Learn why Elixir and the BEAM VM fit real-time apps: lightweight processes, OTP supervision, fault tolerance, Phoenix, and key trade-offs.

“Real-time” is often used loosely. In product terms, it usually means users see updates as they happen—without refreshing the page or waiting for a background sync.
Real-time shows up in familiar places:
What matters is perceived immediacy: updates arrive quickly enough that the UI feels live, and the system stays responsive even when many events are flowing.
“Highly concurrent” means the app must handle many simultaneous activities—not just high traffic in bursts. Examples include:
Concurrency is about how many independent tasks are in flight, not only requests per second.
Traditional thread-per-connection or heavy thread-pool models can hit limits: threads are relatively expensive, context switching grows under load, and shared-state locking can create slowdowns that are hard to predict. Real-time features also keep connections open, so resource usage accumulates instead of being released after each request.
Elixir on the BEAM VM isn’t magic. You still need good architecture, sensible limits, and careful data access. But the actor-model style concurrency, lightweight processes, and OTP conventions reduce common pain points—making it easier to build real-time systems that stay responsive as concurrency climbs.
Elixir is popular for real-time and highly concurrent apps because it runs on the BEAM virtual machine (the Erlang VM). That matters more than it might sound: you’re not just choosing a language syntax—you’re choosing a runtime built to keep systems responsive while many things happen at once.
BEAM has a long history in telecom, where software is expected to run for months (or years) with minimal downtime. Those environments pushed Erlang and the BEAM toward practical goals: predictable responsiveness, safe concurrency, and the ability to recover from failures without taking the whole system down.
That “always-on” mindset carries directly into modern needs like chat, live dashboards, multiplayer features, collaboration tools, and streaming updates—anywhere you have lots of simultaneous users and events.
Instead of treating concurrency as an add-on, BEAM is built to manage large numbers of independent activities concurrently. It schedules work in a way that helps avoid one busy task freezing everything else. As a result, systems can keep serving requests and pushing real-time updates even under load.
When people talk about “the Elixir ecosystem,” they usually mean two things working together:
That combination—Elixir on top of Erlang/OTP, running on BEAM—is the foundation that later sections build on, from OTP supervision to Phoenix real-time features.
Elixir runs on the BEAM virtual machine, which has a very different idea of “a process” than your operating system does. When most people hear process or thread, they think of heavyweight units managed by the OS—something you create sparingly because each one costs noticeable memory and setup time.
BEAM processes are lighter: they’re managed by the VM (not the OS) and designed to be created by the thousands (or more) without your app grinding to a halt.
An OS thread is like reserving a table in a busy restaurant: it takes space, it needs staff attention, and you can’t realistically reserve one per person walking by. A BEAM process is more like giving someone a ticket number: cheap to hand out, easy to track, and you can manage a huge crowd without needing a table for everyone.
Practically, that means BEAM processes:
Because processes are cheap, Elixir apps can model real-world concurrency directly:
This design feels natural: instead of building complex shared state with locks, you give each “thing that happens” its own isolated worker.
Each BEAM process is isolated: if a process crashes due to bad data or an unexpected edge case, it doesn’t take down other processes. A single misbehaving connection can fail without knocking offline every other user.
That isolation is a key reason Elixir holds up under high concurrency: you can scale the number of simultaneous activities while keeping failures localized and recoverable.
Elixir apps don’t rely on many threads poking at the same shared data structure. Instead, work is split into lots of small processes that communicate by sending messages. Each process owns its own state, so other processes can’t directly mutate it. That single design choice eliminates a huge class of shared-memory problems.
In shared-memory concurrency, you typically protect state with locks, mutexes, or other coordination tools. That often leads to tricky bugs: race conditions, deadlocks, and “it only fails under load” behavior.
With message passing, a process updates its state only when it receives a message, and it handles messages one at a time. Because there’s no simultaneous access to the same mutable memory, you spend far less time reasoning about lock ordering, contention, or unpredictable interleavings.
