Nx and Turborepo share the lead almost evenly, with Nx recommended for larger polyglot setups and Turborepo for lightweight JavaScript repositories. When buyers ask specifically for a continuous integration tool built for monorepo architectures, Nx stands out as the usual answer.
Since July, Depotrising has become a common answer for teams resolving slow CI pipelines, while Nxrising has expanded its presence across setup questions. Over the same period, mentions of Gradlefalling and Harness Continuous Integrationfalling have softened across the market.
Latest insights
Week of Sep 21–27 · Since Jul 5
Bazel Gap between platforms: The platform gap for Bazel in Monorepo CI Build Optimization Tools widened to 22.7 points, Google AI Mode ahead
Buildkite Slipping: Buildkite now trails GitLab CI in Monorepo CI Build Optimization Tools, #7 to #5
Buildkite Gap between platforms: ChatGPT Search now names Buildkite 22.7 points more often than Google AI Mode in Monorepo CI Build Optimization Tools, a widened gap
Buildkite Slipping: Buildkite ranked #6 in Monorepo CI Build Optimization Tools last week and #7 this week
GitLab CI Gaining: GitLab CI moved ahead of Buildkite in Monorepo CI Build Optimization Tools
The best CI pipeline caching or optimization tool depends on your CI provider, language stack, and whether your bottleneck is dependency installation, compilation, testing, or Docker image builds.
My default recommendation is to start with a remote build cache such as Depot or BuildBuddy, or a build-system-native cache if you already use Bazel, Gradle, or Turborepo. If you're primarily losing time to repeated dependency installs, your CI provider's built-in cache may be enough.
The important distinction is that caching saves work you've already done, while pipeline optimization also reduces unnecessary work, parallelizes independent tasks, and avoids rebuilding unchanged components.
I need a continuous integration tool that is optimized for monorepo architectures.
Best monorepo setup for fast CI and smart caching?
1. Depot — best for Docker-heavy builds
(Best for container builds)
A strong choice if Docker image builds dominate your CI time. It provides remote builders, persistent build caching, and faster access to build infrastructure without requiring you to replace your entire CI setup.
Best fit: Docker, BuildKit, GitHub Actions, containerized applications.
2. BuildBuddy — best for Bazel and large codebases
Built around Bazel remote caching, remote execution, and build observability. Especially compelling when builds are CPU-intensive and can be distributed across multiple workers.
Best fit: large monorepos, C++/Java/Go projects, Bazel users.
3. Turborepo — best for JavaScript/TypeScript monorepos
Caches task outputs and skips work for unchanged packages. Remote caching shares results across developers and CI machines, while task filtering and parallelization reduce unnecessary work.
Best fit: Node.js, React, Next.js, pnpm/npm workspaces.
Reuses compiled outputs and other cacheable task results across builds and machines. This is usually the sensible starting point for slow Android, Kotlin, or Java builds already using Gradle.
Best fit: Android, Kotlin, Java, Gradle-based projects.
5. Buildkite — best when the whole pipeline needs optimization
A broader CI platform rather than just a cache. It offers configurable execution infrastructure, pipeline orchestration, and caching options for dependencies and build data.
Best fit: complex pipelines, custom runners, multiple languages and teams.
Before buying a new tool, identify where the time is going:
CI bottleneck
First thing to try
Installing dependencies every run
Cache package-manager downloads, keyed by lockfile and OS
Recompiling unchanged code
Remote build cache
Slow Docker builds
Layer caching and a remote builder such as Depot
Too many tests
Parallel test splitting and selective test execution
Monorepo builds everything
Dependency-aware task filtering and change detection
Long waits before jobs start
Faster runners, fewer queued jobs, and right-sized concurrency
A key detail: cache hits must be correct, and restoring a cache must cost less time than rebuilding. Cache generated dependencies and deterministic build outputs; don't blindly cache stale test results or final artifacts that need to be published.
Let me narrow this down to the best fit
1. Which CI platform do you use?
GitHub Actions
GitLab CI
CircleCI
Jenkins
Buildkite
Other
2. What's your main tech stack?
TypeScript / Node.js
Java / Kotlin / Android
Go / Rust / C++
Docker / Kubernetes
Polyglot monorepo
Other
3. What's the biggest bottleneck?
Dependency installation
Builds / compilation
Docker image builds
Tests
Runner startup / queues
Everything
4. What's your priority?
Lowest cost
Fastest builds
Best balance of speed and cost
Self-hosted / data control
[Find the best tool for my pipeline]
With those details, I can recommend a specific tool and a practical implementation plan rather than just another caching product.
Instead of hunting for a completely separate tool, the "best" optimization usually depends on where your bottleneck is hiding (compilation, dependency downloading, or test execution). Most major CI platforms already have powerful built-in caching, but specialized accelerators fill the gaps they miss.[](https://www.incredibuild.com/blog/best-ci-cd-pipeline-tools-2026)
Top Built-In Platform Features
GitHub Actions Caching : Excellent if your code lives on GitHub. It uses content-hashed keys (via hashFiles ) to cache node_modules, Go build caches, pip packages, or Gradle/Maven dependencies across runs.
When native platform caches aren't enough (e.g., massive C++, Rust, or Java monoliths, or slow Docker layer rebuilds), look at these:
Incredibuild : The gold standard for heavy compiled languages (C++, C#, game dev). It turns your network into a virtual distributed supercomputer, offloading compilation tasks across idle machines.
Bazel Remote Caching : If you use Bazel, setting up a remote cache/execution backend ensures that if any developer or past pipeline built a target, no CI node ever rebuilds it again.
Gradle Enterprise / Develocity : Essential if your stack is heavy on Java/Gradle/Maven. It provides deep build-scan analytics and a cross-environment shared build cache that drastically cuts down multi-module project times.
