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A useful architecture review turns design choices into evidence-backed decisions and a short list of prioritized actions. Use these 41 questions to examine the workload in its business and operational context—not as a compliance exam or a universal scorecard. Ask them early enough to influence hard-to-reverse choices, revisit them before launch, and repeat the review when the architecture changes materially.

How to use this checklist

Keep the conversation constructive and blame-free. The AWS Well-Architected Framework v13 describes an architecture review as “a constructive conversation about architectural decisions, and is not an audit mechanism.” AWS also calls for consistent, blame-free reviews that encourage teams to investigate deeply. AWS Well-Architected Framework v13 and AWS review process guidance frame review as a way to surface critical issues and improvement opportunities, then prioritize actions in the workload’s business context.

Bring in the people who build and run the system, along with relevant security, infrastructure, architecture, and business stakeholders. Smaller teams do not need a formal review board, but should cover those responsibilities. AWS describes one cross-functional board model that reviews designs after design work and before build or purchase, with a possible check before deployment that the implementation matches the reviewed design. AWS architecture review board article

Most answers can emerge in discussion. If a claim is unclear or seems risky, assign someone to verify it against an artifact, measurement, test, or operating procedure. For example, “did we activate encryption or not?” may require checking the actual configuration rather than relying on the diagram. Record the question, the evidence needed, an owner, and a due date.

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Review continuously as the workload evolves, and schedule focused reviews at consequential points: early in design for choices that are difficult to reverse, before go-live, and after significant architecture changes. Make the depth proportionate to risk, business impact, and reversibility.

Context and constraints

  1. What user or business outcome does this system need to deliver? Identify the users, business process, and observable result the design supports.
  2. Which workloads and usage patterns must it support? Describe normal, peak, seasonal, batch, interactive, and other material patterns, with expected volumes where known.
  3. Which environments, regions, and service boundaries are in scope? Name the deployment environments, geographic requirements, system boundaries, and relevant internal or external dependencies.
  4. What data does the system handle, and how is it classified? Identify sensitive data, its source and destination, and applicable retention or residency constraints.
  5. Which regulatory, contractual, organizational, or technical constraints apply? Name the specific obligation or policy and the design control or evidence used to address it.
  6. Which assumptions are still unverified? List assumptions about users, dependencies, data, provider capabilities, and operating conditions, then identify how and by whom each will be checked.

Requirements and trade-offs

  1. Which quality attributes are critical for this workload? Rank the relevant needs—such as security, availability, recoverability, latency, throughput, cost, sustainability, and ease of change—rather than treating them as equally important.
  2. What measurable targets define acceptable performance and service? Point to requirements such as latency thresholds, throughput, availability objectives, or recovery objectives, and specify the workload and measurement conditions behind them.
  3. What trade-offs are acceptable among latency, throughput, availability, recovery, cost, sustainability, and delivery speed? State the decisions and the business owner or stakeholder who accepts them.
  4. What evidence supports the selected design? Cite measurements, prototypes, threat assessments, operational exercises, or documented requirements; mark untested expectations as assumptions.

Security, privacy, and compliance

  1. What threats and misuse cases have been considered? Identify the threat model, trust boundaries, likely abuse paths, and the owner of unresolved risks.
  2. Who can access the system and its data, and how are identities and privileges controlled? Show how authentication, authorization, privileged access, and service identities are configured and reviewed.
  3. How is data protected at rest, in transit, and, where relevant, in use? Verify the actual controls and configuration; do not infer protection from an architecture diagram.
  4. What data is collected, why is it needed, and how long is it retained? Connect collection and retention to purpose, privacy obligations, deletion procedures, and data flows.
  5. How are security events made auditable and investigated? Identify the relevant logs, their protection and retention, and who can review them during an incident.
  6. Which compliance or privacy obligations apply, and what demonstrates that controls meet them? Map the obligations to controls and evidence with accountable owners; a checklist alone does not establish compliance.
  7. Are security controls designed in and integrated early in delivery? Identify the design reviews and development or deployment controls that detect problems before release.

Reliability and recovery

  1. Which failure modes are expected, and what happens when each occurs? Cover component, infrastructure, dependency, data, and operator failures, including the user-visible effect.
  2. What recovery objectives apply, and what evidence shows restoration works? Cite recovery time and data-loss targets where defined, plus results from restore or recovery exercises.
  3. Which dependencies share a failure domain with this system? Identify common infrastructure, identity, network, region, or provider dependencies that could defeat apparent redundancy.
  4. How are health and degradation detected? Show the signals, thresholds, and alerts that distinguish a healthy service from one that is failing or serving reduced functionality.
  5. How are incidents handled, and who makes failover or recovery decisions? Point to the incident procedures, decision authority, and any manual actions required under pressure.