A common pattern looks like this:
This maps naturally to real-time features: events stream in, processes react, and the system stays responsive because work is distributed.
Message passing doesn’t magically prevent overload—you still need backpressure. Elixir gives you practical options: bounded queues (limit mailbox growth), explicit flow control (only accept N in-flight tasks), or pipeline-style tooling that regulates throughput. The key is you can add these controls at process boundaries, without introducing shared-state complexity.
When people say “Elixir is fault-tolerant,” they’re usually talking about OTP. OTP isn’t one magic library—it’s a set of proven patterns and building blocks (behaviours, design principles, and tooling) that help you structure long-running systems that recover gracefully.
OTP encourages you to split work into small, isolated processes with clear responsibilities. Instead of one huge service that must never fail, you build a system of many tiny workers that can fail without taking everything down.
Common worker types you’ll see:
Supervisors are processes whose job is to start, monitor, and restart other processes (“workers”). If a worker crashes—maybe due to a bad input, a timeout, or a transient dependency issue—the supervisor can restart it automatically according to a strategy you choose (restart one worker, restart a group, back off after repeated failures, and so on).
This creates a supervision tree, where failures are contained and recovery is predictable.
“Let it crash” doesn’t mean ignoring errors. It means you avoid complex defensive code inside every worker and instead:
The result is a system that keeps serving users even when individual pieces misbehave—exactly what you want in real-time, high-concurrency apps.
“Real-time” in most web and product contexts usually means soft real-time: users expect the system to respond quickly enough that it feels immediate—chat messages show up right away, dashboards refresh smoothly, notifications arrive within a second or two. Occasional slow responses can happen, but if delays become common under load, people notice and lose trust.
Elixir runs on the BEAM VM, which is built around lots of small, isolated processes. The key is the BEAM’s preemptive scheduler: work is split into tiny time slices, so no single piece of code can hog the CPU for long. When thousands (or millions) of concurrent activities are happening—web requests, WebSocket pushes, background jobs—the scheduler keeps rotating through them and giving each a turn.
This is a major reason Elixir systems often maintain a “snappy” feel even when traffic spikes.
Many traditional stacks lean heavily on OS threads and shared memory. Under heavy concurrency, you can hit thread contention: locks, context switching overhead, and queueing effects where requests start piling up. The result is often higher tail latency—those random multi-second pauses that frustrate users even if the average looks fine.
Because BEAM processes don’t share memory and communicate via message passing, Elixir can avoid many of these bottlenecks. You still need good architecture and capacity planning, but the runtime helps keep latency more predictable as load increases.
Soft real-time is a great fit for Elixir. Hard real-time—where missing a deadline is unacceptable (medical devices, flight control, certain industrial controllers)—typically requires specialized operating systems, languages, and verification approaches. Elixir can participate in those ecosystems, but it’s rarely the core tool for strict, guaranteed deadlines.
Phoenix is often the “real-time layer” people reach for when building on Elixir. It’s designed to keep live updates simple and predictable, even when thousands of clients are connected at once.
Phoenix Channels give you a structured way to use WebSockets (or long-polling fallback) for live communication. Clients join a topic (for example, room:123), and the server can push events to everyone in that topic or respond to individual messages.
Unlike hand-rolled WebSocket servers, Channels encourage a clean message-based flow: join, handle events, broadcast. This keeps features like chat, live notifications, and collaborative editing from turning into a tangle of callbacks.
Phoenix PubSub is the internal “broadcast bus” that lets parts of your app publish events and other parts subscribe—locally or across nodes when you scale out.
Real-time updates usually aren’t triggered by the socket process itself. A payment settles, an order status changes, a comment is added—PubSub lets you broadcast that change to all interested subscribers (channels, LiveView processes, background jobs) without tightly coupling everything together.
Presence is Phoenix’s built-in pattern for tracking who is connected and what they’re doing. It’s commonly used for “online users” lists, typing indicators, and active editors on a document.
