🚀 Top Deployment Strategies in CI/CD (With Examples)

 🌐 Introduction

In today’s world of continuous integration and continuous deployment (CI/CD), software changes are released frequently — sometimes multiple times a day.
But with frequent releases comes risk: what if something goes wrong after deployment?

That’s where deployment strategies come into play.
They define how new versions of applications are rolled out to users — safely, efficiently, and often without downtime.

This article explores the top deployment strategies used in CI/CD pipelines, their advantages, use cases, and tools that support them.

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🧩 What Are Deployment Strategies in CI/CD?

A deployment strategy defines the method of releasing a new version of your application to users or servers.

The main goals of any deployment strategy are:

• ⚡ Zero downtime

• 🧠 Easy rollback

• 🧩 Gradual rollout

• 🕵️ User experience continuity

Choosing the right deployment strategy depends on:

• Application type (web, mobile, microservice)

• Infrastructure (cloud/on-premise)

• User traffic volume

• Business risk tolerance

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🎯 Top 5 Deployment Strategies in CI/CD

Let’s explore the most commonly used deployment strategies with examples.

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1️⃣ Blue-Green Deployment

Concept:
Blue-Green Deployment maintains two identical environments —

• Blue: The current (live) version

• Green: The new version to be deployed

Once the new version (Green) is tested and verified, traffic is switched from Blue to Green.
If any issue occurs, traffic can easily switch back to Blue.

Example Workflow:

1. Current version (Blue) is live.

2. New version (Green) is deployed and tested.

3. Load balancer shifts traffic to Green.

4. Blue is kept idle for rollback.

Benefits:
✅ Zero downtime
✅ Instant rollback
✅ Simple environment switching

Tools Supporting Blue-Green:

• Azure App Service Deployment Slots

• AWS Elastic Beanstalk

• Kubernetes Services with LoadBalancer

Use Case:
E-commerce websites where downtime can cause revenue loss.

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2️⃣ Canary Deployment

Concept:
Canary deployment rolls out the new version to a small subset of users first, monitors its performance, and then gradually increases rollout to everyone.

Example Workflow:

1. Deploy new version to 5% of servers/users.

2. Observe performance and logs.

3. Gradually increase to 20%, 50%, then 100%.

4. Rollback instantly if issues are found.

Benefits:
✅ Reduces risk of full failure
✅ Allows real-world testing
✅ Easy rollback by stopping new rollout

Tools Supporting Canary:

• Kubernetes with Istio or Argo Rollouts

• AWS App Mesh / EC2 Auto Scaling

• LaunchDarkly for feature-based rollouts

Use Case:
Large-scale microservices or SaaS products where gradual user rollout is safer.

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3️⃣ Rolling Deployment

Concept:
In a rolling deployment, old application instances are replaced gradually with new ones — one or a few at a time.

Example Workflow:

1. Deploy new version to one server/pod.

2. Monitor its performance.

3. Continue updating remaining servers.

Benefits:
✅ No downtime
✅ Minimal resource usage
✅ Smooth transition

Drawbacks:
⚠️ Slightly complex rollback
⚠️ Inconsistent versions during rollout

Tools Supporting Rolling Deployment:

• Kubernetes Deployments

• Docker Swarm

• Azure Kubernetes Service (AKS)

Use Case:
Microservices or containerized applications where high availability is required.

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4️⃣ Recreate Deployment

Concept:
Stop the old version completely, then deploy the new version.
It’s the simplest but causes downtime during deployment.

Example Workflow:

1. Stop the old application.

2. Deploy and start the new one.

Benefits:
✅ Simple to execute
✅ Clean environment

Drawbacks:
❌ Causes downtime
❌ Not suitable for production-critical apps

Use Case:
Internal systems or applications where downtime is acceptable (e.g., back-office apps).

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5️⃣ Feature Toggles (Feature Flags or Dark Launches)

Concept:
Deploy new code to production but keep features turned off using feature flags.
Features can be enabled gradually for certain users or conditions.

Example Workflow:

1. Deploy new version with feature flag off.

2. Enable feature for 10% of users.

3. Gradually increase until 100%.

Benefits:
✅ Enables safe experimentation
✅ Rollback without redeploying
✅ Supports A/B testing

Tools Supporting Feature Toggles:

• LaunchDarkly

• Azure App Configuration

• Firebase Remote Config

Use Case:
Testing new UI/UX features with select users before full release.

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🧠 Comparison of Deployment Strategies

Strategy	Downtime	Rollback Ease	Complexity	Best For
Blue-Green	❌ None	✅ Very Easy	⚙️ Moderate	High-availability apps
Canary	❌ None	✅ Easy	⚙️ Moderate	Gradual rollouts
Rolling	❌ None	⚙️ Moderate	⚙️ Moderate	Microservices
Recreate	⚠️ Yes	✅ Easy	⚙️ Simple	Internal apps
Feature Toggles	❌ None	✅ Very Easy	⚙️ Complex	Continuous delivery

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⚙️ Example: Blue-Green Deployment in Azure

1. Create two deployment slots in Azure App Service – Blue (Production) and Green (Staging).

2. Deploy the new app version to Green.

3. Test and verify the Green environment.

4. Swap slots to make Green → Production.

5. Blue becomes your rollback slot.

Command Example:

az webapp deployment slot swap \
  --resource-group MyResourceGroup \
  --name MyWebApp \
  --slot staging \
  --target-slot production

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🚀 Choosing the Right Strategy

Application Type	Recommended Strategy
Web Apps with heavy traffic	Blue-Green / Canary
Microservices	Rolling / Canary
Internal Tools	Recreate
Feature Testing / A/B Testing	Feature Toggles
Cloud-Native Apps	Rolling / Blue-Green

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✅ Benefits of Using Deployment Strategies in CI/CD

• 🚀 Zero Downtime Deployments

• 🧩 Reduced Risk of Failures

• 🔁 Easy Rollback Options

• 🧠 Controlled Feature Releases

• 📊 Better Observability & Feedback

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🔍 Conclusion

Deployment strategies are the final and most critical part of CI/CD pipelines.
They ensure your application updates reach users smoothly, safely, and continuously — without interrupting service.

