Release Train Model

Release train model schedules releases at fixed intervals, publishing whatever features and fixes are complete at each scheduled time. Like trains departing on schedule regardless of passenger count, release trains ship on predetermined dates regardless of content volume. This model prioritizes predictability over content optimization.

What Is Release Train Model

The release train model is a release scheduling approach where releases happen on fixed schedules. Features ready by the cutoff make the release; features not ready wait for the next train. This contrasts with feature-based releasing where specific features drive release timing.

The metaphor captures the key concept: trains leave the station on schedule whether full or empty, and passengers who miss a train catch the next one. Applied to software, releases happen on schedule whether they contain many features or few, and features that miss a cutoff ship in the next release.

How Release Train Model Works

Release trains operate on defined schedules with clear cutoff processes and predictable content determination. This structure enables reliable planning for both producers and consumers.

Schedule definition establishes train frequency. Common intervals include weekly, biweekly, monthly, or quarterly. The interval should match development capacity and consumer adoption speed. More frequent trains mean smaller releases; less frequent trains mean larger releases.

Cutoff management determines what makes each train. Code complete by the cutoff date is included; code not complete waits. This creates natural deadlines that focus work. Cutoffs must be enforced consistently to maintain model integrity.

Content communication tells consumers what is on each train. Release notes document included changes. Pre-release communication previews upcoming trains. This visibility helps consumers decide which trains matter to them.

Key Considerations

• Choose intervals matching team capacity and consumer needs
• Enforce cutoffs consistently to maintain predictability
• Accept that some trains may be lighter than others
• Communicate train contents clearly to consumers
• Allow flexibility for critical issues that miss cutoffs

Common Questions

How do release trains handle urgent fixes?

Urgent fixes present a tension in the release train model. Strictly following the model means waiting for the next train. However, critical issues may require faster response.

Most teams using release trains maintain emergency release procedures for truly critical issues. These releases happen outside the train schedule when severity warrants. Security vulnerabilities and production-breaking bugs typically qualify. Clear criteria prevent routine use of emergency releases.

Frequent trains reduce the need for emergency releases. Weekly trains mean at most a week’s wait for non-critical fixes. Monthly trains may require more frequent emergency exceptions. The right train frequency balances predictability with responsiveness.

How do release trains affect feature development?

Release trains create natural milestones that influence development planning. Features are scoped to fit within train intervals. Large features may be split across multiple trains. This encourages incremental delivery and limits work-in-progress.

The cutoff pressure can motivate completion but also risk rushing. Quality gates must be maintained regardless of deadline pressure. Features not ready should miss the train rather than ship incomplete.

Feature flags enable shipping incomplete features safely. Code can be merged before the feature is ready for consumers, making the train while remaining hidden. Subsequent trains can enable features as they are completed. This approach combines train benefits with feature development flexibility.

Summary

Release train model provides predictable scheduling by releasing at fixed intervals regardless of content. Consistent cutoff enforcement maintains model integrity. The approach prioritizes predictability over content optimization, helping consumers anticipate and plan for updates.