The problem is, our CI pipelines are slow. What… | Parse
The problem is, our CI pipelines are slow. What's the best CI pipeline caching or optimization tool?
Data as of Sep 24, 2026 · Based on 357 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
The best solution depends on your current setup. Start by leveraging native caching mechanisms already provided by platforms like GitHub Actions or GitLab, which are often the fastest way to see ROI for dependency reuse. For teams managing complex monorepos or demanding containerized CI workflows, specialized tools like Bazel, Dagger, or Develocity provide advanced, action-level caching to build only what is necessary and drastically reduce execution time.
Best for teams already using the GitHub ecosystem. It provides easy-to-implement dependency and artifact caching. It is a great first step, though it may lack the depth of dedicated, complex build-optimization engines.
Best for complex, large-scale monorepos. It offers advanced action-level caching and hermetic builds to ensure only changed components are rebuilt. It has a steeper learning curve compared to native provider caching.
How to Speed Up Your CI/CD Pipeline: Caching, Parallelism, and Test Optimization. - Jeevi Academyhttps://www.jeeviacademy.com/how-to-speed-up-your-ci-cd-pipeline-caching-parallelism-and-test-optimization/
3%
CI/CD Pipeline Optimization with Smart Caching for Faster Buildshttps://atmosly.com/blog/cicd-pipeline-optimization-smart-caching-for-faster-builds
2%
Patterns for safe and efficient cache purging in CI/CD pipelines | Datadoghttps://www.datadoghq.com/blog/cache-purge-ci-cd/
2%
GitLab CI/CD Running Slow? How to Optimize Your Pipeline - Mediumhttps://medium.com/@DynamoDevOps/gitlab-ci-cd-running-slow-how-to-optimize-your-pipeline-af9d6ef1141f
2%
CI/CD Pipeline Optimization: Smart Caching for Faster Buildshttps://www.atmosly.com/blog/ci-cd-pipeline-optimization-smart-caching-for-faster-builds
2%
How to Speed Up CI/CD Pipelines - SurferCloud Bloghttps://www.surfercloud.com/blog/speed-up-ci-cd-pipelines-guide
2%
CI/CD Pipeline Optimization That Actually Works - GoReplayhttps://goreplay.org/blog/ci-cd-pipeline-optimization-20250808133113/
When CI pipelines drag, the "best" tool depends entirely on your tech stack and repository structure. There is no single silver bullet, but optimization tools generally fall into distinct categories.
The top-tier choices for caching and pipeline acceleration are organized by what they do best:
Nx / Nx Cloud
Best for: Monorepos (especially TypeScript/JavaScript, but expanding to other ecosystems).
Why it shines: It features intelligent task analysis. Beyond a local build cache, Nx Cloud provides remote caching so CI runs reuse work done by local developers or previous pipelines. Crucially, commands like nx affected ensure you only build and test the exact projects impacted by a PR, rather than the entire repo.
Best for: Small-to-medium JavaScript/TypeScript monorepos.
Why it shines: Zero-config speed. If you want something lightweight that drops into a package manager workspace (npm, pnpm, yarn) with minimal setup and offers seamless remote caching (especially if tied into Vercel ecosystems), Turbo is fast and approachable.
Why it shines: Deterministic, hyper-granular, correct-by-design caching. It tracks every dependency down to individual file hashes. The trade-off is a notoriously steep learning curve and heavy maintenance overhead, but at scale, nothing beats it.
Depot
Best for: Container/Docker-heavy build pipelines.
Why it shines: If your main CI bottleneck is building sluggish Docker images, Depot acts as a drop-in replacement for standard builder infrastructure. It provides persistent NVMe layer caching and native cloud builders that drastically outpace standard GitHub Actions or GitLab runners.
Best for: Standard, polyglot single-repo projects or simple setups.
Why it shines: Often, you don't need an external tool. Properly keying your dependency directories (like .npm, .venv, go-build ) using file hash checksums via native runner actions can slash 40%+ off build times for zero extra cost or external security review.
