Dagger isn't a universal solution - it solves specific problems really well and creates new ones you didn't know you had.
Where It Actually Helps
"Works on My Machine" Hell
If you spend more than an hour a week debugging why shit works locally but breaks in CI, Dagger might save your sanity. Same containers everywhere means no more surprises about Python versions, missing system packages, or environmental differences.
Real example: Your Django app uses psycopg2-binary locally but the CI has psycopg2 compiled against a different PostgreSQL version. With Dagger, the same container with the same exact dependencies runs everywhere. Problem solved, hair saved.
Monorepo Nightmares
If you're managing 5+ services in a monorepo and your CI takes 20+ minutes because it rebuilds everything when someone touches a README, Dagger's caching can actually help. When it works, only the changed service rebuilds. When it doesn't work, you'll spend a day figuring out why touching one service invalidated the cache for everything else.
The intelligent caching is real, but "intelligent" doesn't mean "always works correctly."
Multi-Language Chaos
One pipeline handling Go backend, React frontend, Python ML stuff, and Terraform deployments sounds great in theory. In practice, you'll spend time figuring out which container has the right version of Node.js while the Python containers are downloading PyTorch for the 50th time.
It works, but don't expect it to be magic. You're still dealing with dependency management, just in containers instead of on bare metal.
AI Integration (New in 2025)
They've added LLM support where AI agents can supposedly analyze your code and generate tests within pipelines. Check out their LLM integration guide and AI quickstart for examples. It's legitimately cool when it works, which is about 60% of the time. The other 40% you're debugging why the AI decided your perfectly valid code needs 47 unit tests for a hello world function.
Unless you're already comfortable with both Dagger and LLM integration, maybe start with basic CI/CD before adding AI to the mix.
The Actual Getting Started Experience
Step 1: Install and Realize Docker is Required
You need Docker running first. Not Docker Desktop necessarily, but some container runtime. Check the installation guide for alternatives like Podman or Colima. If you're on a corporate machine with restricted Docker access, you might be fucked before you start.
## macOS
brew install dagger/tap/dagger
## Linux (check the version - 0.14.0 might be old)
curl -L https://dl.dagger.io/dagger/install.sh | sh
## Windows (good luck)
## Use the PowerShell script or just use WSL2
Step 2: Initialize and Watch It Generate Boilerplate
cd your-project
dagger init --source=. --name=my-module --sdk=go
This creates a dagger.json config and some Go boilerplate. Pick Go unless you have strong reasons not to - the other SDKs feel like afterthoughts.
Step 3: Write Your First Function and Immediately Hit Issues
func (m *MyModule) Build(source *dagger.Directory) *dagger.Container {
return dag.Container().
From("golang:1.22-alpine").
WithMountedDirectory("/src", source).
WithWorkdir("/src").
WithExec([]string{"go", "build", "-o", "app", "."})
}
This looks simple but you'll discover:
- Alpine might not have the C libraries your Go dependencies need
- The container doesn't have git, which some Go modules require
- Your build might need environment variables that aren't set
Step 4: Debug Locally (The Good Part)
dagger call build --source=.
dagger call build --source=. terminal # This actually works and is useful
The terminal access for debugging is genuinely helpful. When things break, you can poke around inside the container instead of guessing. Last week, spent 3 hours debugging why our Node.js build was failing with "Module not found". Jumped into the container terminal, ran ls node_modules, realized the fucking thing was installing packages as root but running the build as nobody. Fixed with one WithUser("root") call.
Step 5: CI Integration and Memory Surprises
Add the GitHub Action to your workflow and watch it OOM on the default 2GB runners. Check the CI integration docs for memory requirements and GitHub Actions setup guide. Bump to 8GB or 16GB and try again.
How Teams Actually Adopt This
Start Small or Fail Big
Don't try to migrate everything at once. Pick your most annoying CI job - the one that breaks for mysterious reasons or takes forever - and convert that first. Learn from the pain before expanding.
The teams that succeed:
- Pick one service/component to start with
- Spend 2-4 weeks learning container orchestration quirks
- Gradually add more pieces once the first one is stable
- Accept that caching optimization is ongoing, not one-and-done
Team Size Reality Check
- Small teams (2-5): Probably not worth it unless your current CI is genuinely broken. The learning curve will kill productivity for weeks.
- Medium teams (6-20): Sweet spot if you have container-savvy people and complex builds. The wins can be real.
- Large teams (20+): Best ROI because the infrastructure investment gets amortized across more developers.
What You Actually Need
Development Machines
- 16GB RAM minimum, 32GB preferred (seriously, don't try this on 8GB)
- 50GB+ free disk space for images and cache
- Fast internet for the initial "download the entire internet" phase
CI Infrastructure
- Bump runners from 2-4GB to 8-16GB RAM
- Persistent cache storage (unless you enjoy waiting)
- Budget for higher bandwidth costs during cold starts
Human Investment
- 2-4 weeks of reduced productivity while people learn
- Someone needs to become the "Dagger person" who debugs cache issues
- Ongoing time investment optimizing and maintaining pipelines
Realistic Expectations
Ignore the marketing bullshit about instant productivity gains. Here's what actually happens:
Month 1-2: Productivity drops as team learns containers and debugging gets harder
Month 3-4: Productivity recovers as caching starts working and local testing proves valuable
Month 6+: Genuine improvements if you've optimized properly and the team is comfortable
Real timeline from our Go microservices migration: Week 1 was hell - builds that took 3 minutes in GitHub Actions now took 8 minutes cold in Dagger. Week 3, someone figured out the layer caching and builds dropped to 45 seconds. Week 8, we had a production incident where the staging environment worked perfectly but prod failed with ECONNREFUSED 127.0.0.1:5432. Took 2 hours to realize our Docker Compose setup was different from Kubernetes networking. Fun times.
The benefits are real for the right use cases, but they're not immediate and they're not free. You're trading YAML complexity for container orchestration complexity.
