to facilitate conversations conducted by real humans.
It's also worth noting that LiveView - until recently the most "bleeding-edge" part of this picture - has now matured into a stable 1.0 release followed by further valuable iterations. Changes such as switching to the lean "braces" syntax, support for async tasks or for portals have made the framework pretty complete now. Phoenix itself also keeps getting better with the latest Tailwind, integrated DaisyUI, more refined baked-in auth and more.
There’s never been a better time than now to go for this stack.
Perfect Model for Agentic AI Workflows
Here's where Elixir really shines. The Actor model, powered by GenServers and supervised by OTP, creates cheap, isolated, fault-tolerant processes that communicate via message passing. For TaskLift's shared agentic AI chat features, this architecture is a natural perfect match.
Heck, the AI space is currently inventing the craziest things to spawn autonomous AI agents and to let them talk to each other in order to work in group. And guess what, this is exactly how Erlang and Elixir work - here, you like it or not, everything runs on agent-like processes that can easily and safely talk even across machines. It was designed this way years before the AI fever.
Each AI conversation can run in its own GenServer, maintaining state while remaining completely isolated from other chats. If one conversation hits an edge case and crashes, the supervisor handles it without breaking a sweat while other conversations continue unaffected. If they need to talk to each other, they send messages without the dread of mutexes or locks. Background processes for AI inference, MCP calls, hosting lifecycle or any other automation - they all get their own supervised actors. And we can run a ton of them cheaply, especially if majority of the effort is interacting with various APIs and passing data through.
This isn't just theoretical - we've seen this pattern handle millions of concurrent processes, first in telecom applications (long before smartphones took over), then in WhatsApp (long before Facebook took it over 😉). For us it means an architecture that naturally maps to the problem space and that offers rock-solid reliability even as workloads get heavy.
Scalability Without the Headaches with BEAM
The Erlang Virtual Machine (BEAM) was designed for large-scale telecom systems that can't go down or hiccup. Ever. It's simple and elegant unlike behemoths like Java. And it was born decades ago which gave it time to mature over the years. This translates into serious scalability & resilience advantages for modern applications.
Vertically, BEAM automatically distributes work - including massive amounts of lightweight processes - across all available CPU cores, while still letting them communicate as per Actor model mentioned earlier. You don't need to architect around thread pools or worry about the Global Interpreter Lock - the runtime handles concurrency for you without the risk of other processes getting blocked.
Horizontally, clustering multiple machines into logically connected whole is a piece of cake (a cookie actually). This lets TaskLift scale seamlessly from a single server to a distributed cluster without architectural rewrites. We can freely distribute AI processes to multiple machines and we can assign specific machines to specific tasks if needed to further improve isolation & resource usage.
Native Performance with Rust
Sometimes you need to drop down to native performance, and Elixir doesn't lock you into VM-only solutions. With Rustler, we can enhance TaskLift with performance-critical components in Rust and call them seamlessly from Elixir code without any overhead.
Our team has extensive experience mixing Rust with Elixir for computationally intensive tasks with great success. In TaskLift specifically, whether it's document processing or cryptographic operations, Rustler lets us optimize bottlenecks without abandoning the benefits of the BEAM.
This is what we believe "the best of both worlds" truly means.
Stack That Developers Actually Love
The 2024 Stack Overflow Developer Survey tells the story: Phoenix consistently ranks as one of the most admired web frameworks, while Elixir maintains high developer satisfaction scores year after year. This isn't just marketing fluff - developers genuinely enjoy working with these tools.
Similar data exists for Rust as well, applying to the parts of the TaskLift stack that receive native treatment, completing the picture of what some would call a "dream stack". We don't care for terms like that but for sure we believe it's one that lets developers grow while building reliable and scalable apps. Which indeed does make it our dream stack.
This means we can attract top talent and maintain high team productivity. When your stack is enjoyable to work with, code quality improves and shipping velocity increases.
There's also the elephant in the room - AI entering the stage as a development force - especially uncomfortable for AI product, right? Well, not for us. We use AI as a supporting tool in development too, but above everything we value the work of our engineers. We hope that this stack will make our development story enjoyable. And for the parts where we can use AI to aid us, the Elixir ecosystem looks promising too - emerging tools like Tidewave provide ways to hook AI for coding assistance directly into the app for deeper understanding and enhanced impact.
Bottom Line
Choosing a tech stack is ultimately about trade-offs, but with Elixir, we've found remarkably few downsides. The combination of rapid development cycles, problem-fitting model, fault-tolerant concurrency, seamless scalability, native performance escape hatches and developer experience - it all creates a foundation that can grow with TaskLift from prototype to platform.
For TaskLift, Elixir isn't just a technology choice - it's our huge competitive advantage.
Ready to experience the power of Elixir-driven AI productivity? Try TaskLift today and see what fault-tolerant, scalable task management feels like. Or break it 😉 - a win for us too, helping us pave the early-stage path & get there).
