Modern CI/CD pipelines are designed to automate software building, testing, packaging, and deployment.
However, as projects grow, pipelines can become slower.
A simple application may take only a few minutes to build, but a large production project can spend significant time:
- Installing dependencies
- Compiling source code
- Building application packages
- Downloading tools
- Creating Docker images
- Running repeated build steps
Running all of these steps from scratch every time can waste both time and CI/CD resources.
Two important concepts help solve this problem:
CI/CD artifacts and build caching.
Artifacts allow pipelines to save and transfer important build outputs.
Caching allows pipelines to reuse files and dependencies that do not need to be generated repeatedly.
Although artifacts and caches may appear similar, they solve different problems.
In this complete guide, you will learn what CI/CD artifacts are, what build caching means, how they work, when to use them, the differences between artifacts and caches, and how they improve pipeline performance.
What Are CI/CD Artifacts?
A CI/CD artifact is a file or collection of files produced during a pipeline that is saved for use by another job, stage, deployment process, or user.
In simple terms:
An artifact is an output generated by your CI/CD pipeline.
For example, imagine your pipeline builds an Android application.
The pipeline may generate:
app-release.apk
or:
app-release.aab
These files are build artifacts.
Similarly, a web project may generate:
dist/
A Java project may generate:
application.jar
A Docker-based pipeline may produce:
Docker image
A test stage may produce:
test-report.xml
coverage-report.html
All of these can be considered artifacts.
Simple CI/CD Artifact Example
Consider this pipeline:
Developer Pushes Code
↓
Install Dependencies
↓
Run Tests
↓
Build Application
↓
Generate APK
↓
Store APK as Artifact
↓
Deployment Stage
The APK created during the build stage can be stored and later downloaded or used by another job.
Instead of rebuilding the APK again during deployment, the deployment stage can use the existing artifact.
Why Are Artifacts Important?
Artifacts provide several important benefits in CI/CD pipelines.
Preserve Build Outputs
CI/CD runners are often temporary.
After a job finishes, its environment may be destroyed.
Without artifacts, important generated files could disappear.
Artifacts allow the pipeline to preserve those files.
Transfer Files Between Jobs
CI/CD pipelines often contain multiple jobs.
For example:
Build Job
↓
Test Job
↓
Deploy Job
A build job may generate an application package.
The deployment job needs access to that package.
Artifacts provide a reliable way to transfer the file between jobs.
Avoid Rebuilding the Same Application
Suppose the build stage creates:
app-release.aab
The production deployment stage should ideally deploy exactly that file.
Rebuilding the application again during deployment could theoretically produce a different result.
Using the same artifact helps maintain consistency.
Store Test Reports
Artifacts are not limited to application binaries.
Testing stages can store:
Unit test reports
Integration test reports
Coverage reports
Screenshots
Logs
Performance reports
These files help developers investigate pipeline failures.
Support Release Management
Artifacts can also represent official application releases.
For example:
Version 1.0.0
backend-v1.0.0.jar
frontend-v1.0.0.zip
app-v1.0.0.apk
These files can be stored with release information.
Common Types of CI/CD Artifacts
Artifacts depend on the type of project.
Common examples include:
Android APK
Android AAB
iOS IPA
Java JAR
Java WAR
.NET DLL
ZIP files
Compiled binaries
Docker images
Frontend build folders
Test reports
Coverage reports
Logs
Documentation
Source maps
Artifact Example in Flutter
Suppose a Flutter pipeline runs:
flutter build apk --release
Flutter creates the application inside a path similar to:
build/app/outputs/flutter-apk/app-release.apk
The CI pipeline can save this file as an artifact.
Another job can later download it.
Artifact Example in React
A React application may be built using:
npm run build
The build command may produce:
build/
or:
dist/
depending on the project setup.
This directory contains production-ready frontend files.
The pipeline can store this folder as an artifact and later deploy it to a hosting environment.
Artifact Example in Node.js
A Node.js project may generate:
dist/
after TypeScript compilation.
