Caching Strategies for Faster CI/CD

In modern software development, CI/CD pipelines are the backbone of rapid deployment cycles. However, as codebases grow and testing requirements expand, build times can balloon from minutes to hours. The solution? Smart caching strategies that can reduce pipeline execution time by 40-70%. This comprehensive guide explores proven caching techniques used by industry leaders to keep their deployment pipelines blazing fast.

Why Caching Matters in CI/CD

Every second your CI/CD pipeline takes costs money and developer productivity. When engineers wait 30 minutes for build feedback, they context-switch to other tasks, losing momentum. According to research by CircleCI, teams with build times under 10 minutes deploy 3x more frequently than those with longer builds.

Caching addresses this by storing reusable artifacts between pipeline runs. Instead of downloading dependencies, compiling code, or building Docker layers from scratch every time, your pipeline reuses previously computed results. Companies like Google and Netflix have reduced their pipeline times by 60% through strategic caching implementations.

The key is understanding what to cache, where to cache it, and how to invalidate it when necessary. Poor caching strategies can lead to stale builds and hard-to-debug issues. This guide will help you avoid those pitfalls.

Fundamentals of CI/CD Caching

What Can Be Cached?

Not everything in your pipeline benefits from caching. Here are the high-value targets:

Dependencies and Package Managers

• npm/yarn node_modules
• pip packages and virtual environments
• Maven/Gradle dependencies
• Docker base layers
• Composer packages (PHP)
• Go modules

Build Artifacts

• Compiled binaries
• Transpiled JavaScript
• CSS preprocessor outputs
• Static assets
• Test compilation results

Docker Layer Cache

• Base images
• Intermediate build layers
• Multi-stage build outputs

Test Results

• Unit test execution data
• Code coverage reports
• Linting outputs

Cache Key Strategies

The cache key determines when cached data is reused or invalidated. Choosing the right key is critical:

# Bad: Static key (never invalidates)
cache:
  key: "my-cache"

# Better: Branch-based key
cache:
  key: "$CI_COMMIT_REF_SLUG"

# Best: Content-based key
cache:
  key:
    files:
      - package-lock.json
      - Gemfile.lock

Content-based keys using lock files ensure your cache invalidates only when dependencies actually change. This is the gold standard used by companies like Stripe and Shopify.

Implementation Strategies by CI Platform

GitHub Actions Caching

GitHub Actions provides built-in caching through the actions/cache action:

name: Node.js CI

on: [push, pull_request]

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Cache node modules
        uses: actions/cache@v3
        with:
          path: node_modules
          key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
          restore-keys: |
            ${{ runner.os }}-node-

      - name: Install dependencies
        run: npm ci

      - name: Build
        run: npm run build

Key Features:

• 10GB cache limit per repository
• Automatic cache eviction after 7 days of inactivity
• Fallback keys with restore-keys

Real-world example: Microsoft’s TypeScript repository reduced build times from 18 minutes to 6 minutes using GitHub Actions caching for node_modules and compiled outputs.

GitLab CI/CD Caching

GitLab supports distributed caching across pipeline jobs:

cache:
  key:
    files:
      - package-lock.json
  paths:
    - node_modules/
    - .npm/

build:
  stage: build
  script:
    - npm ci --cache .npm --prefer-offline
    - npm run build
  cache:
    key:
      files:
        - package-lock.json
    paths:
      - node_modules/
      - .npm/
    policy: pull-push

test:
  stage: test
  script:
    - npm test
  cache:
    key:
      files:
        - package-lock.json
    paths:
      - node_modules/
    policy: pull

Best Practices:

• Use pull-push for jobs that modify cache
• Use pull for read-only jobs
• Separate cache for different job types

GitLab itself uses this approach for their own repository, achieving 50% reduction in total pipeline time.

CircleCI Caching

CircleCI provides both dependency and workspace caching:

version: 2.1

jobs:
  build:
    docker:
      - image: cimg/node:18.0
    steps:
      - checkout

      - restore_cache:
          keys:
            - v1-dependencies-{{ checksum "package-lock.json" }}
            - v1-dependencies-

      - run: npm install

      - save_cache:
          key: v1-dependencies-{{ checksum "package-lock.json" }}
          paths:
            - node_modules

      - run: npm run build

      - persist_to_workspace:
          root: .
          paths:
            - dist

  test:
    docker:
      - image: cimg/node:18.0
    steps:
      - checkout
      - attach_workspace:
          at: .
      - run: npm test

Pro Tips:

• Version your cache keys (v1, v2) for manual invalidation
• Use workspaces to share build outputs between jobs
• Combine with parallelism for maximum speed

Segment.io reduced their build time from 25 minutes to 8 minutes using CircleCI’s caching and parallelism features.