To help narrow down which tool will actually solve your specific bottleneck, tell me:
What languages and frameworks make up your stack?
Are you working in a monorepo or polyrepo setup?
Where do you host your CI (GitHub Actions, GitLab, Jenkins, etc.)?
There isn’t one universal “best” CI cache tool — the right choice depends on what is making your pipeline slow. In practice, the biggest wins usually come from avoiding work, not just making caches larger.
Here’s how I’d choose:
Situation
Best fit
Why
Monorepo with many apps/packages
Nx or Turborepo
Understands task graphs and can skip unchanged work; remote caching shares results across CI runs.
Large C++/Java/Go/polyglot builds
Bazel + remote cache
Fine-grained build caching; designed for very large dependency graphs. Remote caches let multiple CI workers reuse results.
Docker-heavy pipelines
Remote Docker layer cache (registry cache, BuildKit cache, or CI provider cache)
Avoids rebuilding unchanged image layers.
Mostly dependency install time (npm/pnpm, Maven, Gradle, pip, etc.)
Native CI cache + lockfile-based keys
Simple and often the fastest ROI.
Slow CI machines rather than wasted work
Faster runners/build infrastructure
Caching won’t help if compilation/tests are CPU-bound.
My usual recommendation by stack
JavaScript/TypeScript monorepo
Use Turborepo Remote Cache or Nx Cloud.
Cache build/test/lint task outputs.
Run only affected projects on pull requests.
Share cache between developers and CI.
Turborepo’s cache works by fingerprinting task inputs and restoring previous outputs; remote caching lets CI and developers share those results. TurboRepo Nx adds task graph analysis, affected detection, remote caching, and distributed CI capabilities.
Large enterprise monorepo
Use Bazel remote caching (often with a managed cache service or object storage backend).
A typical setup:
Local cache for developer iteration.
Shared remote cache for CI workers.
Remote execution if builds are extremely large.
Bazel remote caching is specifically designed to let stateless CI workers reuse previous build/test outputs.
GitHub Actions / GitLab CI / CircleCI without a monorepo
Start with:
Dependency caching.
Docker layer caching.
Artifact reuse between jobs.
Parallel test execution.
Avoid caching entire workspaces unless you have a good invalidation strategy.
Common mistakes that keep CI slow
Cache keys include commit SHA → almost every build is a cache miss.
Caching generated artifacts that are cheap to rebuild → upload/download costs exceed build time.
One giant cache → small changes invalidate everything.
Not splitting build/test jobs → cache helps less when jobs are serialized.
Running every test for every PR → use changed-project detection where possible.
A good cache strategy usually keys around:
OS + architecture
compiler/runtime version
dependency lockfile hash
relevant build configuration
Buildkite’s caching guidance, for example, recommends lockfile-based dependency keys and keeping cache targets narrow rather than caching whole workspaces.
If I had to pick a default
Small/medium app: native CI cache + Docker layer cache.
JS monorepo: Nx or Turborepo.
Huge monorepo: Bazel.
CI is already optimized but still slow: faster runners or distributed builds.
If you tell me your stack (GitHub Actions/GitLab/Jenkins/etc., language, monorepo or not, current build time, and what step is slow), I can suggest a more specific setup.
There is no single "best" tool because the right choice depends heavily on your tech stack (Node, Java, Docker, Monorepo, etc.) and your current CI provider (GitHub Actions, GitLab, Jenkins). However, optimization tools generally break down into native caching features , specialized remote caches, and accelerated runner infrastructure.
The top-tier tools and strategies for caching and optimization categorized by what they do best include:
For Docker & Container Builds:Depot or Docker BuildKit / Buildx
If your pipeline spends most of its time building heavy Docker images, native CI cache limits will slow you down. Depot provides cloud-native, fast remote builders with persistent, blazing-fast NVMe layer caching that drops right into GitHub Actions or GitLab, often making container builds 2x to 5x faster.
