Use Tuist's dependency-graph-aware binary caching to reuse precompiled modules and cut rebuild time locally and in CI — a step-by-step setup for well-modularized apps.
On a large modular app, most modules do not change between builds, yet Xcode often recompiles far more than necessary. That wasted work slows down both local development and CI.
Tuist's binary caching addresses this by reusing precompiled binaries for modules that have not changed. Instead of rebuilding a stable core module every time, Tuist can swap in a cached artifact.
Because Tuist already understands your full dependency graph, it can reason about exactly which modules are affected by a change and which are safe to reuse.
The result, for suitably modularized projects, is faster clean builds and faster CI runs. This guide walks through setting it up sensibly.
Caching rewards good architecture. If your app is a single monolithic target, there is little for a cache to reuse, because any change invalidates the whole thing.
Before investing in caching, confirm your app is split into meaningful modules with clean, acyclic dependencies. The more independent your modules, the more the cache can help.
Use Tuist's graph visualization to sanity-check boundaries. A tangled graph where everything depends on everything will see minimal caching benefit.
If you are not modularized yet, do that first. Caching amplifies a good structure; it cannot rescue a monolith.
At a high level, Tuist computes a fingerprint for each module based on its sources, settings, and dependencies. If a module's fingerprint matches a previously built artifact, Tuist reuses that binary.
When you change a module, its fingerprint changes, so it and anything depending on it are rebuilt, while unrelated modules stay cached.
This is why a clean, acyclic graph matters so much: it keeps the blast radius of any change small, maximizing cache hits.
Keep this model in mind as you work. It explains why some changes rebuild a lot and others almost nothing.
Tuist exposes caching through its commands. Rather than memorize flags that can change between versions, consult the current caching documentation for the exact command and options.
The general workflow is to warm the cache by building your modules once, after which subsequent generations and builds can reuse those artifacts for unchanged modules.
Start locally to build intuition. Warm the cache, make a small change to one module, and observe that only that module and its dependents rebuild.
Once the local behavior makes sense, you are ready to extend caching to shared and CI environments.
Local caching helps an individual, but the biggest wins come from a shared cache that many machines and CI runners can pull from.
A shared cache means a module built once by any contributor or CI job can be reused by everyone else, so engineers rarely rebuild modules they did not touch.
Tuist offers a hosted server product that supports remote caching for teams. Because hosted offerings and their pricing evolve, review the official site for current capabilities and plans.
Evaluate whether a shared remote cache justifies its cost for your team size. Larger teams with heavy CI usage tend to benefit most.
CI is where caching often pays off the most, because CI frequently builds from a clean state and would otherwise recompile everything every run.
Wire `tuist install` and `tuist generate` into your CI pipeline, and configure it to use your cache so unchanged modules are restored rather than rebuilt.
Ensure your CI runs a consistent, pinned Tuist version — a version manager helps here — so cache fingerprints stay stable across runs and machines.
Measure before and after. Capture your baseline CI build time first, then enable caching, so you can quantify the real improvement for your project.
Caching is only useful if it is correct. Periodically run a full clean build with caching disabled and confirm it matches your cached builds.
Be cautious with anything that can invalidate assumptions, such as non-deterministic build steps or code generation that is not captured in fingerprints. These can cause stale artifacts.
If you ever suspect a bad cache, clear it and rebuild from scratch. Treat unexplained build weirdness as a reason to invalidate the cache early in debugging.
A little discipline here keeps caching a reliable accelerator rather than a source of mystery failures.
Track your cache hit rate and build times over time. If hit rates are low, the usual culprit is coarse modularization or frequent changes to a widely depended-on module.
Refactoring a hub module that everything depends on into smaller pieces can dramatically improve cache effectiveness, since fewer builds get invalidated.
Revisit your graph as the app grows. Caching performance and architecture quality are tightly linked, so improvements to one help the other.
Finally, remember the boundary: caching speeds up building, but Tuist still does not sign or submit your app. Release continues to run through Xcode and the Apple Developer Program — just faster to build along the way.
Caching is a powerful tool, but it is not the right first move for every slow build, and reaching for it too early can mask simpler problems.
If your build is slow because of a single enormous target, expensive compile-time work, or heavy code generation, caching addresses the symptom rather than the cause. Fixing structure often yields larger and more durable gains.
Small projects and solo developers may find the setup and shared-cache overhead is not worth the modest savings. The tool shines most at team and CI scale.
Treat caching as one layer in a broader build-performance strategy that also includes sensible modularization, trimming unnecessary dependencies, and keeping your toolchain current. Measure first so you invest where the real bottleneck is, not where you assume it is.
Adopting caching goes more smoothly when you treat it as a staged rollout rather than a single switch you flip.
Begin locally on your own machine and confirm the mental model: warm the cache, change one module, and watch only that module and its dependents rebuild. This builds intuition before anyone else is affected.
Next, introduce caching in CI, where the payoff is usually largest, and measure the before-and-after build times so you have real numbers rather than impressions. Keep the Tuist version pinned so fingerprints stay stable across runs.
Finally, if a shared remote cache fits your team size and budget, roll it out to everyone so a module built once is reused everywhere. Communicate the new workflow clearly, keep a documented way to clear the cache when something looks wrong, and revisit your module boundaries periodically since architecture and cache effectiveness rise and fall together.
Not much. Caching reuses unchanged modules, so a monolithic single target sees little benefit because any change invalidates the whole build. Modularize first.
Local caching via the open-source CLI is free, but shared remote caching is part of Tuist's hosted server product, which has paid tiers. Check the official site for current pricing.
Periodically run a full clean build without caching and compare results, avoid non-deterministic build steps, and clear the cache whenever you suspect stale artifacts.
Usually coarse modularization or frequent changes to a widely depended-on hub module. Splitting hub modules into smaller pieces reduces the blast radius of changes and improves hit rates.