For example:
npm run build
could produce:
dist/server.js
The dist directory can then be stored as a pipeline artifact.
Artifact Example in Java
A Java Maven project might run:
mvn package
The output may be:
target/application.jar
The JAR file can be stored as an artifact and used later for deployment.
How Artifacts Move Through a Pipeline
Consider this workflow:
Source Code
↓
Build Job
↓
application.jar
↓
Artifact Storage
↓
Deployment Job
↓
Production Server
The build server does not need to remain running.
The artifact is uploaded to storage.
Another job can retrieve it later.
Example GitHub Actions Artifact
A simplified GitHub Actions workflow may look like:
name: Build Application
on:
push:
branches:
- main
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v4
- name: Install dependencies
run: npm ci
- name: Build application
run: npm run build
- name: Upload build artifact
uses: actions/upload-artifact@v4
with:
name: production-build
path: dist/
The dist directory is uploaded as an artifact.
A second job can download it.
deploy:
needs: build
runs-on: ubuntu-latest
steps:
- name: Download artifact
uses: actions/download-artifact@v4
with:
name: production-build
- name: Deploy application
run: ./deploy.sh
The deployment stage uses the build output from the previous stage.
What Is Build Caching?
Build caching is different from artifacts.
A build cache stores reusable files from previous pipeline runs so that future builds do not need to recreate or download them again.
In simple terms:
Cache helps make future pipeline runs faster.
Suppose every pipeline executes:
npm install
and downloads hundreds of dependencies.
Downloading those packages every time wastes time.
A cache can preserve dependency files between pipeline runs.
The next pipeline can reuse those files.
Simple Build Cache Example
Without caching:
Pipeline Run 1
Download Dependencies
Build
Test
Pipeline Run 2
Download Dependencies Again
Build
Test
Pipeline Run 3
Download Dependencies Again
Build
Test
With caching:
Pipeline Run 1
Download Dependencies
Save Cache
Build
Test
Pipeline Run 2
Restore Cache
Build
Test
Pipeline Run 3
Restore Cache
Build
Test
This can significantly reduce pipeline execution time.
What Can Be Cached?
Common cached data includes:
Package dependencies
Compiler outputs
Build system files
Downloaded SDK components
Gradle cache
Maven cache
npm cache
Yarn cache
pnpm cache
Flutter Pub cache
CocoaPods cache
Docker layers
Caching npm Dependencies
A Node.js project frequently installs packages using:
npm ci
Instead of downloading every package repeatedly, the CI environment can cache npm package data.
A cache may be based on:
package-lock.json
If the lock file does not change, the same cache can often be reused.
Example GitHub Actions npm Cache
GitHub Actions can automatically handle dependency caching during Node.js setup.
Example:
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 22
cache: npm
- name: Install dependencies
run: npm ci
The dependency cache can make future installations faster.
Flutter Build Caching
Flutter projects can benefit heavily from caching.
Common cache locations may include:
Flutter SDK
Dart Pub packages
Gradle dependencies
CocoaPods dependencies
For example, Flutter packages downloaded using:
flutter pub get
may be reused through caching.
Without caching:
Download Packages
↓
Download Gradle Dependencies
↓
Compile
With caching:
Restore Packages
↓
Restore Gradle Cache
↓
Compile
This can make mobile CI pipelines considerably faster.
Gradle Build Cache
Android development commonly uses Gradle.
Gradle can cache dependency files and certain build outputs.
Typical Gradle cache locations include:
~/.gradle/caches
and:
~/.gradle/wrapper
Caching these directories can reduce repeated downloads.
However, caches should be configured carefully to avoid using outdated build files.
Maven Cache
Java projects using Maven frequently download dependencies from remote repositories.
These dependencies are commonly stored locally in:
~/.m2/repository
CI systems can cache this directory.
Instead of repeatedly downloading the same dependencies, Maven can use previously cached packages.
Docker Layer Caching
Docker builds can also benefit from caching.