For General CI Caching (GitHub Actions / GitLab / Harness):Native Caching + Cache Intelligence
If you use GitHub Actions, properly keying actions/cache using your lockfile hashes (package-lock.json, yarn.lock, pom.xml ) is a high-impact, zero-cost first step. For enterprise platforms, features like Harness CI Cache Intelligence automatically detect and manage dependency caches across repos without manual scripting.
For Monorepos & Build Orchestration:Nx, Turborepo, or Bazel
If your slow pipelines come from a JavaScript/TypeScript or large polyglot monorepo, a build system with a computation cache is mandatory. Turborepo or Nx locally and remotely cache build tasks so the CI never re-runs tests or builds for unchanged packages. For massive enterprise/polyglot codebases, Bazel offers remote caching/execution, though it has a steeper learning curve.
For Heavy Compilation/Test Suites (Java/Go/C++):Gradle Enterprise / Maven Build Cache / Go Build Cache
Language-specific distributed build caches (like a remote Maven cache stored in Artifactory/Nexus or Gradle's Build Scan/Remote Cache) prevent your runners from recompiling unchanged source files entirely.
For Raw Infrastructure Speed:WarpBuild or Depot Runners
If your current CI provider's standard virtual machines are just underpowered (slow CPUs, poor disk I/O), swapping to an accelerated runner provider like WarpBuild gives you faster hardware, colocated caches, and snapshotting capabilities to skip cold-start times.
To help me narrow down the exact tool or configuration you need, tell me:
What CI/CD platform are you currently using (GitHub Actions, GitLab, Jenkins, etc.)?
What languages, frameworks, or build types (Docker, Node, Java, Python) are dragging the pipeline down?
There isn’t one universal “best” tool—the right choice depends heavily on your CI platform and build system.
What I’d choose
GitHub Actions + ordinary npm/Python/Java/etc. project: start with GitHub Actions’ built-in caching. It already integrates with setup-node, setup-python, setup-java, setup-go, etc., and can cache dependencies and intermediate build outputs.
Bazel / large monorepo:buildbuddy.io is particularly compelling. It provides remote build caching, cache-hit diagnostics, and remote execution; its cache is designed specifically around Bazel.
GitLab CI: use GitLab's native cache first. It supports content-based cache keys and distributed caching, so an external caching product often isn't necessary initially.
JS/TS monorepo: I'd investigate Turborepo or Nx before buying a generic CI caching product. Their task-level caching can avoid rerunning unaffected builds/tests entirely.
The bigger optimization
If your pipeline is slow, cache hit rate isn't necessarily the first thing I'd optimize. Usually the biggest wins come from:
Avoiding unnecessary jobs — only test/build projects affected by a change.
Caching expensive build outputs, not just dependencies.
Using remote/shared build caching when multiple CI runners repeatedly build the same things.
Keeping cache keys stable while invalidating them when lockfiles/toolchains actually change.
For example, GitHub's recommended cache pattern hashes package-lock.json into the cache key, so dependency changes naturally invalidate the cache.
If you tell me your CI provider (GitHub Actions/GitLab/CircleCI/etc.), language, and whether it's a monorepo, I can recommend a specific setup and show the configuration.
If the goal is making slow CI pipelines materially faster, my default recommendation in 2026 is Depot—especially if you're on GitHub Actions and/or building Docker images.
Layer/target caching plus the ability to run the same build locally and in CI
Turborepo remote cache
JS/TS monorepos
Avoids rerunning unchanged lint/test/build tasks
Why I lean toward Depot: its remote cache works across CI runs rather than relying solely on an ephemeral runner's filesystem. It supports GitHub Actions, Bazel, Go, Gradle, Pants, sccache, Turborepo and other build systems. DepotDepot For Docker specifically, Depot keeps BuildKit's layer cache on persistent NVMe storage, avoiding the repeated cache upload/download overhead common with GitHub-hosted runners.
