GitHub Actions vs GitLab CI for backend, web, and mobile

What people struggle with in multi-app CI

When one repo builds a backend, a web app, and mobile apps, CI stops being "just run tests". It becomes a traffic controller for different toolchains, different build times, and different release rules.

The most common pain is simple: one small change triggers far too much work. A docs edit kicks off iOS signing, or a backend tweak forces a full web rebuild, and suddenly every merge feels slow and risky.

In multi-app setups, a few problems show up early:

• Runner drift: SDK versions differ across machines, so builds behave differently between CI and local.
• Secrets sprawl: API keys, signing certs, and store credentials get duplicated across jobs and environments.
• Cache confusion: the wrong cache key creates stale builds, but no cache makes everything painfully slow.
• Mixed release rules: backends want frequent deploys, while mobile releases are gated and need extra checks.
• Pipeline readability: configuration grows into a wall of jobs no one wants to touch.

That’s why the choice between GitHub Actions and GitLab CI matters more in monorepos than in single-app projects. You need clear ways to split work by path, share artifacts safely, and keep parallel jobs from stepping on each other.

A practical comparison comes down to four things: runner setup and scaling, secret storage and scoping, caching and artifacts, and how easy it is to express "only build what changed" without turning the pipeline into fragile rule soup.

This is about day-to-day reliability and maintainability, not which platform has more integrations or a nicer UI. It also doesn’t replace decisions about your build tools (Gradle, Xcode, Docker, and so on). It helps you choose the CI that makes a clean structure easier to keep clean.

How GitHub Actions and GitLab CI are structured

The biggest difference is how each platform organizes pipelines and reuse, and that starts to matter once backend, web, and mobile builds share the same repo.

GitHub Actions stores automation in YAML files under .github/workflows/. Workflows trigger on events like pushes, pull requests, schedules, or manual runs. GitLab CI centers around .gitlab-ci.yml at the repo root, with optional included files, and pipelines typically run on pushes, merge requests, schedules, and manual jobs.

GitLab is built around stages. You define stages (build, test, deploy), then assign jobs to stages that run in order. GitHub Actions is built around workflows containing jobs. Jobs run in parallel by default, and you add dependencies when something must wait.

For running the same logic across many targets, GitHub’s matrix builds are a natural fit (iOS vs Android, multiple Node versions). GitLab can do similar fan-out using parallel jobs and variables, but you often end up wiring more pieces yourself.

Reuse looks different as well. In GitHub, teams typically lean on reusable workflows and composite actions. In GitLab, reuse usually comes from include, shared templates, and YAML anchors/extends.

Approvals and protected environments also differ. GitHub often uses protected environments with required reviewers and environment secrets so production deploys pause until approved. GitLab commonly combines protected branches/tags, protected environments, and manual jobs so only specific roles can run a deploy.

Runner setup and job execution

Runner setup is where the two platforms start to feel different in daily use. Both can run jobs on hosted runners (you don’t manage the machine) or self-hosted runners (you own the machine, updates, and security). Hosted runners are simpler to start with; self-hosted runners are often needed for speed, special tools, or access to private networks.

A practical split in many teams is Linux runners for backend and web, and macOS runners only when you must build iOS. Android can run on Linux, but it’s heavy, so runner size and disk space matter.

Hosted vs self-hosted: what you manage

Hosted runners are a good fit when you want predictable setup without maintenance. Self-hosted runners make sense when you need specific Java/Xcode versions, faster caches, or internal network access.

If you go self-hosted, define runner roles early. Most repos do well with a small set: a general Linux runner for backend/web, a beefier Linux runner for Android work, a macOS runner for iOS packaging and signing, and a separate runner for deploy jobs with tighter permissions.

Picking the right runner per job

Both systems let you target runners (labels in GitHub, tags in GitLab). Keep naming tied to workloads, like linux-docker, android, or macos-xcode15.

Isolation is where many flaky builds come from. Leftover files, shared caches that get corrupted, or tools installed "by hand" on a self-hosted machine can all create random failures. Clean workspaces, pinned tool versions, and scheduled runner cleanup usually pay back quickly.

Capacity and permissions are the other recurring pain points, especially with macOS availability and cost. A good default is: build runners can build, deploy runners can deploy, and production credentials live in the smallest possible set of jobs.

Secrets and environment variables

Secrets are where multi-app CI pipelines get risky. The fundamentals are similar (store secrets in the platform, inject them at runtime), but scoping feels different.

GitHub Actions typically scopes secrets at the repository and organization level, with an extra Environment layer. That Environment layer is useful when production needs a manual gate and a separate set of values from staging.

GitLab CI uses CI/CD variables at the project, group, or instance level. It also supports environment-scoped variables, plus protections like "protected" (only available on protected branches/tags) and "masked" (hidden in logs). Those controls are helpful when one monorepo serves multiple teams.