Consider this Dockerfile:
FROM node:22
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build
Docker creates layers.
If:
package.json
and:
package-lock.json
have not changed, the dependency-installation layer may be reused.
This means Docker does not need to execute:
npm ci
again.
Docker layer caching can dramatically reduce container build times.
Artifacts vs Cache
Artifacts and cache are often confused because both store files.
However, their purpose is different.
| Artifacts | Cache |
|---|---|
| Store pipeline outputs | Store reusable build data |
| Used between pipeline stages | Usually reused between pipeline runs |
| Often required for deployment | Mainly used for performance |
| Should be reliable | Can usually be recreated |
| May contain release binaries | Usually contains dependencies |
| Examples: APK, JAR, ZIP | Examples: npm, Gradle, Maven cache |
A simple way to remember the difference is:
Artifacts = Results
Cache = Reusable Resources
Example: Artifact vs Cache in Flutter
Suppose your Flutter pipeline runs:
flutter pub get
flutter test
flutter build appbundle
The Flutter package cache might be:
~/.pub-cache
This is a cache because it helps future builds run faster.
The generated file:
app-release.aab
is an artifact because it is the final build output.
Example: Artifact vs Cache in Node.js
Suppose your application runs:
npm ci
npm test
npm run build
The npm dependency cache is:
Cache
The output:
dist/
is:
Artifact
The cache accelerates builds.
The artifact is the result of the build.
Cache Keys Explained
CI systems need to determine whether a stored cache is still valid.
They usually use something called a cache key.
For example:
npm-linux-package-lock-hash
A cache key could depend on:
Operating system
Runtime version
Dependency lock file
Project version
Example logic:
Linux
+
Node 22
+
package-lock.json hash
If package-lock.json changes, a new cache may be created.
Why Cache Keys Matter
Suppose your dependencies change.
Old cache:
React 18
Express 4
New dependencies:
React 19
Express 5
If the CI pipeline incorrectly restores the old cache, it could produce inconsistent results.
Using a dependency-lock-file hash allows the pipeline to detect that dependencies changed.
Cache Hit and Cache Miss
Two common caching terms are:
Cache Hit
A cache hit occurs when the CI system finds a matching cache.
Example:
Search Cache
↓
Matching Cache Found
↓
Restore Cache
This makes the build faster.
Cache Miss
A cache miss occurs when no matching cache exists.
Example:
Search Cache
↓
No Cache Found
↓
Download Dependencies
↓
Create New Cache
A cache miss is not necessarily an error.
It usually means the pipeline needs to rebuild the cache.
Exact Cache Match vs Restore Key
Some CI systems support fallback cache matching.
For example:
Exact Key
linux-node22-abcd1234
If that cache is not found, the system might search for:
linux-node22-
This can allow older caches to be reused partially.
However, fallback caching should be used carefully because outdated data may produce unexpected behavior.
Dependency Lock Files and Caching
Dependency lock files are extremely important when designing cache keys.
Examples include:
package-lock.json
yarn.lock
pnpm-lock.yaml
pubspec.lock
Podfile.lock
gradle.lockfile
composer.lock
If these files change, your dependency cache may also need to change.
That is why many pipelines calculate a hash based on a lock file.
Example Cache Key Concept
A conceptual key might look like:
flutter-linux-8b39ad72
where:
flutter-linux
represents the environment and:
8b39ad72
represents a hash generated from:
pubspec.lock
When dependencies change:
pubspec.lock
changes.
Therefore, the cache key also changes.
Artifact Retention
Artifacts do not necessarily remain stored forever.
CI/CD platforms often support artifact retention policies.
For example:
Keep artifacts for 7 days
Keep artifacts for 30 days
Keep release artifacts for 90 days
Keep production releases permanently
The correct retention period depends on project requirements.
Temporary test reports might only need short-term retention.
Official production release artifacts may need longer retention.
Why Artifact Retention Matters
Artifacts consume storage.