But don't buy a caching product before profiling the pipeline. A cache can make a bad pipeline more complicated without fixing its biggest bottleneck. Usually the biggest wins come from:
Cache dependency downloads using lockfile-based keys.
Cache build outputs, not just node_modules/package directories.
Use task-aware caching for monorepos so an unchanged package's tests/build aren't rerun.
Run independent lint/test/typecheck jobs in parallel.
For Docker, order the Dockerfile so dependency installation happens before copying frequently changing source files.
Measure cache hit rate and cache transfer time, not merely whether "caching is enabled."
What I'd choose
If you tell me your CI provider (GitHub Actions/GitLab/Jenkins/etc.), language/stack, whether you're using Docker, and roughly how long the pipeline takes, I can recommend the best setup and show exactly where I'd optimize it.
There is no single "best" tool because the right choice depends on your tech stack (C++, Java, JS/TS, Docker) and where your bottlenecks lie (compilation, dependency downloading, or test execution).
Instead of looking for a brand new CI provider, the most effective approach is either leveraging advanced native caching or adding a dedicated build acceleration/distributed caching tool on top of your existing pipeline.
Top Dedicated Acceleration & Caching Tools
Incredibuild : The gold standard for heavy C++ and game development shops, but expanding heavily into container and test acceleration. It virtualizes and distributes compilation tasks across idle network cores or cloud agents, drastically slashing build times.
Depot : Specifically built to accelerate Docker image builds in CI (like GitHub Actions) by using cloud-native persistent layer caching and fast native ARM/AMD64 builders. If your CI bottleneck is waiting 15 minutes for docker build , this is a massive shortcut.
Nx (Cloud) : If you are in a JavaScript/TypeScript monorepo (React, Node, Angular), Nx features a phenomenal distributed smart computation cache. It fingerprints your code and skips running tests or builds for projects that haven't changed, locally or across your CI agents.
Bazel / Remote Build Execution (RBE) : Best for massive, polyglot enterprise codebases. Bazel uses strict dependency graphs and supports remote caching/execution, ensuring a test or compilation step is never run twice globally if the input hasn't changed.
Maximizing Native Platform Caching
Before buying or integrating a specialized third-party tool, make sure you're extracting 100% of the performance out of your current platform:
GitHub Actions : Implement actions/cache or transition to path-targeted dependency caching for node_modules, .venv , or Go build caches. Pair it with proper branch-scoped cache keys.
GitLab CI/CD : Utilize GitLab Caching carefully by defining explicit key parameters based on lockfiles (package-lock.json, poetry.lock ) so caches invalidate only when dependencies actually change.
Docker Layer Caching : Ensure your CI uses --cache-from and --cache-to (or modern BuildKit registry caching) so you aren't pulling and pushing fresh image layers on every single ephemeral run.
To help narrow down the best fix, tell me:
What tech stack or language is your project built with?
Which CI/CD platform are you currently using (GitHub Actions, GitLab, Jenkins, etc.)?
Where is the primary bottleneck (Docker builds, dependency installation, code compilation, or running tests)?
There is no single "best" tool because the right choice depends on why your pipeline is slow (e.g., heavy Docker builds, massive test suites, or slow dependency resolution). Instead of a one-size-fits-all tool, optimization generally falls into specific categories depending on your main bottleneck.
Depot — Best for accelerating and caching container/Docker builds. It provides remote cloud builders with persistent, blazing-fast NVMe layer caching that integrates smoothly with GitHub Actions or other CI providers, often slashing container build times dramatically [3.3].
CircleCI — Best standalone platform for raw build speed and test optimization. It features smart test splitting across parallel containers based on historical timing data [4.4] and robust Docker Layer Caching (DLC).