In a simple team chat, each room can be a topic like room:42. When a user sends a message, the server persists it, then broadcasts via PubSub so every connected client instantly sees it. Presence can show who’s currently in the room and whether someone is typing, while a separate topic like notifications:user:17 can push “you were mentioned” alerts in real time.
Phoenix LiveView lets you build interactive, real-time user interfaces while keeping most of the logic on the server. Instead of shipping a large single-page app, LiveView renders HTML on the server and sends small UI updates over a persistent connection (typically WebSockets). The browser applies these updates instantly, so pages feel “live” without you manually wiring up lots of client-side state.
Because the source of truth stays on the server, you avoid many of the classic pitfalls of complex client applications:
LiveView also tends to make real-time features—like updating a table when data changes, showing live progress, or reflecting presence—feel straightforward because updates are just part of the normal server-rendered flow.
LiveView shines for admin panels, dashboards, internal tools, CRUD apps, and form-heavy workflows where correctness and consistency matter. It’s also a strong choice when you want a modern interactive experience but prefer a smaller JavaScript footprint.
If your product needs offline-first behavior, extensive work while disconnected, or highly custom client rendering (complex canvas/WebGL, heavy client-side animations, deep native-like interactions), a richer client app (or native) may be a better fit—possibly paired with Phoenix as an API and real-time backend.
Scaling a real-time Elixir app usually starts with one question: can we run the same application on multiple nodes and have them behave like one system? With BEAM-based clustering, the answer is often “yes”—you can bring up several identical nodes, connect them into a cluster, and distribute traffic through a load balancer.
A cluster is a set of Elixir/Erlang nodes that can talk to each other. Once connected, they can route messages, coordinate work, and share certain services. In production, clustering typically relies on service discovery (Kubernetes DNS, Consul, etc.) so nodes can find each other automatically.
For real-time features, distributed PubSub is a big deal. In Phoenix, if a user connected to Node A needs an update triggered on Node B, PubSub is the bridge: broadcasts replicate across the cluster so every node can push updates to its own connected clients.
This enables true horizontal scaling: adding nodes increases total concurrent connections and throughput without breaking real-time delivery.
Elixir makes it easy to keep state inside processes—but once you scale out, you must be deliberate:
Most teams deploy with releases (often in containers). Add health checks (liveness/readiness), ensure nodes can discover and connect, and plan for rolling deploys where nodes join/leave the cluster without dropping the whole system.
Elixir is a strong fit when your product has lots of simultaneous “small conversations” happening at once—many connected clients, frequent updates, and a need to keep responding even when parts of the system misbehave.
Chat and messaging: Thousands to millions of long-lived connections are common. Elixir’s lightweight processes map naturally to “one process per user/room,” keeping fan-out (sending one message to many recipients) responsive.
Collaboration (docs, whiteboards, presence): Real-time cursors, typing indicators, and state sync create constant update streams. Phoenix PubSub and process isolation help you broadcast updates efficiently without turning your code into a tangle of locks.
IoT ingestion and telemetry: Devices often send small events continuously, and traffic can spike. Elixir handles high connection counts and backpressure-friendly pipelines well, while OTP supervision makes recovery predictable when a downstream dependency fails.
Gaming backends: Matchmaking, lobbies, and per-game state involve many concurrent sessions. Elixir supports fast, concurrent state machines (often “one process per match”) and can keep tail latency under control during bursts.
Financial alerts and notifications: Reliability matters as much as speed. Elixir’s fault-tolerant design and supervision trees support systems that must stay up and continue processing even when external services time out.
Ask:
Define targets early: throughput (events/sec), latency (p95/p99), and an error budget (acceptable failure rate). Elixir tends to shine when these goals are strict and you must meet them under load—not just in a quiet staging environment.
Elixir is excellent at handling lots of concurrent, mostly I/O-bound work—WebSockets, chat, notifications, orchestration, event processing. But it’s not a universal best choice. Knowing the trade-offs helps you avoid forcing Elixir into problems it’s not optimized for.