The main failure mode is accidental exposure: debug output, a failed command that echoes variables, or an artifact that accidentally includes a config file. Treat logs and artifacts as shareable by default.

In backend + web + mobile pipelines, secrets usually include cloud credentials, database URLs and third-party API keys, signing material (iOS certificates/profiles, Android keystore and passwords), registry tokens (npm, Maven, CocoaPods), and automation tokens (email/SMS providers, chat bots).

For multiple environments (dev, staging, prod), keep names consistent and swap values by environment scope instead of copying jobs. That keeps rotation and access control manageable.

A few rules prevent most incidents:

• Prefer short-lived credentials (like OIDC to cloud providers when available) over long-lived keys.
• Use least privilege: separate deploy identities for backend, web, and mobile.
• Mask secrets and avoid printing environment variables, even in failures.
• Restrict production secrets to protected branches/tags and required reviewers.
• Never store secrets in build artifacts, even temporarily.

A simple, high-impact example: mobile jobs should only receive signing secrets on tagged releases, while backend deploy jobs can use a limited deploy token on merges to main. That change alone cuts the blast radius if a job is misconfigured.

Caching and artifacts for faster builds

Most slow pipelines are slow for one boring reason: they download and rebuild the same things over and over. Caching avoids repeat work. Artifacts solve a different problem: keeping the exact outputs from a specific run.

What to cache depends on what you build. Backends benefit from dependency and compiler caches (for example, Go module cache). Web builds benefit from package manager caches and build tool caches. Mobile builds often need Gradle plus Android SDK caching on Linux, and CocoaPods or Swift Package Manager caches on macOS. Be careful with aggressive iOS build output caching (like DerivedData) unless you understand the trade-offs.

Both platforms follow the same basic pattern: restore cache at the start of a job, save updated cache at the end. The day-to-day difference is control. GitLab makes cache and artifact behavior explicit in one file, including expiration. GitHub Actions often relies on separate actions for caching, which is flexible but easier to misconfigure.

Cache keys matter more in monorepos. Good keys change when inputs change, and stay stable otherwise. Lockfiles (go.sum, pnpm-lock.yaml, yarn.lock, and similar) should drive the key. It also helps to include a hash of the specific app folder you’re building instead of the whole repo, and to keep separate caches per app so one change doesn’t invalidate everything.

Use artifacts for the deliverables you want to keep from that exact run: release bundles, APK/IPA outputs, test reports, coverage files, and build metadata. Caches are best-effort speed-ups; artifacts are records.

If builds are still slow, look for oversized caches, keys that change every run (timestamps and commit SHAs are common culprits), and cached build outputs that aren’t reusable across runners.

Monorepo fit: multiple pipelines without chaos

A monorepo gets messy when every push triggers backend tests, web builds, and mobile signing, even if you only changed a README. The clean pattern is: detect what changed, run only the jobs that matter.

In GitHub Actions, this often means separate workflows per app with path filters so each workflow runs only when files in its area change. In GitLab CI, it often means one pipeline file using rules:changes (or child pipelines) to create or skip job groups based on paths.

Shared packages are where trust breaks. If packages/auth changes, both backend and web might need rebuilding even if their folders didn’t change. Treat shared paths as triggers for multiple pipelines and keep dependency boundaries clear.

A simple trigger map that keeps surprises down:

• Backend jobs run on changes in backend/** or packages/**.
• Web jobs run on changes in web/** or packages/**.
• Mobile jobs run on changes in mobile/** or packages/**.
• Docs-only changes run a fast check (formatting, spellcheck).

Parallelize what is safe (unit tests, linting, web build). Serialize what must be controlled (deployments, app store releases). Both GitLab’s needs and GitHub job dependencies help you run fast checks early and stop the rest when they fail.

Keep mobile signing isolated from everyday CI. Put signing keys in a dedicated environment with manual approval, and run signing only on tagged releases or a protected branch. Normal pull requests can still build unsigned apps for validation without exposing sensitive credentials.

Step by step: a clean pipeline for backend, web, and mobile

A clean multi-app pipeline starts with naming that makes intent obvious. Pick one pattern and stick to it so people can scan logs and know what ran.

One scheme that stays readable:

• Pipelines: pr-checks, main-build, release
• Environments: dev, staging, prod
• Artifacts: backend-api, web-bundle, mobile-debug, mobile-release

From there, keep jobs small and promote only what passed earlier checks:

1. PR checks (every pull request): run fast tests and lint only for the apps that changed. For backend, build a deployable artifact (a container image or a server bundle) and store it so later steps don’t rebuild it.

2. Web build (PR + main): build the web app into a static bundle. On PRs, keep the output as an artifact (or deploy to a preview environment if you have one). On main, produce a versioned bundle suitable for dev or staging.

3. Mobile debug builds (PR only): build a debug APK/IPA. Don’t sign for release. The goal is quick feedback and a file testers can install.

