Cloud compiler

Your integration cost stops growing.

Every component you add to an event-driven system usually costs you in glue code, integration tests, and event-ordering risk. The Fluxtion cloud compiler removes the repeated plumbing work: component suppliers attach event, dependency and lifecycle metadata once, and Fluxtion uses that structure to generate a deterministic processor you ship as a single artefact.

The reusable unit is not just a component — it is a component with orchestration metadata attached. That metadata is what lets Fluxtion infer how the component participates in the processor graph.

The economics of build vs buy shift with it. Instead of every consumer hand-wiring every new library, suppliers make components composable once — drop in a vendor component with its metadata attached, and the compiler composes it into your generated processor like one of your own. Consumers compose approved components at near-zero marginal integration cost.

Two cost curves

Same components. Same demand. Different floors.

Functionality demand rises in both. The only thing that changes is what's underneath the integration.

Manual integration

Cost and errors climb. Trust falls.

Manual integration — cost climbs, trust falls.FunctionalityCostErrorsTrust ↓HighLowFew componentsMany →

Each new component is a new pair of adapters, a new integration test matrix, and a new ordering bug waiting to surface. The developer cost of wiring grows faster than the value of the next feature.

Cloud compiler

Marginal plumbing cost stays low. Ordering errors fall. Trust rises.

Cloud compiler — flat cost, rising trust.FunctionalityCostErrorsTrust ↑HighLowFew componentsMany →

Component count grows. Functionality grows with it. The wiring cost is paid once, by the service. What you ship is a single deterministic processor — auditable, replayable, and repeatable from the same input events.

The plumbing tax

Most enterprise software spend is glue.

Pick any meaningful enterprise system — a trading desk, a claims pipeline, a sensor network. The components are usually well-understood: a market feed, a risk model, an alerting rule, a journal. What costs money is the wiring between them. Which one fires first. What format the data is in when it arrives. What happens when one of them is slow. Whether the auditor can reconstruct the decision later.

The plumbing tax is the share of a development budget that goes to integration — not to capability. In most teams it's a majority. Adding a component means re-paying the tax. Removing one means re-paying the tax. Upgrading one means re-paying the tax. The Fluxtion cloud compiler reduces the repeated plumbing tax and turns much of it into a predictable generation cost.

What changes

Three jobs disappear. Three artefacts appear.

You stop paying for
Hand-written glue
Bespoke adapters between every pair of components — the integration code that grows quadratically as you add capability.
Per-component integration testing
The matrix of compatibility tests that has to be re-run every time a component is added, removed, or upgraded.
Brittle event ordering
Ad-hoc decisions about which component fires when. Subtle bugs that only surface under load or after a config change.
You get back
A compiled processor
One generated dispatcher class that hard-wires the entire graph. Ships with your application, runs in your JVM process, no service call at runtime.
A graph artefact
A GraphML diagram of the wired topology — what the auditor reads, what the reviewer approves, what the test fixture replays against.
A deterministic guarantee
For the same processor and the same input events, dispatch order is fixed rather than decided at runtime. Audit and replay become structural concerns of the generated processor, not bolted-on integration work.
Execution inference

The service reasons about your components for you.

You tell the cloud compiler which components you want in the system. The compiler figures out which one runs when — automatically, ahead of time, before any event arrives. There is no orchestrator, no rules engine, no runtime that has to be tuned. The integration is solved before the system boots: for the same processor and the same input events, the dispatch order is fixed rather than decided at runtime.

This is what flattens the marginal cost curve. The repeated hand-wiring work does not grow in the same way with component count, because the compiler is doing the orchestration reasoning developers used to repeat manually.

Technical depth — how the inference works

The cloud compiler analyses the dependency graph of your registered components — which one reads from which, which one writes to which, which one's output another one consumes — and produces a topologically sorted dispatch table. For every input event there is exactly one execution path through the components, and that path is baked into a generated Java class before the application starts.

The output is a plain .java source file. You compile it into your application like any other class. There is no service call at runtime, no daemon to keep alive, no SDK to manage. The cloud compiler runs at build time; the generated dispatcher runs in your process.

Because every dispatch is statically resolved, the runtime has zero reflection and no scheduling decisions on the steady-state path. For the same processor and the same input events, the dispatch order is fixed.

What determinism gives you

Audit, replay, trust — in that order.

A processor that can reproduce the same decisions from the same input events, using the same approved build, gives a regulator something concrete to inspect and sign off. Determinism is the single property that unlocks the rest.

Auditable
The generated graph is a reviewable audit artefact — a single file showing every component, dependency and dispatch order, alongside the event logs and replay evidence.
Replayable
Capture an event log once and replay it against an approved processor to reproduce the same decisions from the same inputs. Compliance, debugging and regression testing share one mechanism.
Trustworthy
Determinism removes runtime scheduling ambiguity. When behaviour changes, the generated graph and replay evidence make the difference inspectable.

See the audit & replay evidence — a real record + the replay flow →

The shape of the deal

Predictable, per-processor pricing.

Traditional integration cost scales with the number of components because the work is human, and humans cost per hour. The Fluxtion cloud compiler turns that line of the budget into a flat fee per generated processor.

A typical engagement has one or two processors per business unit. The marginal plumbing cost stays in the same band whether the processor wires three components or thirty. That predictability is the actual product — more than the speed-up, more than the determinism, the line the CFO cares about.

Talk to us about scoping; we'll work backwards from your component count to a price you can budget for the year.

Cost shape — traditional vs cloud compiler.ManualCloud$$$$Few componentsMany →
For the architect tier

Deeper reading on the mechanism.

What the cloud compiler does — and doesn't

It does

  • Generate orchestration from explicit component metadata and the object graph.
  • Remove repeated plumbing work for supported components.
  • Produce a processor artefact you can inspect, test and replay.

It doesn't

  • Make semantically incompatible components correct.
  • Remove the need for versioning, domain validation or test data.
  • Require a runtime cloud dependency after generation.

The cloud service is used to create or change the processor. The generated processor runs on its own — the runtime is open and carries no cloud dependency. The paid value is the compiler / generation step; the commercial benefit is lower marginal integration cost. See licensing.

Ready to put your integration cost on a flat line?

A 30-minute walkthrough is the fastest way to see whether the cloud compiler fits your stack. We'll work backwards from your component count and your audit requirements to a concrete scope.