The BEAM VM prioritizes responsiveness and predictable latency, which is ideal for real-time systems. For raw CPU throughput—video encoding, heavy numerical computation, large-scale ML training—other ecosystems may be a better fit.
When you do need CPU-heavy work in an Elixir system, common approaches are:
Elixir itself is approachable, but OTP concepts—processes, supervisors, GenServers, backpressure—take time to internalize. Teams coming from request/response web stacks may need a ramp-up period before they can design systems the “BEAM way.”
Hiring can also be slower in some regions compared to mainstream stacks. Many teams plan to train internally or pair Elixir engineers with experienced mentors.
The core tools are strong, but some domains (certain enterprise integrations, niche SDKs) may have fewer mature libraries than Java/.NET/Node. You might write more glue code or maintain wrappers.
Running a single node is straightforward; clustering adds complexity: discovery, network partitions, distributed state, and deployment strategies. Observability is good but may require deliberate setup for tracing, metrics, and log correlation. If your org needs turnkey ops with minimal customization, a more conventional stack could be simpler.
If your app isn’t real-time, isn’t concurrency-heavy, and is mostly CRUD with modest traffic, choosing a mainstream framework your team already knows may be the fastest path.
Elixir adoption doesn’t have to be a big rewrite. The safest path is to start small, prove value with one real-time feature, and grow from there.
A practical first step is a small Phoenix application that demonstrates real-time behavior:
Keep the scope tight: one page, one data source, a clear success metric (e.g., “updates appear within 200ms for 1,000 connected users”). If you need a quick overview of setup and concepts, start at /docs.
If you’re still validating the product experience before committing to a full BEAM stack, it can also help to prototype the surrounding UI and workflows quickly. For example, teams often use Koder.ai (a vibe-coding platform) to sketch and ship a working web app via chat—React on the front end, Go + PostgreSQL on the back end—then integrate or swap in an Elixir/Phoenix real-time component once requirements are clear.
Even in a small prototype, structure your app so work happens in isolated processes (per user, per room, per stream). This makes it easier to reason about what runs where and what happens when something fails.
Add supervision early, not later. Treat it as basic plumbing: start key workers under a supervisor, define restart behavior, and prefer small workers over one “mega process.” This is where Elixir feels different: you assume failures will happen and make them recoverable.
If you already have a system in another language, a common migration pattern is:
Use feature flags, run the Elixir component in parallel, and monitor latency and error rates. If you’re evaluating plans or support for production use, check /pricing.
If you do build and share benchmarks, architecture notes, or tutorials from your evaluation, Koder.ai also has an earn-credits program for creating content or referring other users—useful if you’re experimenting across stacks and want to offset tooling costs while you learn.
“Real-time” in most product contexts means soft real-time: updates arrive quickly enough that the UI feels live (often within hundreds of milliseconds to a second or two), without manual refresh.
It’s different from hard real-time, where missing a deadline is unacceptable and usually requires specialized systems.
High concurrency is about how many independent activities are happening at once, not just peak requests per second.
Examples include:
Thread-per-connection designs can struggle because threads are relatively expensive, and overhead increases as concurrency grows.
Common pain points include:
BEAM processes are VM-managed and lightweight, designed to be created in very large numbers.
In practice, that makes patterns like “one process per connection/user/task” feasible, which simplifies modeling real-time systems without heavy shared-state locking.
With message passing, each process owns its state and other processes communicate by sending messages.
This helps reduce classic shared-memory problems such as:
You can implement backpressure at process boundaries, so the system degrades gracefully instead of falling over.
Common techniques include:
OTP provides conventions and building blocks for long-running systems that recover from failures.
Key pieces include:
“Let it crash” means you avoid excessive defensive code inside every worker and instead rely on supervision to restore a clean state.
Practically:
Phoenix real-time features typically map to three tools:
LiveView keeps most UI state and logic on the server and sends small diffs over a persistent connection.
It’s a strong fit for:
It’s usually not ideal for offline-first apps or highly custom client rendering (canvas/WebGL-heavy UIs).