Elixir for real-time, fault-tolerant systems.
When a platform needs to handle thousands of concurrent connections, process events in real time, and never go down. Phoenix, OTP, and the BEAM VM.
elixir-lang.org ↗Elixir powers our most demanding backend systems. Platforms that process real-time data feeds, manage concurrent state across thousands of connections, and require near-zero downtime. We build on Phoenix for API layers, OTP for process supervision and fault tolerance, and Oban for reliable background job processing. Event-driven architectures with CQRS and event sourcing give us full auditability. ETS caching provides sub-millisecond lookups for time-sensitive operations. Every component runs under supervisor trees that self-heal on failure.
The BEAM VM was built for telecom systems requiring extreme uptime. That same foundation makes Elixir ideal for platforms where reliability is non-negotiable. OTP supervision trees mean individual process failures don't cascade. GenServer and DynamicSupervisor make concurrent connection management straightforward without callback hell or complex async patterns. Pattern matching eliminates entire categories of null reference bugs. Commanded gives us event sourcing with full audit trails. When we need a system that handles real-time data, concurrent users, and financial-grade reliability, Elixir is the only answer.
Elixir and Phoenix development for real-time platforms
Some platforms cannot afford to slow down or go dark, and that is the work we build in Elixir. When a system has to hold thousands of concurrent connections, process a live stream of events, and keep responding while individual parts fail, the BEAM VM that Elixir runs on is purpose-built for it: it was designed for telecom systems that measure downtime in seconds per year. We build the API layer on Phoenix, structure the system as supervised processes under OTP so a single failure heals itself instead of cascading, and use lightweight in-memory storage for the sub-millisecond lookups time-sensitive features depend on. Pattern matching removes a whole category of null and error-handling bugs at the language level. The result is a backend that stays responsive under real concurrency and recovers from faults on its own, which is exactly what real-time data platforms, live feeds, and always-on services require and what conventional request-per-thread stacks struggle to deliver.
When we reach for Elixir over Node
Elixir is a deliberate choice, not a default, and we are honest about when it earns its place. Most backends we build run happily on Node, where the single-language stack and vast ecosystem are the right trade. We reach for Elixir when the defining requirement is massive concurrency with near-zero downtime: tens of thousands of simultaneous connections, real-time event processing that cannot drop messages, or financial-grade reliability where a crash is unacceptable. The BEAM handles that class of workload natively, with supervision trees that isolate failures and a scheduler built for hundreds of thousands of lightweight processes, rather than bolting concurrency onto a runtime that was not designed for it. The trade-off is a smaller talent pool and ecosystem, so we choose Elixir where its strengths are the whole point of the project and stay on Node or Python where those strengths would go unused.
How we build with Elixir
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Model processes and supervision
We map the system into supervised processes under OTP, deciding what each owns and how failures are isolated. Designing the supervision tree first is what gives the platform its self-healing behavior, so a fault in one process does not take down the rest.
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Phoenix API layer
We build the API and real-time channels on Phoenix, using pattern matching to make request handling explicit and total. This eliminates a class of null and error cases at the language level and keeps the boundary between clients and the system clean.
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Concurrency and state
We manage concurrent connections and shared state with the platform native process model and in-memory storage, so lookups stay fast under load. Handling many simultaneous users is the runtime job here, not something we simulate with fragile async plumbing.
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Reliable background work
We run background jobs through a persistent queue with retry guarantees, so events are processed reliably even across restarts. Combined with supervision, this gives the platform the audit trail and durability that always-on systems require.
Real-Time Data Platform
GenServer-based feed consumers processing hundreds of events per second across concurrent SSE connections with automatic reconnection and circuit breakers.
Event-Sourced Backend
CQRS architecture with Commanded for domain event processing, complete audit trails, and reliable state transitions across distributed services.
High-Reliability API
Phoenix API with three-tier rate limiting, Oban job queues with retry guarantees, and ETS caching for sub-millisecond response times.
Systems we pair Elixir with
Node.js
Most of our backends run on Node, and Elixir is the specialist we bring in alongside it for the real-time, high-concurrency slice. The two coexist in a system, each owning the workloads it is suited to.
Python
When an Elixir platform needs AI or heavy data work, we pair it with a Python service that owns the model inference and pipelines, while Elixir handles the concurrent, real-time layer over a clean API boundary.
Railway
Where our persistent Elixir services deploy. A long-running container suits a system built to stay alive and hold thousands of connections open, rather than a function that spins up and exits per request.
AWS
We reach into AWS for durable storage and managed infrastructure that sits around an Elixir platform, keeping the concurrent, stateful compute on the BEAM while AWS handles the parts it does best.
