AWS Deployment Strategies to Minimize Risk and Maximize Uptime
By Nimika Grover | May 7, 2025
In today’s cloud-driven ecosystem, deploying applications securely and reliably is a critical priority for enterprises. As an AWS Partner, we’ve helped numerous clients minimize risk by leveraging industry best practices and AWS-native tools to create resilient deployment pipelines.
HSI recommends and use progressive delivery techniques for their customers – such as blue/green and canary deployments to safely introduce changes with minimal impact on users. This Blog details the procedures for implementing several risk-mitigation deployment strategies, along with the best practices that enable them.
- Blue/Green Deployment: The Blue-Green deployment strategy involves running two identical environments, referred to as Blue and Green. While the Blue environment is live and serving production traffic, the green environment remains idle. When it’s time for a new release, we deploy and test the changes in the green environment. Once the new version is verified, we switch the traffic to the green environment, making it the live production environment. In case of any issues, we can quickly revert to the previous version in the blue environment. This strategy ensures minimal downtime and allows for easy rollback if needed
Best Practices:
- Keep environments as identical as possible to avoid configuration drift.
- Use infrastructure automation (Elastic Beanstalk, CloudFormation, or Terraform) to replicate the production environment reliably for green.
- Always conduct smoke tests on green before switchover and have clear rollback procedures (which blue/green simplifies by design).
- Monitor user experience closely after cutover – integrate health checks and gradually ramp up traffic if possible.
- Blue/green can incur extra cost (running two environments in parallel), so employ auto-scaling and turn off idle environments promptly when they’re no longer needed.
- Canary Deployments: Canary deployment is a strategy where a small subset of users or traffic is directed to the new release while most users remain on the old version. This approach allows you to monitor the behaviour of the new version in a production environment without impacting all users. By gradually increasing the traffic to the new release and closely monitoring its performance, you can detect any issues and quickly roll back if necessary.
Best Practices:
- Canary deployments work best with robust monitoring and an automated judgment of success.
- Define clear success criteria (e.g., “no more than 1% error rate increase”) before ramping up traffic.
- Keep the canary period long enough to capture a realistic load and usage patterns (including peaktraffic times if possible).
- Communicate with stakeholders that a small segment of users will see new features – sometimes organizations even test the canary on internal users or beta testers first.
- Gradual rollouts can slow down how quickly features reach all users, so balance risk vs. speed based on the criticality of changes.
- Lastly, automate as much as possible: use Code Deploy or deployment pipelines to handle the traffic shifts and rollback for you so that no human delay occurs if an issue is detected.
- Parallel Environment Deployment: A parallel environment deployment refers to the practice of provisioning a separate environment in parallel to the current production environment, deploying changes there, and then switching over when ready. This is essentially an immutable deployment approach, as opposed to modifying the infrastructure in-place.
Blue/green deployments are a prime example of this strategy – you have two complete environments running side by side. However, the concept also covers variations like one-box deployments (deploy to one server in parallel to the cluster), shadow deployments (run the new version in parallel without serving live users, just to see if it behaves similarly), or any scenario where the new version runs in isolation until fully tested.
Best Practices:
- Always automate the environment creation. Manual steps to build a parallel stack can be error prone. Using templates (CloudFormation, Terraform) or orchestrated services (Elastic Beanstalk) ensures your blue and green are truly identical in setup.
- Also, incorporate configuration management for any differences (for example, environment-specific configuration should be minimal; if needed, use AWS Systems Manager Parameter Store or AWS AppConfig to manage configs and ensure both environments get the correct settings).
4.Traffic Shifting Approach is a controlled deployment strategy that enables teams to gradually redirect live traffic from an existing application version to a new one, reducing the risk of outages or degraded user experiences. Rather than pushing a release to all users at once, this approach introduces changes incrementally starting with a small percentage of traffic and progressively increasing it as the system demonstrates stability.
Best Practices:
- Regardless of method, observe the “shift, observe, and adjust” mantra. Shift a portion of traffic, observe system and user metrics, then adjust accordingly (increase, hold, or rollback).
- Automate the shifting if possible – tools like Code Deploy, Service Mesh controllers, or CI/CDpipelines can handle increments for you.
- Ensure you have health checks at the shifted environment; for instance, if using Route 53, enable health checks on the records so it will stop routing to the green environment automatically if it fails health checks.
- Also, communicate and document the traffic policies – it can become confusing in complex systems to know how much traffic is supposed to go where at a given time, so dashboards or logs that show current routing percentages are useful.