Deployments don’t go wrong when you hit the Deploy button. They go wrong days or weeks earlier, when planning is fuzzy, coordination is weak, and the team hasn’t rehearsed what could happen.

This is where the idea of a Deployment Dry Run comes in: practicing your production release as if it were real—without putting real users at risk.

In this post, we’ll walk through how to design these rehearsals using canary releases, feature flags, and a structured production‑ready checklist to create safer, more predictable, and more learn‑from‑able releases.

Why Treat Deployments Like Performances?

Think of a deployment as a live performance:

• You have a fixed time when the event happens.
• Multiple teams (dev, QA, ops, product, support) must be in sync.
• When things go wrong, it’s usually because something wasn’t rehearsed or communicated.

The outcome of a deployment is determined long before the code reaches production:

• Architecture decisions
• Testing strategy
• Rollback plan
• Monitoring and alerting
• Communication and documentation

A Deployment Dry Run is a deliberate rehearsal of all of these elements. It doesn’t just test your code; it tests your process, assumptions, and readiness.

The Two Big Risk‑Reducers: Canary Releases & Feature Flags

Before digging into dry runs, let’s clarify two concepts that make them much safer and more powerful.

Canary Releases: Gradual Exposure at the Infrastructure Level

A canary release rolls out a new version of your service to a small percentage of production traffic first—say 1% of users—before gradually increasing to 100%.

This reduces risk by:

• Limiting the blast radius if something breaks
• Allowing real‑world performance and error rates to be compared between old and new versions
• Making it easy to pause rollout when anomalies are detected

Canary releases are typically configured at the infrastructure or deployment layer (e.g., load balancers, service mesh, or orchestrator), treating the new version as a separate instance of the service.

Feature Flags: Fine‑Grained Control at the Application Level

Feature flags (feature toggles) allow you to enable or disable specific behaviors in your code at runtime without redeploying.

They take risk reduction a step further by providing:

• Finer‑grained targeting – Turn on a feature only for internal users, beta testers, specific regions, or cohorts.
• Instant rollback – When a feature misbehaves, turn it off immediately without rolling back the whole deployment.
• Per‑feature control – Different rollout schedules and strategies per feature, even within the same deployment.

Used together:

• Canary releases control which version of the application receives traffic.
• Feature flags control what behavior users experience within that version.

This combination gives you a powerful way to rehearse launches in production with minimal user exposure and maximum control.

The Deployment Dry Run: What Are We Rehearsing?

A Deployment Dry Run is more than “deploying to staging and clicking around.” It is a structured exercise that treats an upcoming release like a real event.

You’re rehearsing:

• Technical readiness – Does the system behave as expected under realistic load, data, and workflows?
• Operational readiness – Are monitoring dashboards, alerts, and logs set up and meaningful for this change?
• Team readiness – Does everyone know their role, timelines, and escalation paths?
• Risk and rollback readiness – Do we know how to stop, roll back, or disable problematic features quickly?

To do that consistently, you need a Production‑Ready Checklist.

Building a Production‑Ready Checklist

A good checklist won’t guarantee success, but it will dramatically reduce preventable failures and surprises.

Below is an example structure you can adapt.

1. Code & Testing Readiness

• Unit, integration, and end‑to‑end tests passing for all impacted components
• Critical paths covered by automated tests (auth, payments, data integrity, etc.)
• Backwards compatibility confirmed (APIs, DB schemas, message formats)
• Performance and load testing results reviewed where relevant

2. Architecture & Data Readiness

• Database migrations are idempotent and backwards compatible
• Data backfill or transformations are planned, tested, and timed
• Capacity and scaling assumptions validated (e.g., with canary or staging load)
• Dependencies (internal services, third‑party APIs) are available and version‑compatible

3. Canary & Feature Flag Strategy

• Canary rollout plan defined (e.g., 1% → 10% → 50% → 100%) and documented
• Feature flags created for each risky or user‑facing change
• Targeting rules defined (internal users, beta users, specific markets, etc.)
• Rollback paths documented (canary rollback, feature flag off, version rollback)

4. Monitoring, Alerts & Success Metrics

• Dashboards updated to include key metrics for this release
• Alerts configured for error rates, latency, traffic anomalies, and business KPIs
• Success metrics clearly defined (e.g., conversion uplift, latency reduction, error drop)
• Logging and tracing cover new code paths and failure scenarios

5. Coordination & Communication

• Release owner identified and accountable
• Deployment window agreed across dev, ops, product, and support
• Runbook created: steps, checkpoints, and decision criteria
• Support team briefed on expected changes, known risks, and how to escalate
• Stakeholder communication templates prepared (before, during, and after release)

6. Post‑Release Plan

• Monitoring plan for first minutes, hours, and days post‑deployment
• Clear thresholds for “roll forward” vs. “rollback/feature off” decisions
• Reserved time scheduled for post‑release review and learning

This checklist becomes your baseline rehearsal script.