Imagine:
Build Size = 500 MB
10 Builds Per Day
30 Days
That could create:
500 MB × 10 × 30
= 150 GB
of stored build outputs.
Keeping every artifact permanently may become expensive.
Retention policies help control storage usage.
Artifact Naming Best Practices
Artifacts should have meaningful names.
Instead of:
build.zip
consider:
myapp-v2.4.0-linux-x64.zip
or:
android-production-v2.4.0.aab
Good artifact names may include:
Application name
Version
Environment
Platform
Architecture
Build number
For example:
shop-app-v3.1.2-production-android.aab
Immutable Artifacts
An important CI/CD principle is:
Build once, deploy the same artifact everywhere.
Suppose your pipeline creates:
application-v1.5.0.jar
The same JAR should ideally move through:
Testing
↓
Staging
↓
Production
instead of rebuilding the application separately for every environment.
This is often called an immutable artifact approach.
Why Build Once and Deploy Many?
Imagine this workflow:
Build for Testing
↓
Rebuild for Staging
↓
Rebuild for Production
Each rebuild introduces a possibility that the output could be different.
A safer model is:
Build Once
↓
Artifact
↓
Testing
↓
Staging
↓
Production
This ensures production receives the same build that passed previous checks.
Artifact Repositories
Large organizations often use dedicated artifact repositories.
These repositories store packages and build outputs centrally.
Examples of artifact repository categories include:
Container registries
Package registries
Binary repositories
They may store:
Docker images
Java packages
npm packages
Python packages
Compiled binaries
Release archives
Artifact repositories also help teams manage versions.
Example:
application:1.0.0
application:1.1.0
application:1.2.0
Docker Images as Artifacts
Docker images are often treated as deployment artifacts.
A pipeline might perform:
Source Code
↓
Docker Build
↓
Docker Image
↓
Container Registry
↓
Staging Deployment
↓
Production Deployment
For example:
myapp:1.4.0
The same image can be deployed across multiple environments.
Build Cache vs Docker Image
These concepts are different.
A Docker build cache helps create images faster.
A Docker image is the final build result.
Example:
Docker Layers
= Cache
while:
myapp:1.4.0
= Artifact
Caching in Monorepos
Monorepos can contain multiple applications.
Example:
repository/
apps/
frontend/
backend/
mobile/
packages/
shared/
Rebuilding every application after every small change can be expensive.
Smart caching can detect which components changed.
For example:
Frontend changed
↓
Rebuild Frontend
Backend unchanged
↓
Use cached result
Shared library unchanged
↓
Use cached result
This is commonly called incremental or task-level caching.
Local Cache vs Remote Cache
Build caching may exist at different levels.
Local Cache
The cache exists on one machine or CI runner.
Example:
Developer Computer
↓
Local Gradle Cache
Local caches are fast but may not be accessible to other machines.
Remote Cache
A remote cache is stored centrally.
Multiple developers or CI runners can access it.
Example:
Developer A
↓
Remote Cache
↑
Developer B
↑
CI Server
Remote caching can be extremely useful for large development teams.
Build Caching and CI Runners
CI runners may be:
Persistent
or
Ephemeral
A persistent runner can keep local data between jobs.
An ephemeral runner may be destroyed after each pipeline job.
For ephemeral runners, remote cache storage becomes especially useful.
Without remote caching:
New Runner
Download Everything
Build
Delete Runner
With remote caching:
New Runner
Restore Cache
Build
Save Updated Cache
Delete Runner
Cache Invalidation
Cache invalidation means determining when old cached data should no longer be used.
This is one of the most important aspects of caching.
For example, a Node.js dependency cache should usually change when:
package-lock.json
changes.
A Flutter cache may depend on:
pubspec.lock
A Docker dependency layer may change when:
package.json
changes.
Incorrect cache invalidation can cause stale or incorrect builds.
Common Cache Invalidation Strategy
A good strategy is:
Environment
+
Tool Version
+
Dependency Lock File Hash
Example:
ubuntu-node22-a8d3971c
This helps ensure cache compatibility.
