Rolling Updates vs Canary vs Blue/Green deployment strategies

In Kubernetes—and DevOps in general—how you deploy can be just as important as what you deploy. A solid deployment strategy helps reduce risk, improve availability, and roll out changes confidently.

This post breaks down key deployment strategies, with practical examples and real-world tradeoffs.

Rolling Update (Kubernetes Default)

• Replaces old pods with new ones incrementally.
• Controlled via maxUnavailable and maxSurge.
• No traffic routing needed—Kubernetes handles everything.

strategy:
  type: RollingUpdate
  rollingUpdate:
    maxUnavailable: 1
    maxSurge: 1

Pros: Simple, efficient, minimal resource cost
Cons: No version isolation—bugs in new pods can affect users immediately

Blue/Green Deployment

• Two full environments: Blue (live), Green (staging/testing).
• New version is deployed to Green, tested, and traffic is switched over when ready.
• Rollback is instant—just point traffic back to Blue.

Pros: Safe, instant rollback
Cons: Requires extra infra and routing control (Ingress or Load Balancer)

How to implement

• You deploy two versions of your app: Blue (current live) and Green (new candidate).
• Once Green is validated, you switch traffic to it using your ingress or load balancer.
• Rollback = switch back to Blue.

When to use

• Mission-critical apps where downtime is unacceptable
• You want fast rollback without redeploying
• You have capacity for two environments

Canary Deployment

• Gradual rollout to a subset of users.
• Observability is key—monitor metrics, logs, errors before scaling up.

Example:

5 total replicas
→ 1 pod runs v2 (canary)
→ 4 pods run v1 (stable)

Pros: Real-world testing, minimal risk
Cons: Needs advanced tooling (Istio, Flagger, Argo Rollouts), proper metrics & alerting

How to implement

• Deploy new version gradually (e.g., 10% of traffic → 25% → 50%)
• Monitor errors, metrics, latency
• Promote if healthy, rollback if not

When to Use

• You want to test new features in production
• Risk needs to be managed progressively
• You have observability in place (Prometheus, Grafana, etc.)

Final thoughs

Choosing the right deployment strategy depends on:

• Risk tolerance
• App criticality
• Infrastructure budget
• Observability maturity

In my homelab, I use Rolling Updates, but I’m actively exploring Canary + GitOps + Flagger for more controlled experiments.