GitHub Actions handles 5 million workflow runs per day and sits in 62% of developers’ personal projects. Dagger, built by Docker’s original creators, takes a completely different approach: pipelines as code instead of YAML, with every step running in containers. The two tools aren’t really competitors, though. Dagger runs inside GitHub Actions (or any other CI system), replacing the YAML logic while keeping the runner infrastructure.
This comparison breaks down when to use GitHub Actions alone, when to add Dagger on top, and when the combination doesn’t make sense. Real benchmark numbers, pricing, and code examples included.
Current as of April 2026. Dagger v0.20.3, GitHub Actions January 2026 pricing.
How They Work: Fundamentally Different Models
GitHub Actions uses YAML workflow files (.github/workflows/*.yml) that define jobs and steps. Each job gets a fresh virtual machine. Steps run sequentially within a job, and parallelism requires splitting work into separate jobs with dependency declarations.
Dagger uses real programming languages (Python, Go, TypeScript). You write functions that the Dagger Engine executes inside containers. The engine builds a directed acyclic graph (DAG) from your function calls and runs independent operations in parallel automatically. No YAML anywhere.
Here’s the same pipeline (“install dependencies, run tests, build container image”) in both tools.
GitHub Actions (YAML)
name: CI
on: [push]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-go@v5
with:
go-version: '1.22'
- run: go test ./...
build:
needs: test
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USER }}
password: ${{ secrets.DOCKER_TOKEN }}
- uses: docker/build-push-action@v6
with:
push: true
tags: myapp:latestDagger (Python)
import dagger
from dagger import dag, function, object_type
@object_type
class CI:
@function
async def test(self, source: dagger.Directory) -> str:
"""Run Go tests"""
return await (
dag.container()
.from_("golang:1.22")
.with_mounted_directory("/src", source)
.with_workdir("/src")
.with_exec(["go", "test", "./..."])
.stdout()
)
@function
async def build_and_push(self, source: dagger.Directory, token: dagger.Secret) -> str:
"""Build and push container image"""
return await (
dag.container()
.from_("golang:1.22")
.with_mounted_directory("/src", source)
.with_workdir("/src")
.with_exec(["go", "build", "-o", "app", "."])
.with_registry_auth("docker.io", "myuser", token)
.publish("docker.io/myuser/myapp:latest")
)The Dagger version is more lines of code, but it’s testable, type-safe, and runs identically on your laptop. The GitHub Actions version is shorter but tied to GitHub’s runner environment.
Feature Comparison
| Feature | GitHub Actions | Dagger |
|---|---|---|
| Pipeline language | YAML | Python, Go, TypeScript (real code) |
| Local execution | No (third-party tools like act exist but are limited) | Yes, natively. Same pipeline, same caching |
| Caching | Manual (actions/cache), 10 GB limit per repo | Automatic content-addressed caching, no manual keys |
| Parallelism | Requires separate jobs with needs: declarations | Automatic DAG-based parallelism |
| Debugging | Re-run with debug logging, SSH into runner (Enterprise) | Interactive breakpoints, inspect any container state locally |
| Portability | GitHub only | Any CI system with Docker (GitHub Actions, GitLab CI, Jenkins, CircleCI) |
| Ecosystem | 20,000+ marketplace actions | 1,500+ Daggerverse modules |
| Vendor lock-in | High (YAML is GitHub-specific) | None (open source, runs anywhere) |
| Learning curve | Low (YAML is familiar) | Medium (requires programming knowledge) |
| Self-hosted runners | Yes | N/A (uses the host CI’s runners) |
Caching: Where the Performance Gap Shows
Caching is the single biggest technical difference between the two approaches.
GitHub Actions uses actions/cache with explicit cache keys. You define what to cache (node_modules, Go modules, Docker layers) and set restore keys for partial matches. The cache is limited to 10 GB per repository and shared across all branches. On ephemeral runners, cache misses are common because the cache must be downloaded fresh each run.
Dagger caches automatically. Every operation is content-addressed: if the inputs haven’t changed, the output is served from cache without re-running. This works the same locally and in CI. With Dagger Cloud, the cache is distributed across 26 global regions, which means even ephemeral CI runners get cache hits.
Real numbers from OpenMeter’s published case study: their CI pipeline went from 25 minutes (GitHub Actions alone) to 10 minutes (with Dagger Cloud caching), and then to 5 minutes when combined with faster runners. That’s a 5x improvement with a 50% cost reduction.
