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What production readiness means
A passing test is evidence about the behavior that test covers. It does not, by itself, show that dependencies will cope with production traffic, that a release can be reversed, or that an on-call person can detect and handle a failure. Readiness is an operational decision as well as a software-quality decision.
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Google’s Production Readiness Review guidance treats its checklist as specific to the service, informed by domain expertise, related systems, and production practices. AWS likewise frames operational readiness as whether a team can support a workload. The useful question is not “Did we complete a generic checklist?” but “What evidence shows this particular service can be operated safely?”
Review readiness before launch—and revisit it
Run a readiness review before general availability, then revisit it as the workload, its dependencies, its team, and its requirements change. AWS recommends using operational readiness reviews through the workload lifecycle and incorporating lessons from incidents and new requirements. A review is a point-in-time decision, not a permanent badge.
#1 Best Overall
For each item below, name an owner and record the evidence. A checked box without evidence does not establish readiness. Tailor the questions to the service’s users, architecture, impact, and applicable obligations.
Questions to answer before going live
1. Are the user outcomes and launch boundaries clear?
- Which user-facing behaviors must work at launch, and how will the team verify them?
- What reliability and performance expectations follow from how people use the service and the impact of an interruption?
- What are the launch boundaries, critical dependencies, and known limitations?
Set reliability targets around user experience rather than choosing an arbitrary uptime number. Google’s reliability guidance recommends defining reliability in relation to user goals and setting realistic targets.
2. Does the team understand the architecture and its failure modes?
- Can the team explain the service’s components, data flows, critical dependencies, and likely failure modes?
- Can dependencies handle expected production traffic?
- What happens if a dependency becomes slow, unavailable, or returns bad data? Should the service degrade gracefully or fail safely?
- Are capacity, redundancy, and scaling choices proportionate to the service’s targets and impact?
Map how components interact and what could go wrong. Consider the blast radius of updates and dependency failures, not just whether each component works in isolation.
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3. Are security, privacy, and governance obligations addressed?
- Have the relevant security and privacy requirements been identified and reviewed?
- Are configuration and access controls appropriate for this workload?
- Are applicable governance and compliance obligations met, or are exceptions documented with owners and mitigations?
Requirements depend on the service, its jurisdiction, and its industry; there is no basis to assume one particular regulatory regime applies to every launch. AWS includes security and governance in its operational readiness guidance.
4. Can the team deploy changes safely and recover from a bad release?
- Has the service been validated against its stated behavior and operational requirements in environments that meaningfully represent production?
- Are changes small enough to diagnose and reverse where possible?
- Does a deployment provide fast quality feedback and a clear rollback or mitigation path?
- Are configuration and infrastructure changes reviewable and repeatable?
AWS recommends frequent, small, reversible changes and rapid recovery from unsuccessful changes. Consistent, templated environments and operations-as-code practices can help, but neither guarantees a safe deployment on its own. See AWS’s Prepare guidance.
5. Can the team see what the service is doing?
- Can the team assess health across relevant components using useful telemetry?
- Do monitoring and alerts relate to user or business outcomes, rather than generating noise without actionable context?
- Can an on-call responder identify the affected service, assess impact, and find diagnostic information?
Metrics, logs, events, and traces can help reveal workload state; the right signals depend on the service. Monitoring should help responders detect issues and investigate them, not simply produce data.
6. Are incident response and recovery workable?
- Are routine procedures documented in runbooks?
- Do responders have playbooks for investigating less-understood failures?
- Can the team restore service and recover data, and has recovery been exercised where relevant?
- Are incident ownership, escalation routes, and service-appropriate communications clear?
- Have relevant incidents and postmortem actions informed the review?
AWS distinguishes runbooks for understood procedures from playbooks for investigating issues that are not yet well understood. Google’s reliability guidance emphasizes response, recovery, recovery testing, and learning from postmortems.
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- Is a named team accountable for the service in production?
- Are enough trained people available for support and on-call duties?
- Do they know the system, operational tools, procedures, and escalation routes?
Readiness includes the team’s capacity and skills to operate the workload, not only the quality of the release artifact.
Best Value
Turn findings into a launch decision
For every finding, record the evidence, potential impact, owner, and mitigation. Treat hard requirements as launch blockers. For other risks, make an explicit decision with the people accountable for operating the service. AWS recommends tracking findings without mitigations for implementation before launch and revisiting readiness over the workload’s lifecycle.
A practical decision can be one of three outcomes:
- Ready to launch: The evidence supports the service’s requirements, and no unresolved blocker remains.
- Ready with accepted mitigations: The accountable operators accept named risks, mitigations, owners, and follow-up dates.
- Not ready: Specific work remains before the team can operate the service safely.
This is a practical way to record a risk-based decision, not a universal certification framework. Keep the evidence and unresolved risks visible so the decision can be revisited when the service or its operating conditions change.
Quick Recap
Sources and further reading
- AWS Well-Architected Framework: Ensure a consistent review of operational readiness
- AWS Well-Architected Framework: Ensure that workload teams are prepared
- AWS Well-Architected Framework: Prepare
- Google SRE Workbook: Production Readiness Review
- Google Cloud Architecture Framework: Reliability
- Google Cloud Architecture Framework
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