When Should Cache Be Deleted?
Caches may need to be cleared when:
Dependencies become corrupted
Toolchain changes
Compiler changes
Runtime version changes
Build configuration changes
Cache causes unexpected behavior
Developers sometimes solve mysterious CI problems by manually clearing the cache.
However, frequent manual cache clearing usually means the cache strategy should be improved.
Why You Should Not Cache Everything
Caching every directory is usually a bad idea.
Some files may contain:
Temporary data
Old builds
Environment-specific files
Secrets
Generated configuration
Unstable metadata
Caching these files can introduce unpredictable behavior.
Cache only files that:
Are expensive to recreate
Can safely be reused
Can be invalidated reliably
Avoid Caching Secrets
Sensitive files should not be placed inside shared caches.
Examples include:
API keys
Private certificates
Passwords
Signing keys
Service account credentials
Access tokens
Secrets should normally be managed through the CI platform’s secure secret-management system.
Cache Security Risks
Caches may create security problems if improperly configured.
Consider a workflow where untrusted code can modify a shared cache.
Another pipeline might restore that compromised cache later.
Teams should carefully control:
Who can create caches
Who can restore caches
Which branches share caches
What files are cached
Production pipelines should be especially strict.
Artifacts and Security
Artifacts can also contain sensitive information.
For example, test logs might accidentally expose:
Tokens
Database URLs
User information
Environment variables
Before uploading artifacts, ensure they do not contain secrets.
Artifact access should also be restricted appropriately.
Artifact Checksums
For important release artifacts, teams may calculate checksums.
For example:
SHA-256
A checksum helps verify that an artifact has not changed.
Example concept:
Application File
↓
SHA-256 Hash
↓
Compare Hash Before Deployment
If the hash changes unexpectedly, deployment can be stopped.
Artifact Versioning
Production artifacts should usually have unique versions.
Instead of repeatedly generating:
app-release.apk
you might use:
app-1.0.0.apk
app-1.1.0.apk
app-1.2.0.apk
Other identifiers can include:
Git commit SHA
Build number
Release tag
Example:
backend-a91f46c.jar
This helps track exactly which code produced a deployment.
Git Commit SHA as Artifact Version
Many teams include the Git commit identifier in build artifacts.
Example:
api-82a19fc.jar
Then you can trace:
Production Artifact
↓
Git Commit
↓
Source Code
This greatly improves release traceability.
Build Cache Performance
Caching can save a surprising amount of time.
Suppose a pipeline normally takes:
Dependency Installation = 4 minutes
Compilation = 5 minutes
Tests = 3 minutes
Total = 12 minutes
After introducing caching:
Dependency Restore = 30 seconds
Cached Compilation = 2 minutes
Tests = 3 minutes
Total ≈ 5.5 minutes
That saves more than six minutes per pipeline.
If the pipeline runs dozens or hundreds of times per day, the productivity benefit becomes substantial.
Cache Compression
Caches are commonly compressed before upload.
Conceptually:
Cache Directory
↓
Compress
↓
Upload
During another pipeline:
Download
↓
Extract
↓
Reuse
Compression reduces storage and network transfer size.
However, excessively large caches can still become slow.
A cache that takes five minutes to download may provide little advantage.
Cache Size Optimization
Do not blindly store huge caches.
Instead, evaluate:
Cache Size
vs
Time Saved
Suppose:
Cache Download = 3 minutes
Fresh Dependency Download = 2 minutes
The cache is actually slowing down the pipeline.
Caching is beneficial only when restoring the cache is cheaper than recreating the data.
Cache Warming
Cache warming means creating frequently needed cache data before it is required.
For example:
Nightly Pipeline
↓
Download Dependencies
↓
Generate Cache
Developers’ pipelines can then restore that cache the next morning.
Cache warming is usually more useful for large projects with expensive dependencies.
