Canary Deployment Cheat Sheet

Techniques for gradually shifting production traffic to a new version while monitoring metrics to catch regressions early.

2 PagesAdvancedFeb 10, 2026

Core Concept

How canary releases limit blast radius.

Canary- A small subset of instances running the new version, receiving a small percentage of traffic
Baseline- The stable version still serving the majority of traffic during the canary phase
Progressive rollout- Gradually increasing the canary's traffic share (e.g. 5% -> 25% -> 50% -> 100%) as confidence grows
Automated analysis- Comparing canary vs baseline metrics (error rate, latency) to auto-promote or auto-rollback
Blast radius- The scope of users affected if the new version has a defect; canary keeps it small initially
Feature flags- Often paired with canaries to decouple deployment from feature exposure

Nginx Weighted Canary

Split traffic between stable and canary upstreams by weight.

nginx

upstream backend {    server 10.0.0.1:3000 weight=9;  # stable, ~90%    server 10.0.0.2:3000 weight=1;  # canary, ~10%}server {    listen 80;    location / {        proxy_pass http://backend;    }}

Kubernetes Canary (replica ratio)

Approximate traffic split via replica counts behind one Service.

yaml

# stable: 9 replicas, canary: 1 replica -> Service load-balances ~90/10apiVersion: apps/v1kind: Deploymentmetadata:  name: myapp-stablespec:  replicas: 9  selector:    matchLabels: {app: myapp}  template:    metadata:      labels: {app: myapp, track: stable}---apiVersion: apps/v1kind: Deploymentmetadata:  name: myapp-canaryspec:  replicas: 1  selector:    matchLabels: {app: myapp}  template:    metadata:      labels: {app: myapp, track: canary}

Metrics to Watch

What to monitor before promoting a canary.

Error rate- HTTP 5xx rate on canary compared to baseline over the same window
Latency (p95/p99)- Tail latency regressions often surface before average latency does
Saturation- CPU/memory/connection pool usage on canary instances
Business metrics- Conversion rate, checkout success, etc., for user-facing regressions that infra metrics miss

Pro Tip

Automate the rollback decision based on statistically significant metric deltas (not just eyeballing a dashboard) — tools like Flagger or Argo Rollouts can halt and revert a canary the moment error rates spike, faster than any human on-call.

Was this cheat sheet helpful?

Blue-Green Deployment Cheat Sheet

GitOps Cheat Sheet