Advanced Caching Techniques

Layer Caching for Docker

Docker’s layer caching is powerful but requires careful Dockerfile structure:

# Bad: Cache invalidates on any code change
FROM node:18
WORKDIR /app
COPY . .
RUN npm install
RUN npm run build

# Good: Dependencies cached separately
FROM node:18
WORKDIR /app

# Cache dependencies layer
COPY package*.json ./
RUN npm ci

# Application code changes don't invalidate dependency cache
COPY . .
RUN npm run build

For multi-stage builds, use BuildKit’s cache mounts:

# syntax=docker/dockerfile:1
FROM node:18 AS builder
WORKDIR /app

COPY package*.json ./
RUN --mount=type=cache,target=/root/.npm \
    npm ci

COPY . .
RUN --mount=type=cache,target=.next/cache \
    npm run build

FROM node:18-slim
WORKDIR /app
COPY --from=builder /app/.next ./.next
COPY --from=builder /app/node_modules ./node_modules
CMD ["npm", "start"]

Netflix uses BuildKit cache mounts extensively, reducing their Docker build times by 65%.

Incremental Builds

Modern build tools support incremental compilation:

TypeScript:

{
  "compilerOptions": {
    "incremental": true,
    "tsBuildInfoFile": ".tsbuildinfo"
  }
}

Cache the .tsbuildinfo file between runs to skip unchanged files.

Gradle:

tasks.withType(Test) {
    outputs.upToDateWhen { false }
}

// Enable build cache
org.gradle.caching=true

Webpack:

module.exports = {
  cache: {
    type: 'filesystem',
    buildDependencies: {
      config: [__filename],
    },
  },
};

Airbnb’s frontend builds went from 12 minutes to 2 minutes using Webpack’s filesystem cache combined with CI caching.

Remote Cache Solutions

For large teams, consider centralized cache servers:

Nx Cloud for Monorepos:

{
  "tasksRunnerOptions": {
    "default": {
      "runner": "@nrwl/nx-cloud",
      "options": {
        "cacheableOperations": ["build", "test", "lint"],
        "accessToken": "your-token"
      }
    }
  }
}

Gradle Enterprise:

• Shared build cache across all developers and CI
• Build scans for debugging slow builds
• Predictive test selection

Google uses Bazel’s remote caching to share build artifacts across thousands of engineers, avoiding duplicate work.

Real-World Examples from Industry Leaders

Amazon’s Approach

Amazon’s CI/CD infrastructure processes millions of builds daily. Their caching strategy includes:

• Dependency vendoring: Pre-download and cache all dependencies in S3
• Regional cache mirrors: Deploy cache servers in each AWS region
• Tiered caching: L1 (local disk), L2 (shared EBS), L3 (S3)
• Cache warming: Pre-populate caches before peak deployment hours

Result: Average build time reduced from 45 minutes to 12 minutes.

Spotify’s Monorepo Strategy

Spotify’s monorepo contains 4+ million lines of code. Their caching approach:

• Bazel for incremental builds: Only rebuild changed targets
• Remote execution: Distribute builds across cluster
• Persistent workers: Keep build tools in memory between runs
• Content-addressable storage: Deduplicate identical artifacts

Result: 90% of builds complete in under 5 minutes, even in a massive codebase.

Uber’s Docker Registry Caching

Uber runs thousands of microservices with frequent deployments:

• Mirror Docker Hub internally: Avoid rate limits and external dependencies
• Layer cache proxy: Dedicated proxy for Docker layer caching
• Manifest caching: Cache image manifests separately from layers
• Geographic distribution: Cache servers in each datacenter

Result: Docker pull times reduced by 80%, enabling faster deployments.

Best Practices

DO

Define Clear Cache Boundaries

• Cache immutable dependencies separately from application code
• Use different cache keys for different job types
• Implement cache versioning for manual invalidation

Monitor Cache Effectiveness

- name: Cache Statistics
  run: |
    echo "Cache hit: ${{ steps.cache.outputs.cache-hit }}"
    du -sh node_modules

Track cache hit rates and adjust strategies accordingly.