CI/CD Pipeline Without Cache
A pipeline without caching might look like:
Checkout Repository
↓
Download Node.js Dependencies
↓
Download Build Tools
↓
Compile Application
↓
Run Tests
↓
Build Artifact
Every pipeline performs the same expensive setup.
CI/CD Pipeline With Cache
With caching:
Checkout Repository
↓
Restore Dependency Cache
↓
Restore Build Cache
↓
Compile Changed Code
↓
Run Tests
↓
Build Artifact
↓
Update Cache
The pipeline performs less repeated work.
CI/CD Pipeline Using Both Artifacts and Cache
A real production pipeline may use both.
Git Push
↓
Restore Dependencies Cache
↓
Install Missing Dependencies
↓
Run Unit Tests
↓
Build Application
↓
Save Build Artifact
↓
Deploy to Staging
↓
Integration Tests
↓
Deploy Same Artifact to Production
Here:
Dependencies
= Cache
while:
Application Build
= Artifact
Both work together but serve completely different purposes.
Example Flutter CI/CD Architecture
A Flutter pipeline might look like:
Git Push
↓
Restore Pub Cache
↓
Restore Gradle Cache
↓
flutter pub get
↓
flutter analyze
↓
flutter test
↓
flutter build appbundle
↓
Upload AAB Artifact
↓
Deploy to Testing
↓
Release to Play Store
Caches:
Pub packages
Gradle dependencies
Artifact:
app-release.aab
Example React CI/CD Architecture
Git Push
↓
Restore npm Cache
↓
npm ci
↓
npm test
↓
npm run build
↓
Upload dist/ Artifact
↓
Deploy Artifact
Cache:
npm packages
Artifact:
dist/
Example Java CI/CD Architecture
Checkout Code
↓
Restore Maven Cache
↓
mvn test
↓
mvn package
↓
Upload JAR Artifact
↓
Deploy JAR
Cache:
~/.m2/repository
Artifact:
application.jar
Artifact Promotion
Artifact promotion means moving the same approved artifact through environments.
Example:
Build Artifact
↓
Development
↓
Testing
↓
Staging
↓
Production
The artifact itself does not change.
Only its deployment status changes.
For example:
myapp:1.8.0
Testing ✓
Staging ✓
Production ✓
This improves release consistency.
Artifact Traceability
A good artifact system should allow you to answer:
Which commit created this artifact?
Which CI job built it?
Which tests were executed?
Who approved deployment?
Which environment is running it?
This is extremely important for debugging production issues.
Reproducible Builds
A reproducible build means the same source code and build environment can produce the same or equivalent output.
Artifacts and lock files help improve reproducibility.
A controlled pipeline typically pins:
Runtime versions
Dependency versions
Build tools
Docker base images
Compiler versions
This reduces unexpected differences between builds.
Build Caching Best Practices
Follow these practices when implementing caching:
- Cache only expensive reusable files.
- Use lock files when generating cache keys.
- Include operating system or runtime version when necessary.
- Avoid storing secrets inside caches.
- Do not cache unstable temporary files.
- Monitor cache size.
- Measure whether caching actually improves build time.
- Use separate caches when project environments differ.
- Invalidate caches when toolchains change.
- Periodically remove unused cache entries.
Artifact Best Practices
A reliable artifact strategy should:
- Build artifacts only after required quality checks pass.
- Use clear artifact names.
- Include version information.
- Keep artifacts immutable after creation.
- Use retention policies.
- Protect sensitive artifacts.
- Store production artifacts securely.
- Add checksums for important releases.
- Associate artifacts with Git commits.
- Deploy the same tested artifact to production.
Common Build Caching Mistakes
Using One Cache Key Forever
Example:
cache-v1
If dependencies change but the key never changes, stale files may continue to be restored.
Use dependency information in the key.
Caching Large Unnecessary Directories
A huge cache may take longer to download than rebuilding the data.
Measure performance.
Caching Build Secrets
Never cache sensitive authentication credentials.
Sharing Unsafe Cache Across Branches
A feature branch may produce different dependencies or build results.
