Dagger CI/CD Platform: Technical Reference

Overview

Dagger is a CI/CD platform that lets you write pipelines in real programming languages (Go, Python, TypeScript) instead of YAML. Built by Solomon Hykes (Docker creator) to solve "works on my machine" problems through containerized builds.

Configuration Requirements

System Requirements

• Minimum RAM: 16GB (3-4GB for daemon, 8GB+ for builds)
• Preferred RAM: 32GB for production use
• Disk Space: 50GB+ for images and cache
• Container Runtime: Docker required (or Podman/Colima alternatives)
• CI Runners: Upgrade from 2-4GB to 8-16GB RAM minimum

Critical Failure Modes

• Memory Exhaustion: Default 2GB runners will OOM, builds fail mysteriously
• Cache Invalidation: Comment changes can break entire build cache
• Container Dependencies: Alpine missing C libraries breaks Go builds
• Network Issues: 127.0.0.1 references fail in Kubernetes vs Docker Compose
• Permission Problems: Root vs user container execution conflicts

Resource Requirements

Time Investment

• Learning Curve: 2-4 weeks reduced productivity during adoption
• Team Size Impact:
  • Small teams (2-5): Not worth it unless CI severely broken
  • Medium teams (6-20): Sweet spot with container-savvy developers
  • Large teams (20+): Best ROI due to scale

Infrastructure Costs

• CI Runner Upgrades: 4x memory requirements (2GB → 8GB minimum)
• Storage: Cache can balloon to 50GB+ per project
• Bandwidth: Higher costs during cold starts downloading images

SDK Reality Check

SDK	Status	Use Case	Issues
Go	Production-ready	Primary choice, internally used	None major
Python	Functional	Data teams, Python-only shops	Translated from Go, feels awkward
TypeScript	Basic	Simple builds only	Rough edges, limited features
PHP	Exists	None recommended	Never seen in production

Performance Characteristics

Build Speed Reality

• Cold Builds: Slower than traditional CI (container overhead)
• Cached Builds: 10x faster when cache works properly
• Local Testing: Immediate feedback vs 10-minute CI waits
• Cross-platform: ARM64 from x86 extremely slow

Caching Behavior

• Layer Caching: Works like Docker, usually reliable
• Function Caching: Can break mysteriously on trivial changes
• Dependency Caching: Saves time once warm, first build downloads everything
• Cache Killers: File timestamps, env var order, mount path differences

Critical Warnings

Enterprise Blockers

• Security Policies: "No persistent daemons" rules make this impossible
• Docker Access: Security teams hate Docker daemon requirements
• Secret Management: Don't use as primary secret store
• Audit Trails: May not meet enterprise compliance requirements

Operational Pain Points

• Cache Debugging: Spend entire days figuring out why cache broke
• Memory Leaks: Engine can consume 12GB+ RAM unexpectedly
• Container Cleanup: Manual docker system prune required regularly
• Alpine Issues: Missing system libraries break builds silently

Decision Criteria

Use Dagger If:

• Spending 1+ hours/week debugging "works locally" issues
• Managing 5+ services in monorepo with slow rebuilds
• Team has Docker expertise and 15+ developers
• Current CI randomly fails for unknown reasons
• Need multi-language pipeline coordination

Don't Use Dagger If:

• Simple builds that already work
• Team lacks container experience
• Memory-constrained CI environment
• Enterprise with strict security policies
• Budget-sensitive (higher infrastructure costs)

Migration Strategy

Recommended Approach

1. Start Small: Convert most problematic CI job first
2. Run Parallel: Keep existing CI while testing Dagger
3. Gradual Migration: One service at a time over months
4. Team Training: Expect 4-8 weeks learning curve without Docker experience

Success Indicators

• Month 1-2: Productivity drops during learning
• Month 3-4: Productivity recovers with working cache
• Month 6+: Genuine improvements if properly optimized

Common Failure Scenarios

Installation Issues

• Docker Desktop: Corporate restrictions block daemon access
• WSL2 Windows: Complex setup, use Linux if possible
• Memory Limits: 8GB machines unusable for development

Runtime Problems

• OOM Kills: CI runners silently fail with insufficient memory
• Cache Corruption: dagger system prune fixes mysterious issues
• Network Timeouts: Image downloads fail on slow connections
• Permission Denied: Root container vs user execution conflicts

Integration Reality

GitHub Actions

• Use official Dagger GitHub Action
• Requires runner memory upgrades
• Works better than Jenkins integration
• Persistent cache storage recommended

Local Development

• Terminal access for container debugging actually useful
• Same containers everywhere eliminates environment differences
• High memory usage impacts laptop performance
• IDE support decent when working

Support and Ecosystem

Community

• Discord: Active, core team responds
• Documentation: Better than average for open source
• Module Registry: ~100 modules, inconsistent quality
• GitHub Issues: Check before implementing, common problems documented

Long-term Viability

• Open source (Apache 2.0) survives company failure
• Active development with weekly releases
• Container orchestration requires ongoing maintenance
• Community maintenance possible but slower development

Critical Commands

# Installation
brew install dagger/tap/dagger

# Initialize project
dagger init --source=. --name=my-module --sdk=go

# Local testing
dagger call build --source=.
dagger call build --source=. terminal  # Debug access

# Cache management
dagger system prune  # Fix mysterious cache issues
docker system prune -af  # Reclaim disk space

Bottom Line Assessment

Dagger solves real CI/CD pain points but introduces container orchestration complexity. Value comes from eliminating "works locally" debugging sessions and enabling fast local iteration. Cost is higher infrastructure requirements, team learning curve, and ongoing cache optimization.

Best fit: Medium-large teams with container expertise and genuinely problematic existing CI/CD. Not worth it for small teams or simple builds that already work reliably.