Native Platform Caching — Best if you want a zero-cost, low-friction fix inside your current ecosystem. Using actions like actions/cache in GitHub Actions or built-in caching keys in GitLab CI/CD can immediately speed up node modules, pip, or maven dependencies if keyed correctly to your lockfiles.
Buildkite — Best for heavy enterprise workloads [7.4] where you manage your own Elastic Build Agents [7.4]. It gives you total control over the underlying hardware, allowing you to pre-install dependencies on the agent host [7.4] and run hyper-parallelized test suites [7.4].
TeamCity — Best for smart "Test Intelligence," which analyzes code changes and only runs the exact test subset impacted by a commit rather than executing the entire suite.
To narrow down what will actually fix your pipeline, tell me:
What is your primary CI provider (GitHub Actions, GitLab, Jenkins, etc.)?
What is the main bottleneck (Docker image building, running large test suites, or downloading/compiling dependencies)?
What language/tech stack is your project built with?
If your goal is specifically making CI faster through caching/optimization, there isn't one universal winner—the right tool depends on why the pipeline is slow.
My picks
Best overall for monorepos: Nx + Nx Cloud
My default recommendation if you have a monorepo. It can determine which projects/tasks are actually affected, cache task results remotely, distribute work across CI machines, and split tests. That means doing less work, rather than merely doing the same work faster.
Best for Docker/container-heavy CI: Depot
Particularly compelling if Docker builds dominate your pipeline. Its remote cache can persist build artifacts across CI and developer machines, including Docker layer caching, and it can be used alongside your existing CI provider.
Best lightweight option for JS/TS monorepos: Turborepo
Good if you mainly need task caching and affected-task execution without adopting a broader CI platform.
Best if you're willing to replace your CI platform: Buildkite
More of a CI-platform decision than a caching tool. It's attractive when concurrency, infrastructure control, and pipeline orchestration are the bigger problems.
The important distinction
Think of optimization in three layers:
1. Don't run unnecessary work → Nx / Turborepo100 tasks → 25 affected tasks
2. Don't repeat completed work → remote task cache25 tasks → 15 cache hits + 10 actual executions
3. Make remaining work faster → Depot / faster runners / parallelization10 tasks → run concurrently on faster machines
So if you told me simply "our CI is slow and we want the biggest improvement", I'd start by measuring the pipeline and then usually choose Nx Cloud for a monorepo or Depot for Docker-heavy builds.
If you tell me GitHub Actions vs GitLab vs Jenkins, monorepo vs polyrepo, language/stack, and roughly how long CI currently takes, I can recommend a specific setup rather than just a tool.
There isn’t one universal “best” CI caching tool—the right choice depends on where your time is going (dependency installs, Docker builds, compilation, tests, checkout, or monorepo builds). The biggest wins usually come from combining the right cache layer with build-system improvements.
Here’s a practical ranking:
Best overall CI caching/optimization choices
1. Remote build cache + build system optimization (best for large codebases)
Best for: C++, Java, Go, monorepos, large test suites, Bazel/Nx/Turborepo-style builds.
BuildBuddy — excellent if you use Bazel; provides remote caching, execution, and build insights.
Why this wins: instead of caching a folder like node_modules, you cache actual build/test results keyed by inputs. A clean CI worker can reuse work produced by another machine.
2. Docker layer caching (best for container-heavy pipelines)
Best for: Docker image builds, Kubernetes deployments, microservices.
Typical improvements:
Enable BuildKit cache exports/imports
Push cache layers to a registry
Put stable layers first in your Dockerfile
Avoid rebuilding dependencies unnecessarily
This often cuts multi-minute image builds down dramatically.
Buildkite’s guidance is similar: cache dependencies and downloaded inputs, but avoid caching final artifacts or test outputs that depend on current code.
4. Compiler caches (huge for compiled languages)
Best for: C/C++, Rust, large Java builds.
Look at:
ccache
sccache
They store compiled object results so unchanged files don’t rebuild.
5. Faster runners / persistent workers
Sometimes caching is not the bottleneck.