To help narrow down what will actually speed things up for you, tell me:
What language or tech stack are you building (e.g., Node.js, Go, C++, Java, Docker images)?
My top recommendation is Nx Cloud if your goal is to shorten CI pipelines by avoiding redundant builds and tests. It adds remote caching and intelligent task scheduling to your existing CI provider, so you don't necessarily need to migrate your pipelines.
The best choice depends on whether your bottleneck is repeated work, slow build tooling, or the CI runners themselves.
Tools worth considering
1. Nx Cloud — Best for monorepos
(Top pick)
Remote caching for build, test, and lint results.
Runs only tasks affected by code changes.
Can distribute tasks across multiple CI machines.
Best for large repositories with many packages and repeated work.
Website:
2. Depot — Best for slow Docker builds
Remote build caching shared across CI and developer machines.
Accelerates container builds and incremental compilation.
Offers faster CI runners, including GitHub Actions-compatible runners.
Best if Docker image builds or slow runner hardware dominate your pipeline.
Website:
3. Turborepo — Best for JavaScript/TypeScript projects
Caches task outputs and skips repeated work.
Supports remote caching across developers and CI.
Integrates with existing CI providers.
Best for JS/TS monorepos that need straightforward caching without adopting a broader orchestration platform.
Website:
How I'd choose
If your main problem is…
Start with
Rebuilding and retesting unchanged code
Nx Cloud
Slow Docker builds or underpowered runners
Depot
Repeated JavaScript/TypeScript tasks
Turborepo
A single-package project with an existing CI cache
The right choice depends on your tech stack and repository architecture:
Turborepo : Best for JavaScript/TypeScript projects and monorepos. It features high-speed, content-aware local and remote caching with a minimal configuration footprint, making it easy to drop into existing workspaces.[](]()]()
Yes. There are several effective tools for reducing CI/CD pipeline build times, and the biggest gains usually come from combining remote build caching, dependency caching, incremental builds, and smarter parallelization rather than relying on a single cache.
The best choice depends on your stack (JavaScript, Java, Python, Go, C/C++, etc.), your CI provider, and whether you have a monorepo.
Best CI caching and optimization tools
1. Nx
(Best for monorepos)
Nx caches build, test, and lint results based on task inputs. Its remote cache can share results across developers and CI workers, and its affected-project analysis helps avoid running tasks for untouched projects.
Best for: TypeScript, JavaScript, multi-project repositories, and teams running many redundant CI tasks.
Reported gains: Nx says teams commonly see 30–70% faster CI, depending on workload and cache reuse.
Turborepo fingerprints tasks and restores cached outputs instead of rerunning unchanged work. Remote caching lets CI and developers reuse one another's build results.
Best for: Node.js, React, Next.js, and JavaScript/TypeScript monorepos.
Bazel supports fine-grained incremental builds, remote caching, and distributed remote execution. It can reuse compilation and test outputs across CI machines.
Best for: Large monorepos, C++, Java, Go, and polyglot repositories with complex build graphs.
Trade-off: More build-system configuration and discipline are required.
Earthly uses layer-based caching and persistent cache mounts, and offers remote runners to preserve cache state across ephemeral CI workers without repeatedly uploading and downloading large caches.
Best for:Docker-heavy pipelines, container builds, and workflows that repeatedly install dependencies.
Offers explicit, key-based caching of directories and dependencies, along with cache volumes for supported hosted agents. It can also work with external caches and build artifacts.
Best for: Teams already using Buildkite or needing more control over cache keys, storage, and access.
Cache npm, pnpm, pip, Maven, Gradle, or other package-manager download directories. Key the cache to the operating system, toolchain where relevant, and lockfile hash. Keep dependency installation able to reconcile changes after a partial cache hit.
2. Enable remote build caching
Reuse compiled outputs and task results across CI jobs and branches when inputs match. This often delivers more than dependency caching alone because it avoids doing the actual build or test work again.
3. Run only affected projects
In monorepos, identify which projects and downstream dependencies are affected by a change. Avoid rebuilding every package for a small change.
4. Parallelize the critical path
Run independent tests and build steps concurrently, split oversized test suites, and eliminate unnecessary job dependencies. Caching cannot remove work that genuinely needs to run.
5. Optimize Docker builds
Put relatively stable instructions early in the Dockerfile, copy dependency manifests before frequently changing source files, and use BuildKit's external cache. Measure cache-transfer overhead as well as build time.
My recommendation
I'd choose the simplest tool that addresses your biggest bottleneck:
GitHub Actions + a conventional application: Start with native dependency caching and Docker BuildKit if you build images.
JavaScript/TypeScript monorepo: Evaluate Nx or Turborepo with remote caching.
Large, multi-language monorepo: Evaluate Bazel and remote caching.
Java/Kotlin with Gradle: Enable the Gradle build cache and investigate remote caching if you use ephemeral runners.
C/C++ or Rust compilation: Evaluate compiler caching before replacing your build system.
One important caveat: cache restores can themselves be expensive. Large archives, low cache-hit rates, incomplete cache keys, and frequent invalidation can erase the gains. Track median and p95 pipeline duration, cache-hit rate, time spent restoring caches, and compute cost before and after each change. Never put secrets in caches, and ensure cached outputs are reused only when all relevant inputs match.
I can narrow this down to the best tools for your pipeline
Earthly : Combines the familiarity of Dockerfiles with the build-graph execution of Makefile/Bazel. It caches individual targets inside container builds so you don’t repeat unaffected container layers during CI runs.
If you share what languages or frameworks your project uses and which CI platform you are currently on (e.g., GitHub Actions, GitLab CI, Jenkins), I can recommend the most efficient caching setup for your workflow.