Pricing Comparison
| Item | GitHub Actions | Dagger |
|---|---|---|
| Free tier | 2,000 min/mo (private repos), unlimited on public repos | Engine is free and open source (Apache 2.0) |
| Paid compute | Linux 2-core: $0.006/min, macOS 3-core: $0.062/min | N/A (uses your existing CI runners) |
| Observability | Included (workflow logs, run history) | Dagger Cloud free: 1M events/mo. Team: $50/mo flat for up to 10 users |
| Self-hosted | Free (you manage the runners) | Free (you manage the Dagger Engine) |
| Enterprise | 50,000 min/mo included, then per-minute | Custom pricing, single-tenant option |
Dagger doesn’t replace your CI runner costs. It runs on top of GitHub Actions (or any other CI system). The $50/mo Dagger Cloud Team plan adds distributed caching and pipeline visualization. The engine itself is free.
Using Dagger Inside GitHub Actions
Dagger isn’t a replacement for GitHub Actions. It runs inside it. The official dagger/dagger-for-github action installs the Dagger CLI, spins up the Dagger Engine on the runner, and calls your functions. The GitHub Actions workflow becomes a thin wrapper:
name: CI with Dagger
on: [push]
jobs:
ci:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Test
uses: dagger/dagger-for-github@v7
with:
verb: call
args: test --source=.
- name: Build and Push
uses: dagger/dagger-for-github@v7
with:
verb: call
args: build-and-push --source=. --token=env:DOCKER_TOKEN
env:
DOCKER_TOKEN: ${{ secrets.DOCKER_TOKEN }}Your pipeline logic lives in the Dagger module (Python/Go/TypeScript code in your repo). The YAML just orchestrates when to call it. If you ever switch from GitHub Actions to GitLab CI, only the thin wrapper changes. The Dagger code stays identical.
When to Use GitHub Actions Alone
GitHub Actions without Dagger makes sense when:
- Simple workflows: lint, test, deploy. If your pipeline is under 50 lines of YAML and works reliably, adding Dagger creates unnecessary complexity
- Open source projects: GitHub Actions is free and unlimited for public repos. The ecosystem of 20,000+ marketplace actions means most tasks already have a pre-built action
- Teams unfamiliar with Go/Python/TypeScript: Dagger requires writing real code. If your team is more comfortable with YAML, the learning curve may not be worth it for simple pipelines
- GitHub-only shops: if you have no plans to use another CI provider, the portability benefit of Dagger doesn’t apply
When to Add Dagger
Adding Dagger on top of GitHub Actions (or any CI) makes sense when:
- Builds are slow: Dagger’s automatic caching and parallelism can cut build times significantly. The OpenMeter case study showed 2.5x improvement from caching alone
- “Works on my machine” problems: Dagger pipelines run identically locally and in CI because everything runs in containers. You debug the pipeline on your laptop, not by pushing commits and waiting
- Multi-CI environments: if your organization uses GitHub Actions for some projects and GitLab CI or Jenkins for others, Dagger provides a single pipeline codebase that runs on all of them
- Complex pipelines: once a YAML file exceeds ~200 lines with conditional logic, matrix builds, and custom scripts, it becomes difficult to test and maintain. Typed code with editor support scales better
- Vendor lock-in concerns: if you want the option to switch CI providers without rewriting pipelines, Dagger gives you that portability
Decision Matrix
| Scenario | Recommendation |
|---|---|
| Simple lint/test/deploy | GitHub Actions alone |
| Open source project | GitHub Actions alone (free unlimited minutes) |
| Builds over 10 minutes | Add Dagger for automatic caching |
| “It works locally but fails in CI” | Add Dagger for local/CI parity |
| Multi-CI (GHA + GitLab + Jenkins) | Dagger for portable pipeline code |
| 200+ line YAML workflows | Dagger for maintainable typed code |
| Monorepo with 50+ services | Dagger for fine-grained caching and parallelism |
| Team knows only YAML | GitHub Actions alone (lower learning curve) |
What Dagger Does Not Replace
Dagger is not a standalone CI/CD platform. It doesn’t provide runners, trigger mechanisms, or PR integrations. You still need GitHub Actions (or GitLab CI, Jenkins, etc.) for:
- Triggering pipelines on push, PR, schedule, or manual dispatch
- Providing compute (runner VMs or containers)
- Status checks on pull requests
- Integration with GitHub’s security features (Dependabot, code scanning)
- Marketplace actions for non-build tasks (notifications, deployments, approvals)
Dagger replaces the pipeline logic (what runs inside each step), not the pipeline infrastructure (when and where it runs).
Getting Started
If you want to try Dagger, start by installing it alongside your existing GitHub Actions workflows. No need to rewrite everything at once. Pick one slow or complex job, convert it to a Dagger function, and compare the results. The official dagger/dagger-for-github action makes the integration straightforward. For installation instructions on Ubuntu or Rocky Linux / AlmaLinux, see our dedicated guides.
