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One concept that frequently appears on the DevOps Engineering Professional exam is the difference between immutable and mutable deployments — specifically how application artifacts and runtime environments are changed (or not) during updates. Understanding these patterns helps you design safer rollout strategies, reduce configuration drift, and simplify rollback procedures. A simple analogy:
  • Immutable deployment: instead of renovating a house while living in it, you build a brand-new house with all the updates. Once it’s ready you move in and dispose of the old house. There is no dust, no mid-renovation downtime, and no risk of leaving the old house in a partially modified state.
  • Mutable deployment: you stay in the same house and make changes while people are living there — repaint walls, change lighting, replace furniture — and every change is visible immediately. If something breaks during the update, users see it live.
Mutable deployments Mutable deployments change existing systems in place (for example, patching an OS, updating an app on a running VM, or editing configuration on a host). Typical methods include SSH, Systems Manager, or configuration-management tools such as Chef, Puppet, or Ansible.
A slide titled "Mutable Deployments" showing a developer using SSH/SSM/configuration management tools to update a running server from App(v1) to App(v2). The diagram uses icons for the developer, a tool stack, and server "chip" boxes representing the app versions.
Benefits and trade-offs of mutable deployments:
  • Pros: Smaller incremental updates, lower short-term resource use, and often faster for tiny fixes on VMs.
  • Cons: Prone to configuration drift, inconsistencies across hosts, riskier rollbacks (manual steps may be required), and accumulation of undocumented changes.
Common mutable tools and approaches:
  • SSH, AWS Systems Manager (SSM)
  • Configuration management: Chef, Puppet, Ansible
  • In-place OS/app patching and live edits
Mutable updates can be useful for quick hotfixes or emergency patches, but they require strong configuration management and drift detection to remain reliable at scale.
Immutable deployments Immutable deployments avoid modifying running instances. Instead, you create a new, fully provisioned instance (VM, container image, or a new serverless version), run tests, route traffic to it, and then retire the old instance. This eliminates in-place changes that lead to drift and makes environments reproducible and versionable. When using AMI-based immutable deployments, you bake an image that contains the OS and application versions, launch new instances from that AMI, and replace older instances. This ensures consistent, tested images are deployed.
A diagram titled "Immutable Deployments – AMI-Based Deployments" showing an AWS CodePipeline workflow with stages for building code, building an AMI, and rolling out using services like CodeCommit, CodeBuild, CloudFormation, EC2 Image Builder, Auto Scaling and resulting AMIs/instances. Arrows show the flow from source to AMI creation and a rolling update to instances.
Traffic-shifting strategies: blue/green and canary Blue/green and weighted (canary) deployments are common immutable strategies to shift traffic from the old environment to the new one. Host two identical environments (blue and green) and gradually shift traffic using weighted DNS or load balancer routing. If a problem appears, revert by sending traffic back to the previous environment.
A diagram showing a blue/green immutable deployment: Amazon Route 53 uses weighted routing (90% to existing, 10% to new) to send traffic to two Elastic Beanstalk environments, each with an Elastic Load Balancer and EC2 instances.
Key benefits of blue/green and canary approaches:
  • Instant rollback by switching traffic back to the previous environment.
  • Minimal user disruption during deployment.
  • Safer testing with real production traffic because you can quickly cut over or revert.
A presentation slide titled "Immutable Deployments – Blue/Green Deployment – Benefits." It shows three numbered cards listing benefits: instant rollback to previous version, minimal user disruption, and safer testing in real-world traffic, each with a colored circular icon.
Serverless and immutable patterns Serverless platforms also support immutable deployment patterns. AWS Lambda supports versions and aliases and integrates with traffic-shifting tools (such as API Gateway and CodeDeploy) to perform percentage-based canary releases and safe switches between versions.
A slide titled "Immutable Deployments — Lambda Versions" showing an Amazon API Gateway icon routing traffic to two AWS Lambda version icons, with 0% traffic to Lambda v1 and 100% traffic to Lambda v2.
Summary: trade-offs and decision criteria
  • Mutable deployments
    • Pros: Smaller incremental updates, lower immediate resource use, and quick for small live changes.
    • Cons: Configuration drift, difficult rollbacks, and increased operational complexity over time.
  • Immutable deployments
    • Pros: Reproducible, versioned environments; easy and reliable rollbacks; reduced risk from undocumented changes; simpler debugging of known-good images.
    • Cons: Often requires temporary parallel environments (higher resource usage during deployment) and additional tooling for image/version builds and orchestration.
Immutable deployments are safer, but they can increase short-term resource costs because you run parallel environments during cutover. Factor this into capacity planning and CI/CD pipeline design.
Quick comparison table Recommended AWS services and patterns
  • AMI-based: EC2 Image Builder, AMIs, Auto Scaling groups
  • Containers: Amazon ECS, Amazon EKS with image-tag/version strategies
  • Serverless: AWS Lambda versions and aliases + CodeDeploy traffic shifting
  • Traffic control: Route 53 weighted routing, Application Load Balancer
  • CI/CD orchestration: AWS CodePipeline, CodeDeploy, CloudFormation, Terraform
Further reading and references
A slide titled "Conclusion" containing a three-column table that compares Mutable and Immutable approaches. It lists factors like deployment speed, resource usage, configuration drift, rollback reliability, security, debugging, and Infrastructure as Code with pros and cons for each approach.
This article should give you a clear distinction between mutable (change-in-place) and immutable (replace-with-new-version) deployment strategies, their benefits, and where each approach is commonly applied in AWS. Choose the approach that balances speed, cost, operational complexity, and risk for your organization and automate the process to make rollouts and rollbacks reliable.

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