Running an Effective Deployment Dry Run

Here’s how a dry run might look in practice.

Step 1: Simulate the Release in a Non‑Prod Environment

Use a production‑like environment (staging, pre‑production) to:

• Execute every step in your runbook as if it were real
• Perform mock database migrations and config changes
• Validate infrastructure changes (load balancers, routing, CI/CD pipelines)
• Enable feature flags for internal or test cohorts

The goal is not “it works on staging,” but “we fully understand the release path and where it might fail.”

Step 2: Rehearse Cross‑Team Coordination

Even in a non‑prod dry run, simulate the human workflow:

• Notify your internal stakeholders using the same channels as real deployments
• Have support pretend to receive an issue and walk through the escalation flow
• Ensure logs, dashboards, and alerts are visible and interpretable by on‑call engineers

This uncovers recurring gaps in readiness, coordination, and communication that code alone won’t reveal.

Step 3: Practice Canary and Feature Flag Rollouts

Use your dry run to validate your rollout strategy:

• Confirm you can route a small percentage of traffic to the new version (even if synthetic)
• Confirm that enabling/disabling feature flags behaves exactly as expected
• Verify that logs and metrics properly separate new vs. old paths

By the time you reach production, canary behavior and flag toggling should feel boring and routine, not experimental.

Turning Production into a Controlled Rehearsal

Eventually, your real users do become part of the story—but you still don’t have to expose them to full risk.

A well‑run production deployment can itself be a controlled rehearsal if you:

Start Small with Canary Releases

• Begin with a tiny fraction of traffic (e.g., 1%)
• Closely monitor error rates, latency, and core business metrics
• Increase traffic only when metrics are stable for a defined period

If something looks wrong, you stop or roll back the canary before the issue becomes widespread.

Use Feature Flags for High‑Risk Features

• Ship the code behind flags disabled by default
• Turn it on first for your own team or internal accounts
• Expand to small user cohorts once you’re confident
• Keep the ability to instantly turn the feature off if metrics degrade

Feature flags effectively allow you to decouple deploy from release: you can safely deploy code, then gradually release functionality when you’re ready.

Measuring Release Health: Metrics, Not Vibes

A deployment dry run only adds real value if you measure its outcomes.

Define success metrics before you deploy, such as:

• Technical: error rate, latency, CPU/memory usage, retry rates
• User experience: page load times, time to first interaction, crash rates
• Business: conversions, sign‑ups, drop‑off in key funnels, usage of new features

Post‑release, compare these metrics:

• Old vs. new version (canary vs. baseline)
• Before vs. after enabling each feature flag

This data tells you:

• Whether the release is healthy and safe to continue
• Whether it actually delivered the intended value
• What you should adjust for next time

Learning from Every Deployment

Recurring problems—surprise incidents, missing alerts, confused stakeholders—are signals, not bad luck. Address them systematically:

1. Run a brief post‑release review for each significant deployment.
2. Identify what went well, what went wrong, and what was merely “unclear.”
3. Translate these insights into updates to your Production‑Ready Checklist, runbooks, and tooling.

Over time, your deployment process becomes:

• More predictable
• Less stressful
• Easier to scale across teams

Conclusion: Make Rehearsal Part of the Culture

Safe, reliable deployments are not the result of heroics during incidents. They are the outcome of deliberate practice: dry runs, structured checklists, small blast radiuses, and clear metrics.

By combining:

• Canary releases to limit risk at the infrastructure level
• Feature flags to finely control exposure and enable instant rollback
• A Production‑Ready Checklist to standardize preparation
• Cross‑team coordination and communication to avoid surprises
• Post‑release monitoring and learning to improve each time

…you transform deployments from nerve‑wracking events into routine, well‑rehearsed operations.

Don’t wait for the next incident to fix your release process.

Start treating every deployment as a performance—and every dry run as your chance to make opening night uneventful for your users and successful for your team.