Rolling deployment vs progressive delivery: Choosing a deployment strategy

Rolling deployment

Rolling deployment is a gradual deployment strategy where new application versions are released incrementally across multiple instances or servers. Instead of updating all instances simultaneously, the deployment process replaces old versions with new ones in batches, ensuring that some instances remain available to serve traffic throughout the update process. This approach minimizes downtime and reduces the risk of widespread service disruption.

The rolling deployment strategy maintains service availability by keeping a portion of the infrastructure running the previous version while gradually updating remaining instances. If issues arise during deployment, the process can be paused or rolled back before affecting all instances. This method is particularly effective for applications that can tolerate running multiple versions simultaneously for short periods and provides a balanced approach between deployment speed and risk mitigation.

Progressive delivery

Progressive delivery extends traditional deployment practices by combining deployment techniques with feature management and monitoring to control how new features reach users. This strategy emphasizes gradual feature rollouts, real-time monitoring, and the ability to make dynamic decisions about feature exposure based on performance metrics and user feedback. Progressive delivery treats deployment and feature release as separate, controllable processes.

Unlike traditional deployments that make features immediately available to all users, progressive delivery allows teams to incrementally expose functionality to specific user segments while continuously monitoring system health and user experience. This approach enables rapid experimentation, reduces blast radius of potential issues, and provides fine-grained control over feature adoption. Teams can make data-driven decisions about feature rollouts and quickly respond to any problems that emerge.

Comparison: Rolling deployment vs progressive delivery

Scope

• Rolling deployment: Focuses primarily on the technical deployment of code to infrastructure instances

• Progressive delivery: Encompasses both deployment and feature release management with user-centric controls

Granularity

• Rolling deployment: Controls deployment at the server or instance level in predetermined batches

• Progressive delivery: Controls feature exposure at the user, segment, or percentage level with dynamic adjustments

Risk management

• Rolling deployment: Reduces risk by limiting the number of instances affected simultaneously during deployment

• Progressive delivery: Minimizes risk by controlling feature exposure and enabling rapid response to user experience issues

Rollback strategy

• Rolling deployment: Requires redeploying previous code versions to affected instances

• Progressive delivery: Allows instant feature disabling through configuration changes without code redeployment

Monitoring focus

• Rolling deployment: Monitors system health and deployment success across infrastructure components

• Progressive delivery: Tracks feature performance, user behavior, and business metrics alongside system health

Feature flags in rolling deployment

Feature flags complement rolling deployments by providing an additional safety layer during the gradual infrastructure updates. While rolling deployment manages which servers receive the new code, feature flags control whether new functionality within that code is active for users. This separation allows teams to deploy code across all instances using the rolling strategy while keeping new features disabled until the deployment is complete and verified. If issues arise during the rolling deployment, feature flags can instantly disable problematic functionality without requiring a code rollback.

The combination of rolling deployment and feature flags enables more confident deployments by decoupling code deployment from feature activation. Teams can roll out new code gradually across their infrastructure while keeping features safely behind flags, then systematically enable features once all instances are successfully updated. This approach provides multiple layers of control and reduces the complexity of rollback procedures since features can be toggled off immediately while keeping the new codebase in place.

Feature flags in progressive delivery

Feature flags are fundamental to progressive delivery, serving as the primary mechanism for controlling feature exposure and enabling dynamic release management. In progressive delivery, feature flags don’t just act as on/off switches but provide sophisticated targeting capabilities, allowing teams to expose features to specific user segments, geographic regions, or percentage-based cohorts. These flags integrate with monitoring and analytics systems to automatically adjust feature rollouts based on performance metrics, user behavior, and business outcomes.

The progressive delivery approach leverages feature flags to implement advanced release patterns like canary releases, A/B testing, and ring-based deployments at the feature level. Teams can gradually increase feature exposure while continuously monitoring key performance indicators, automatically rolling back features if metrics indicate problems, or accelerating rollouts when features perform well. This tight integration between feature flags and progressive delivery creates a feedback-driven release process that optimizes both technical performance and business value delivery while maintaining the ability to respond instantly to any issues that arise.

Rolling deployments offer the advantage of being straightforward to implement and understand, making them ideal for teams seeking a simple deployment strategy with minimal infrastructure complexity. They provide automatic rollback capabilities and ensure zero-downtime deployments by gradually replacing old instances with new ones. However, rolling deployments have significant limitations in terms of risk management and control. Once initiated, they follow a predetermined timeline that cannot be easily adjusted based on real-time performance metrics or user feedback. If issues arise during deployment, they may affect a substantial portion of users before detection, and the rollback process, while possible, can be time-consuming and may still impact user experience during the reversal period.

Progressive delivery provides superior control and risk mitigation through feature flags, canary releases, and sophisticated traffic routing mechanisms that allow for real-time adjustments based on metrics and user behavior. This approach enables teams to target specific user segments, gradually increase exposure, and make data-driven decisions about deployment progression. The ability to instantly halt or modify deployments based on performance indicators significantly reduces the blast radius of potential issues. However, progressive delivery requires more complex infrastructure, tooling, and operational expertise, making it more expensive and challenging to implement initially. Choose rolling deployments when you need a simple, cost-effective solution for straightforward applications with predictable release patterns and lower risk tolerance requirements. Opt for progressive delivery when deploying critical applications, serving large user bases, or when you need granular control over release impact and the ability to make real-time adjustments based on user feedback and system metrics.

Frequently asked questions

What is rolling deployment according to software development practices?

Rolling deployment is a gradual deployment strategy where new application versions are released incrementally across multiple instances or servers. Instead of updating all instances simultaneously, the deployment process replaces old versions with new ones in batches, ensuring that some instances remain available to serve traffic throughout the update process. This approach minimizes downtime and reduces the risk of widespread service disruption by maintaining service availability while keeping a portion of the infrastructure running the previous version during updates.

What are the pros and cons of rolling deployment?

Rolling deployments offer several advantages including being straightforward to implement and understand, making them ideal for teams seeking a simple deployment strategy with minimal infrastructure complexity. They provide automatic rollback capabilities and ensure zero-downtime deployments by gradually replacing old instances with new ones. However, they have significant limitations in terms of risk management and control. Once initiated, they follow a predetermined timeline that cannot be easily adjusted based on real-time performance metrics or user feedback. If issues arise during deployment, they may affect a substantial portion of users before detection, and the rollback process can be time-consuming and may still impact user experience during the reversal period.

What types of deployment strategies exist?

Several deployment strategies are available for software releases, including rolling deployment and progressive delivery. Rolling deployment focuses primarily on the technical deployment of code to infrastructure instances, controlling deployment at the server level in predetermined batches. Progressive delivery encompasses both deployment and feature release management with user-centric controls, allowing for feature exposure at the user, segment, or percentage level with dynamic adjustments. Other strategies include canary releases, A/B testing, and ring-based deployments. Rolling deployments are best suited for straightforward applications with predictable release patterns, while progressive delivery is ideal for critical applications serving large user bases that require granular control over release impact and real-time adjustments based on user feedback and system metrics.