Carefully define which branches can share caches.
Assuming Cache Must Always Exist
Caches are optimization tools.
Your build should normally still work when the cache is missing.
In other words:
Cache Available
→ Faster Build
but:
Cache Missing
→ Build Should Still Work
Common Artifact Mistakes
Rebuilding During Deployment
If the tested artifact is rebuilt immediately before production deployment, you are no longer deploying the exact file that passed testing.
Prefer:
Build Once
Test Artifact
Deploy Same Artifact
Overwriting Existing Artifacts
Avoid replacing:
version-1.0.0
with a different build.
Published release artifacts should generally remain immutable.
Keeping Every Artifact Forever
Large build outputs can consume significant storage.
Use retention rules.
Poor Artifact Naming
Names such as:
final.zip
latest-final.zip
final-v2.zip
create confusion.
Use proper version identifiers.
Artifacts vs Cache vs Source Code
These three concepts should not be confused.
Source Code
= Input
Cache
= Reusable Optimization
Artifact
= Build Output
Example:
Git Repository
↓
Source Code
npm Cache
↓
Reusable Dependencies
dist/
↓
Artifact
Artifacts vs Cache vs Container Registry
A container registry stores Docker or OCI images.
For containerized applications:
Docker Build Cache
helps accelerate Docker builds.
The resulting:
Docker Image
is an artifact.
The:
Container Registry
stores and distributes that artifact.
Frequently Asked Questions
What is an artifact in CI/CD?
A CI/CD artifact is a file or build output generated during a pipeline and stored so that it can be downloaded, transferred, tested, or deployed later.
Examples include APK files, JAR files, ZIP files, compiled binaries, frontend build directories, and test reports.
What is build caching?
Build caching stores reusable dependencies or intermediate build data so that future pipeline executions can avoid repeating expensive work.
What is the difference between cache and artifact?
A cache improves pipeline performance by storing reusable data.
An artifact stores an important pipeline output.
Simply remember:
Cache = Speed
Artifact = Result
Is node_modules an artifact?
Normally, no.
Dependencies or dependency caches are generally treated as cache-related data.
The final application output, such as dist/, is usually considered the artifact.
Is an APK an artifact?
Yes.
An Android APK or AAB generated by a CI/CD pipeline is a typical build artifact.
Is a Docker image an artifact?
Yes.
A Docker image built from application source code is commonly treated as a deployment artifact.
Should artifacts be cached?
Artifacts and caches serve different purposes.
Build artifacts should usually be stored as artifacts rather than relying on a temporary cache.
What happens if a cache is deleted?
The pipeline should normally rebuild or download the required information and then create a new cache.
Cache deletion should affect performance, not correctness.
Should the CI/CD pipeline fail if a cache is missing?
Usually no.
A cache is an optimization.
A missing cache should normally cause a slower build rather than a failed build.
How long should artifacts be stored?
It depends on the project.
Temporary test artifacts may be retained for a few days, while official production releases may need to be stored much longer.
Final Thoughts
Artifacts and build caching are two important components of efficient CI/CD pipelines.
Although both involve storing files, their responsibilities are very different.
Artifacts preserve important pipeline outputs.
Examples include:
APK
AAB
IPA
JAR
Docker image
Frontend build
Test report
Build caches preserve reusable data that helps future builds execute faster.
Examples include:
npm packages
Gradle dependencies
Maven packages
Flutter Pub packages
Docker layers
A well-designed CI/CD pipeline often uses both:
Source Code
↓
Restore Cache
↓
Install Missing Dependencies
↓
Build
↓
Test
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Generate Artifact
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Store Artifact
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Deploy Same Artifact
Caching reduces unnecessary repeated work, while artifacts provide consistent and traceable application builds.
The most important principle to remember is:
Cache what can be recreated.
Store what must be preserved.
When artifacts and caching are designed correctly, CI/CD pipelines become faster, more reliable, easier to debug, and safer to deploy.