4. Release builds (tags only): when a tag like v1.4.0 is pushed, run full backend and web builds plus signed mobile release builds. Generate store-ready outputs and keep release notes alongside the artifacts.

5. Manual approvals: place approvals between staging and prod, not before basic testing. Developers can trigger builds, but only approved roles should deploy to production and access production secrets.

Common mistakes that waste time

Teams often lose weeks to workflow habits that quietly create flaky builds.

One trap is leaning too hard on shared runners. When many projects compete for the same pool, you get random timeouts, slow jobs, and mobile builds that fail only at peak hours. If backend, web, and mobile builds matter, isolate heavy jobs on dedicated runners (or at least separate queues) and keep resource limits explicit.

Secrets are another time sink. Mobile signing keys and certificates are easy to mishandle. A common mistake is storing them too broadly (available to every branch and job) or leaking them through verbose logs. Keep signing material limited to protected branches/tags, and avoid any step that prints secret values (even base64 strings).

Caching can backfire when teams cache huge directories or mix up caches and artifacts. Cache only stable inputs. Keep outputs you need later as artifacts.

Finally, in monorepos, triggering every pipeline on every change burns minutes and patience. If someone tweaks a README and you rebuild iOS, Android, backend, and web, people stop trusting CI.

A quick checklist that helps:

• Use path-based rules so only affected apps run.
• Separate test jobs from deploy jobs.
• Keep signing keys restricted to release workflows.
• Cache small, stable inputs, not entire build folders.
• Plan predictable runner capacity for heavy mobile builds.

Quick checks before you commit to one platform

Before you choose, do a few checks that reflect how you actually work. They save you from picking a tool that feels fine for one app but painful once you add mobile builds, multiple environments, and releases.

Focus on:

• Runner plan: hosted, self-hosted, or a mix. Mobile builds often push teams toward a mix because iOS needs macOS.
• Secrets plan: where secrets live, who can read them, and how rotation works. Production should be tighter than staging.
• Cache plan: what you cache, where it’s stored, and how keys are formed. If the key changes on every commit, you’ll pay the cost without the speed.
• Monorepo plan: path filters and a clean way to share common steps (lint, tests) without copy-paste.
• Release plan: tags, approvals, and environment separation. Be explicit about who can promote to production and what proof they need.

Pressure-test those answers with a small scenario. In a monorepo with a Go backend, a Vue web app, and two mobile apps: a docs-only change should do almost nothing; a backend change should run backend tests and build an API artifact; a mobile UI change should build only Android and iOS.

If you can’t describe that flow on one page (triggers, caches, secrets, approvals), run a one-week pilot on both platforms using the same repo. Pick the one that feels boring and predictable.

Example: a realistic monorepo build and release flow

Picture one repo with three folders: backend/ (Go), web/ (Vue), and mobile/ (iOS and Android).

Day to day, you want fast feedback. On releases, you want full builds, signing, and publish steps.

A practical split:

• Feature branches: run lint + unit tests for the changed parts, build backend and web, and optionally run an Android debug build. Skip iOS unless you really need it.
• Release tags: run everything, create versioned artifacts, sign mobile apps, and push images/binaries to your release storage.

Runner choice shifts once mobile is involved. Go and Vue builds are happy on Linux almost anywhere. iOS requires macOS runners, which can drive the decision more than anything else. If your team wants full control of build machines, GitLab CI with self-hosted runners can be easier to run as a fleet. If you prefer less ops work and quick setup, GitHub hosted runners are convenient, but macOS minutes and availability become part of your planning.

Caching is where real time is saved, but the best cache differs by app. For Go, cache module downloads and the build cache. For Vue, cache the package manager store and rebuild only when lockfiles change. For mobile, cache Gradle and the Android SDK on Linux; cache CocoaPods or Swift Package Manager on macOS, and expect larger caches and more invalidation.

A decision rule that holds up: if your code is already hosted on one platform, start there. Switch only if runners (especially macOS), permissions, or compliance force your hand.

Next steps: choose, standardize, and automate safely

Pick the tool that matches where your code and people already are. Most of the time, the difference shows up in daily friction: reviews, permissions, and how fast someone can diagnose a broken build.

Start simple: one pipeline per app (backend, web, mobile). Once stable, pull shared steps into reusable templates so you reduce copy-paste without blurring ownership.

Write down secret scopes like you’d write down who has keys to an office. Production secrets should not be readable by every branch. Set a rotation reminder (quarterly beats never), and agree on how emergency revokes work.

If you’re building with a no-code generator that produces real source code, treat generation/export as a first-class CI step. For example, AppMaster (appmaster.io) generates Go backends, Vue3 web apps, and Kotlin/SwiftUI mobile apps, so your pipeline can regenerate code on change, then build only the affected targets.

Once you have a flow your team trusts, make it the default for new repos and keep it boring: clear triggers, predictable runners, tight secrets, and releases that only run when you mean them to.