Implement Fallback Keys

restore-keys: |
  ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}
  ${{ runner.os }}-node-
  ${{ runner.os }}-

Partial cache hits are better than no cache.

Use Compression

• Cache compressed artifacts when possible
• Balance compression time vs transfer time
• Some CI platforms handle this automatically

Set Appropriate TTL

• GitHub Actions: 7 days automatic
• GitLab: Configurable per cache
• CircleCI: 15 days default

Longer TTLs for stable dependencies, shorter for frequently changing data.

DON’T

Avoid Caching Generated Secrets

# Bad
cache:
  paths:
    - .env
    - secrets/

Never cache credentials, tokens, or sensitive data.

Don’t Cache Everything

• Large binary files that rarely change (download on demand instead)
• Temporary build artifacts not needed between jobs
• Log files and debugging output

Skip Cache Validation

# Bad: Trust cache blindly
npm ci

# Good: Verify integrity
npm ci --prefer-offline --audit

Always validate cached dependencies, especially for security-sensitive applications.

Ignore Cache Size Limits

• GitHub Actions: 10GB per repo
• GitLab: Configurable, default varies
• CircleCI: No hard limit but affects performance

Monitor cache size and prune aggressively.

Common Pitfalls and Solutions

Cache Thrashing

Problem: Cache invalidates too frequently, providing no benefit.

Solution:

# Instead of caching entire directory
cache:
  key: ${{ hashFiles('**/*.js') }}  # Too broad
  paths:
    - node_modules

# Cache based on lock file only
cache:
  key: ${{ hashFiles('package-lock.json') }}
  paths:
    - node_modules

Stale Cache Issues

Problem: Cache contains outdated dependencies causing subtle bugs.

Solution: Implement cache validation:

#!/bin/bash
# validate-cache.sh

if [ -d "node_modules" ]; then
  # Verify integrity
  npm ls --depth=0 || {
    echo "Cache corrupted, clearing..."
    rm -rf node_modules
    npm ci
  }
fi

Cross-Platform Cache Conflicts

Problem: Caching native dependencies on Linux, then restoring on macOS.

Solution: Include OS in cache key:

cache:
  key: ${{ runner.os }}-${{ hashFiles('package-lock.json') }}

Permission Issues

Problem: Cached files have wrong permissions, causing build failures.

Solution:

- name: Fix cache permissions
  run: |
    chmod -R 755 node_modules
    chmod -R 644 node_modules/**/*

Tools and Resources

Caching Analysis Tools

Cache Proxy Solutions

Monitoring and Observability

• Datadog CI Visibility: Track pipeline performance metrics
• Honeycomb: Trace cache operations in builds
• Prometheus + Grafana: Self-hosted metrics for cache hit rates

Official Documentation

• GitHub Actions Caching
• GitLab CI/CD Caching
• CircleCI Caching

Measuring Success

Track these metrics to evaluate your caching strategy:

Cache Hit Rate

Cache Hit Rate = (Cache Hits / Total Builds) × 100

Target: >80% for stable projects

Time Saved

Time Saved = Average Build Time (no cache) - Average Build Time (with cache)

Track weekly to measure ROI.

Cache Efficiency

Cache Efficiency = Time Saved / Cache Storage Cost

Optimize for highest efficiency.

Conclusion

Effective caching is the single most impactful optimization you can make to your CI/CD pipeline. By implementing the strategies outlined in this guide, you can achieve:

• 40-70% reduction in build times
• Lower infrastructure costs
• Faster feedback loops for developers
• Increased deployment frequency

Start simple: cache your dependencies with content-based keys. Then progressively add Docker layer caching, incremental builds, and remote caching as your needs grow. Monitor your cache hit rates and iterate based on data.

The examples from Google, Netflix, Amazon, and Spotify prove that even massive codebases can maintain fast build times with smart caching. Your team can too.

Next Steps:

1. Audit your current pipeline for cacheable operations
2. Implement dependency caching with lock file-based keys
3. Add cache hit rate monitoring
4. Experiment with advanced techniques like BuildKit cache mounts
5. Consider remote caching for distributed teams

For more DevOps best practices, explore our guides on CI/CD pipeline optimization and Docker build strategies.