Performance and capacity

  1. What latency and throughput are expected under normal and peak load? State the workload, target, and measurement method rather than relying on a single average.
  2. How will bottlenecks and saturation be detected? Identify the metrics, traces, or tests that reveal limits in compute, storage, network, queues, or downstream services.
  3. What scaling assumptions have been tested? Explain whether capacity grows by adding instances, increasing resources, or another mechanism, and cite test results or measurements.
  4. How will data growth and workload variability affect capacity? Estimate material growth patterns and identify what would trigger a capacity review or design change.

Operations and change

  1. Who owns the service in production? Name the team accountable for service health, support, and architectural decisions.
  2. Can the team deploy and roll back safely? Describe the release process, rollback or mitigation path, and the conditions under which it has been exercised.
  3. Can operators monitor and troubleshoot the system? Identify dashboards, logs, traces, runbooks, and access needed to diagnose common and high-impact failures.
  4. How are changes made without creating unacceptable risk? Explain testing, approvals, rollout controls, and safeguards for changes to data, interfaces, and infrastructure.
  5. Is architecture documentation useful and maintained? Point to current diagrams, interfaces, key dependencies, and operational details, and identify who updates them.
  6. Where are important design decisions and their rationale recorded? Link the decision history to the trade-offs, assumptions, and conditions that could require revisiting them.

Cost, sustainability, and simplicity

  1. What are the main cost drivers, and how are they measured? Identify the major services or resource categories, the workload assumptions behind their costs, and who reviews actual spend.
  2. What sustainability considerations matter for this workload? Identify relevant resource-use or environmental goals and the design or operating choices used to address them.
  3. Do components or managed services provide enough value for their complexity? Compare the capability gained with operating effort, constraints, cost, and the skills the team needs.
  4. Can the design be simplified without violating a requirement? Identify redundant components, unnecessary layers, or needless variations, and test proposed simplification against stated needs.

Boundaries, dependencies, and evolution

  1. Are components coupled in ways that impede independent change or failure isolation? Examine interfaces, shared state, release dependencies, and the impact of a component failure.
  2. Which design choices are difficult to reverse? Name decisions with high migration or switching costs, the assumptions behind them, and the evidence needed before committing.
  3. What is the plan for migration, compatibility, and retirement? Describe how data and consumers move, how old and new versions coexist where needed, and how obsolete components are safely removed.

Decision and action

  1. Which risks are accepted, by whom, and until when? Record the risk, its impact, the accountable decision-maker, and a review or expiry date.
  2. What material evidence gaps remain, and what action will close or consciously accept each one? Assign each action an owner and due date, and state what evidence or decision will count as completion.

When comparing architecture alternatives

Use the same workload-specific criteria for every plausible option, and separate hard constraints from preferences. Provider frameworks can help identify concerns, but they are not neutral scoring standards for every architecture.

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Comparison axis Evidence to seek
Security and privacy fit Threat model, data handling, access controls, privacy needs, and applicable obligations.
Reliability, availability, and recovery Failure behavior, dependency domains, recovery objectives, and exercise results.
Performance and scaling Workload-specific measurements, peak behavior, bottlenecks, and growth assumptions.
Operational skill and effort Required expertise, on-call procedures, maintenance work, and support model.
Lifecycle cost Cost drivers under stated workload assumptions, along with ownership and review practice.
Sustainability Relevant resource-use goals and the evidence available to compare options.
Ease and risk of change Reversibility, compatibility, migration effort, and ability to change components independently.
Implementation complexity Components, dependencies, failure modes, and ongoing coordination required.

For each uncertain comparison, record whether the next step is a prototype, measurement, threat assessment, or operational exercise. Do not turn unknowns into precise-looking scores.

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Use provider frameworks as prompts, not verdicts

AWS Well-Architected Framework v13, published 2024-11-06, organizes cloud workload guidance around six pillars: operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. AWS Well-Architected Framework

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Google Cloud’s Well-Architected Framework, last reviewed 2026-01-28 UTC, also has six pillars—security, reliability, performance, cost, operations, and sustainability—and applies to cloud, migrated, hybrid, and multicloud workloads. It emphasizes designing for change, maintained architecture documentation, simplicity, and decoupling; it also notes that stateless design can increase reliability and scalability. These are considerations, not universal prescriptions: state, coupling, and managed-service choices should be judged against the workload’s actual requirements. Google Cloud Well-Architected Framework

Google Cloud’s security guidance describes security as a collaborative responsibility and recommends security by design, zero trust, early controls in development, and attention to regulatory, compliance, and privacy needs. Tailor those prompts to the jurisdiction, threat model, data sensitivity, and organizational policy; neither a provider framework nor this checklist proves that a system is secure, compliant, resilient, or production-ready. Google Cloud Security, Privacy and Compliance pillar

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