Certainly! Here’s a clean comparison of Blue-Green Deployment, Canary Deployment, and A/B Testing without any icons:
1. Blue-Green Deployment
Concept:
You maintain two identical environments — one is the current live environment (Blue), and the other is where you deploy the new version (Green). Once tested, you switch all traffic to Green.
Key Characteristics:
| Feature | Description |
|---|---|
| Rollback | Fast — just switch back to the Blue environment |
| Traffic Routing | All users switch to the new version at once |
| Use Case | Zero-downtime deployments, easy rollback |
| Tools | AWS CodeDeploy, Kubernetes, load balancers |
2. Canary Deployment
Concept:
Release the new version to a small subset of users (e.g., 5%) and gradually increase the percentage while monitoring performance and error rates.
Key Characteristics:
| Feature | Description |
|---|---|
| Rollback | Easy — stop or revert the rollout at any stage |
| Traffic Routing | Gradual shift of traffic over time |
| Use Case | Minimize risk with real-time monitoring |
| Tools | AWS App Mesh, Argo Rollouts, Istio, CodeDeploy |
3. A/B Testing
Concept:
Split traffic between two or more versions (A = control, B = variation) to test user behavior or feature performance.
Key Characteristics:
| Feature | Description |
|---|---|
| Rollback | Depends — not always relevant (it’s a test) |
| Traffic Routing | Based on rules (cookies, geography, user IDs) |
| Use Case | Experimentation, data-driven feature decisions |
| Tools | LaunchDarkly, Optimizely, CloudFront, Lambda@Edge |
Summary Comparison
| Feature | Blue-Green | Canary | A/B Testing |
|---|---|---|---|
| Rollout Style | All at once | Gradual | Parallel groups |
| Rollback Ease | Very easy | Easy | Depends on outcome |
| Use Case | Fast switchovers | Safety-focused | Experimentation |
| Risk | Low | Very low | Medium |
| User Experience | Uniform | Progressive | Split/Randomized |
Let me know if you’d like a YAML deployment example for any of these strategies.