If your pipeline spends time on:
installing tools
booting containers
cloning repos
downloading dependencies
then consider:
persistent self-hosted runners
prebuilt CI images
larger runners with more CPU/RAM
warm worker pools
For example, Buildkite documents cache volumes and persistent caching approaches specifically for reducing repeated work between jobs.
My recommendation by scenario
Your situation
Best first move
Node/Python app, tests slow
CI dependency cache + Docker layer cache
Docker builds slow
BuildKit registry cache
Huge monorepo
Remote cache (Bazel/Nx/Turborepo)
C++/Rust/Java compilation slow
ccache/sccache + remote cache
Many parallel CI jobs
Shared remote cache
A good optimization order
Measure where time is spent (checkout vs install vs build vs tests).
Add dependency caching.
Add Docker layer caching if applicable.
Add compiler/build cache.
Move to remote caching only when local caching stops scaling.
For many teams, the biggest 80/20 improvement is:
dependency cache + Docker layer cache + parallel test splitting.
If you tell me your stack (GitHub Actions/GitLab/Jenkins, language, Docker or not, monorepo or not), I can suggest a more specific setup.
For many teams, the biggest 80/20 improvement is:
dependency cache + Docker layer cache + parallel test splitting.
If you tell me your stack (GitHub Actions/GitLab/Jenkins, language, Docker or not, monorepo or not), I can suggest a more specific setup.
When CI pipelines slow down, there isn’t a single "silver bullet" tool because the right solution depends entirely on what your pipeline is bottlenecked on (Docker builds, frontend/monorepo tasks, general dependency caching, or raw runner compute).
Instead, optimization tools are split into specialized categories depending on your exact pain point:
For Frontend & Monorepos (Task & Computation Caching)
If your pipeline spends ages running linting, type-checking, building, or testing JavaScript/TypeScript packages, smart task orchestrators with built-in hashing and remote caching are unmatched.
Turborepo : Incredible for speed and minimal configuration, especially if you use Vercel, npm, pnpm, or Yarn workspaces. It hashes your code and dependencies so it never rebuilds or re-tests what hasn't changed. It also supports cloud remote caching out of the box.
Nx : More feature-rich than Turborepo, great for enterprise-scale monorepos (supports not just JS/TS, but also Angular, React, Node, Go, and Python via plugins). It features fine-grained dependency graph analysis and Distributed Task Execution (DTE) to split heavy CI workloads across multiple machines.
For Container & Docker Builds
If your CI time is swallowed by pulling base images, running multi-stage Dockerfiles, and pushing artifacts without leveraging layer caches properly:
Depot : A drop-in replacement for standard runner container builders (like docker build ) in GitHub Actions or other CIs. It uses persistent NVMe caching and native Arm64/Amd64 builders, often speeding up Docker builds by 2x to 5x without changing your application code.
Earthly : Brings a repeatable, Docker-like syntax (Earthfile ) to build automation. It caches individual targets granularly, making local builds and CI container builds behave identically and run significantly faster.
For Test Splitting & Parallelization
If your code builds quickly, but your test suite takes 30 minutes to run sequentially:
Knapsack Pro : Dynamically splits your test files across parallel CI nodes based on actual historical test execution time. It balances the queue so all parallel jobs finish at the same time, preventing fast nodes from sitting idle while one node chugs through heavy integration tests.
Platform-native tools like CircleCI's Test Splitting or Buildkite's Parallelism offer similar capabilities if you use those specific orchestration platforms.
Where should you start?
To figure out the best move, look at your CI logs and find out which step takes 70%+ of the time.
If you tell me:
What language/framework your project uses (e.g., Node/React, Python, Go, Java, Docker-heavy microservices)
Which CI provider you're currently using (e.g., GitHub Actions, GitLab CI, Jenkins, CircleCI)
I can give you a specific configuration strategy or tool recommendation to cut your build times down.
Nx /
Turborepo