Whether you use Blue-Green, Canary, Rolling, or Feature Toggles, the goal remains the same:
✅ Deliver better software faster, with zero downtime and maximum reliability.

🚀 Understanding CI/CD Pipeline: From Local Repository to Deployment

 🌐 Introduction to CI/CD Pipeline

In modern software development, Continuous Integration (CI) and Continuous Deployment (CD) are the backbone of DevOps practices.
They help teams deliver high-quality software faster, reliably, and automatically.

• Continuous Integration (CI) focuses on automating the build and testing process whenever code is pushed to a repository.

• Continuous Deployment (CD) focuses on automatically releasing the tested code to different environments such as staging or production.

A well-defined CI/CD pipeline ensures that every change in code goes through an automated and repeatable process — reducing errors, saving time, and improving code quality.

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🧩 Major Stages in a CI/CD Pipeline

Here’s how a typical CI/CD process flows from the developer’s local repository to final production deployment:

1. Code Commit (Local Repository Stage)

• The developer writes and tests code locally.

• Once tested, the developer commits the code to a Version Control System (VCS) like Git.

• Example:

  git add .
  git commit -m "Added user login API"
  git push origin main
  

🧠 This step ensures that your code is versioned, traceable, and ready for integration.

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2. Source Control & Remote Repository

• The pushed code is stored in a remote repository such as GitHub, GitLab, or Bitbucket.

• This repository acts as a central hub for the team, where all code changes are merged and reviewed.

🔍 Example:

• GitHub repository: https://github.com/username/myproject

• Branch strategy: main, develop, feature/*, release/*

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3. Continuous Integration (CI) Process

Once code is pushed, the CI system (like Jenkins, Azure DevOps, or GitHub Actions) triggers an automated build and test pipeline.

Typical CI Steps:

1. Code Checkout: Fetch code from the repository.

2. Build Application: Compile the source code.

3. Run Unit Tests: Verify code functionality.

4. Static Code Analysis: Check code quality (using SonarQube, ESLint, etc.).

5. Package Artifacts: Build deployable units (e.g., .zip, .jar, .dll, or Docker image).

✅ Example (Azure DevOps YAML):

trigger:
  branches:
    include:
      - main

pool:
  vmImage: 'ubuntu-latest'

steps:
  - checkout: self
  - script: dotnet build MyApp.sln
    displayName: 'Build Application'
  - script: dotnet test MyApp.Tests/MyApp.Tests.csproj
    displayName: 'Run Unit Tests'

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4. Artifact Storage

After successful CI, the output (build artifacts) is stored in an artifact repository or container registry:

• Azure Artifacts

• JFrog Artifactory

• Docker Hub

🧩 Example:
A .zip build file or Docker image like myapp:v1.0.0 is stored for deployment.

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5. Continuous Deployment (CD) Process

Once the build artifacts are ready, the CD process handles automated deployment to testing, staging, or production environments.

CD Steps Include:

1. Deploy to Test Environment

2. Run Integration Tests / UI Tests

3. Approval Gates (Manual/Automatic)

4. Deploy to Production

✅ Example (Azure DevOps Release Pipeline):

• Stage 1: Deploy to Staging App Service

• Stage 2: Approval by QA

• Stage 3: Deploy to Production App Service

🧠 Tip: You can also use Infrastructure as Code (IaC) tools like Terraform or ARM Templates to automate infrastructure setup.

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6. Monitoring and Feedback

After deployment, the system is continuously monitored using:

• Azure Application Insights

• Prometheus + Grafana

• New Relic

If any issue is detected, alerts are triggered, and teams can roll back to a stable build.

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⚙️ Example CI/CD Workflow: .NET Core + Angular App on Azure

Let’s consider an example scenario:

Stage	Tool Used	Description
Code Development	Visual Studio / VS Code	Developer codes locally
Version Control	GitHub	Push code to main branch
CI Build	Azure Pipelines	Build .NET Core API and Angular app
Artifact Storage	Azure Artifacts	Store build outputs
CD Release	Azure App Services	Deploy app to staging → production
Monitoring	Application Insights	Monitor performance and logs

💡 Pipeline Summary

Local Machine → GitHub → Azure DevOps CI → Azure Artifact → Azure DevOps CD → Azure App Service (Production)

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✅ Benefits of Implementing CI/CD

• 🚀 Faster Delivery: Automates build, test, and deploy processes.

• 🧠 Improved Code Quality: Automated tests ensure stable builds.

• 🔄 Quick Rollbacks: Easily revert to previous versions.

• 💼 Better Collaboration: Developers can integrate code frequently.

• 🕵️ Early Bug Detection: CI helps identify issues early in the cycle.

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🔍 Conclusion

Implementing a CI/CD pipeline transforms traditional development into a modern DevOps workflow.
From committing code locally to automated deployment, each step ensures speed, reliability, and efficiency.

Whether you use Azure DevOps, GitHub Actions, GitLab CI, or Jenkins, the goal remains the same — deliver quality software faster with minimal human effort.