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Your software is ready for production when you have evidence that it meets its users’ needs and your team can deploy, monitor, secure, support, and recover it—not merely when a demo works or a build passes. There is no universal readiness score: the decision depends on the service, its risks, and the people responsible for operating it.
What production readiness means
A successful demo or passing test suite proves only what the demo or tests covered. A production service also depends on its architecture, integrations, deployment process, security controls, observability, recovery plan, and operational ownership. AWS frames the central question as whether you are ready to support a workload; its guidance recommends an Operational Readiness Review (ORR) to validate that you can operate it (AWS Well-Architected: OPS 7).
Readiness is specific to the service. Google’s SRE guidance says a Production Readiness Review (PRR) checklist is shaped by the service’s details, domain expertise, related systems, and production practices (Google SRE: Production Readiness Review). Treat the review below as a starting point to tailor—not a certification or a substitute for your team’s requirements.
Review the evidence before launch
For each question, record the evidence, the person responsible, and any unresolved risk. A checked box without evidence does not establish that the service is ready.
#1 Best Overall
1. User and business outcomes
- What user-facing behavior must work at launch, and how will you tell whether it is working?
- What reliability and performance expectations follow from how people use the service and the impact of an outage?
- What are the launch boundaries, dependencies, and known limitations?
Set realistic reliability targets around user experience rather than choosing a target without a service-specific reason. Google’s reliability guidance emphasizes defining reliability in relation to user goals (Google Cloud: Reliability overview).
2. Architecture, dependencies, and failure modes
- Can the team explain the service’s components, data flows, critical dependencies, and likely failure modes?
- Can each dependency handle expected production traffic?
- What happens if a dependency is slow, unavailable, or returns bad data? Should the service degrade gracefully or fail safely?
- Are capacity, redundancy, and scaling appropriate to the service’s targets and impact?
Map the system and how its parts interact before deciding what to test or monitor. Google recommends understanding system scope and potential failure modes; its PRR guidance also points to dependencies and the blast radius of updates as review concerns (Google Cloud: Reliability overview; Google SRE: Production Readiness Review).
3. Security, privacy, governance, and compliance
- Have the security and privacy requirements that apply to this workload been identified and reviewed?
- Are access controls and configuration suitable for production?
- Have applicable governance or compliance obligations been addressed? If an exception remains, is it documented with an owner and mitigation?
The applicable obligations depend on the service and its context; a general checklist cannot determine which laws or standards apply. AWS includes security and governance in operational readiness guidance (AWS Well-Architected: OPS 7).
4. Quality and deployment safety
- 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, where possible, reverse?
- Does deployment provide timely feedback and a clear rollback or mitigation path if a release harms the service?
- Are configuration and infrastructure changes repeatable and reviewable?
AWS recommends fast feedback on quality, rapid recovery from unsuccessful changes, and frequent small, reversible changes. Consistent, templated environments and operations as code can help make changes repeatable, but they do not by themselves make a deployment safe (AWS Well-Architected: Prepare).
5. Observability and alert response
- Can the team see health across the service’s relevant components?
- Do monitoring and alerts connect to user or business outcomes, rather than creating noise without a clear response?
- Can the person responding identify the affected service, assess impact, and find useful diagnostic information?
Metrics, logs, events, and traces are examples of telemetry that can help explain workload state. Choose signals that help the team detect and investigate problems, and connect them to the service’s goals where possible. AWS describes these telemetry types in its preparation guidance; Google highlights ongoing observation and monitoring for trends and potential problems (AWS Well-Architected: Prepare; Google Cloud: Reliability overview).
6. Operations, incidents, and recovery
- Are routine procedures documented in runbooks?
- Do playbooks help responders investigate less familiar problems?
- Can the team restore service and recover data, and has that recovery been exercised where relevant?
- Are incident ownership, escalation paths, 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 less well understood. Google’s reliability guidance includes response, recovery, recovery testing, and learning from postmortems (AWS Well-Architected: OPS 7; Google Cloud: Reliability overview).
7. Ownership and staffing
- Is a team named as responsible for the service in production?
- Are enough trained people available to support it and cover on-call duties?
- Do those people know the system, its operational tools and procedures, and how to escalate an issue?
AWS recommends checking that team size and skills match operational needs. The release artifact alone cannot be ready to operate; the team must be able to support it (AWS Well-Architected: OPS 7).
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For every finding, capture the evidence, likely impact, owner, and mitigation. Treat unmet hard requirements as launch blockers. For other risks, make an explicit decision with the people accountable for operating the service; do not let an unassigned issue become an implied acceptance.
AWS recommends adding discoveries without mitigations to the backlog for implementation before launch, and using readiness reviews through the workload lifecycle. A practical decision can be one of three outcomes:
- Ready to launch: the review evidence supports the service’s requirements, and no blocking findings remain.
- Ready with accepted mitigations: accountable owners have accepted the remaining risks, documented mitigations, and set follow-up dates.
- Not ready: specific work remains before launch, such as satisfying a hard requirement or providing a recovery capability.
This three-outcome decision is a practical way to record findings, not a universal readiness standard. AWS’s ORR guidance recommends reviewing before general availability and revisiting readiness periodically as the workload changes and incident lessons or new requirements emerge (AWS Well-Architected: OPS 7).
Revisit readiness as the service changes
Production readiness is not a one-time gate. Architecture, dependencies, traffic, team skills, and operating requirements can change after launch. Reassess when those changes alter the risks or the team’s ability to support the workload, and incorporate relevant incident lessons into the next review. The appropriate balance among reliability, security, performance, cost, operations, and other concerns depends on the service; Google’s framework presents these as areas to consider together (Google Cloud Architecture Framework).
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