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The official guidance available from Microsoft, AWS, and Google Cloud provides decision dimensions rather than an independent head-to-head ranking. Use it to create a shortlist, then validate that shortlist with your own architecture, prices, and tests.
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What you are actually choosing
Choosing a cloud provider means choosing an operating environment for a particular workload. That environment includes regions, managed services, identity and networking controls, pricing and contracts, support arrangements, migration tooling, and the skills required to run everything after launch. A provider can be an excellent fit for one application and a poor fit for another because the constraints differ.
Provider-level claims such as “more secure,” “faster,” or “cheapest” are too broad to decide an unspecified workload. AWS’s Well-Architected material, Google Cloud’s Well-Architected Framework, and Microsoft’s assessment and migration guidance all point toward evaluating workload-specific requirements. The guidance is provider-authored, so it supplies useful checklists but is not a neutral controlled comparison.
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Define a decision that can be tested
Write the decision as a testable statement: for example, “Can this customer-facing API run in an approved region, meet a 150-millisecond p95 latency target for users in three countries, restore within two hours, and stay within our monthly operating-cost limit?” That statement is more useful than “Which cloud is best?” because each part can be verified against a design, a quote, or a test.
Separate hard constraints from preferences. A hard constraint eliminates a candidate when it fails. A preference changes the score among candidates that already qualify.
Inventory the workload before comparing providers
Do not begin with a pricing calculator or a list of similarly named products. First document what the application does and what it depends on. Microsoft’s workload-assessment guidance recommends baselining the components, dependencies, usage, compliance needs, reliability requirements, and team readiness that determine a viable target architecture.
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- Current hosting platform, operating system, runtime, framework, and application architecture.
- Compute type, vCPU and memory use, accelerator or GPU requirements, and whether workloads are steady, seasonal, or bursty.
- Storage type and capacity, growth rate, disk I/O, retention, backup volume, and restore requirements.
- Database engines and versions, extensions, replication mode, connection counts, transaction rates, and data-consistency requirements.
- Network throughput, ingress and egress patterns, peak concurrency, user geographies, and dependencies on external systems.
- Integrations such as identity providers, payment systems, message brokers, monitoring, hardware devices, and private connections.
- Measured normal and peak latency, throughput, error rate, queue depth, and resource utilization.
Capture a baseline over representative periods rather than relying on a single quiet-day sample. Peak behavior often determines scaling, database sizing, network capacity, and the number of failure domains required.
Write the non-negotiable requirements
- Data-residency and sovereignty rules, including where primary data, replicas, logs, backups, and support-access records may be stored.
- Industry or internal controls and the specific services and configurations that must be eligible for them.
- Supported operating systems, runtimes, database engines, drivers, and licensed software.
- Service-level objectives, availability targets, recovery point objective (RPO), recovery time objective (RTO), and maximum acceptable downtime.
- Encryption, key-management, network-isolation, audit, retention, and access-review requirements.
- Contract, procurement, support, and billing requirements, including existing commitments or negotiated discounts.
Verify each requirement against the exact service, edition, configuration, and region. A provider’s general compliance statement does not prove that an individual workload configuration satisfies your obligations.
Filter candidates by region and service fit
After inventorying the workload, remove provider-region-service combinations that cannot meet a hard requirement. Region selection affects data location, latency, price, service availability, disaster-recovery design, and sometimes sustainability. AWS explicitly recommends checking compliance, service availability, regional cost, and network latency when selecting regions in its Well-Architected Framework guidance. Google Cloud notes that residency requirements can constrain failover-region choices and therefore affect recovery objectives in its Well-Architected Framework. Microsoft’s migration guidance likewise treats compliance and region availability as architecture inputs.
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Check the actual feature, not the product name
A service-name mapping is only a starting point. For every required capability, compare:
- Supported versions, APIs, protocols, operating systems, and extensions.
- Quotas, maximum object sizes, connection limits, throughput ceilings, and scaling behavior.
- Availability across the regions you are allowed to use.
- Integration with identity, networking, logging, monitoring, backup, and deployment tools.
- Which tasks are managed by the provider and which remain your responsibility.
- Migration and export options if you later change architecture or provider.
Microsoft’s Azure counterpart material can help identify possible destinations during an Azure migration, but it is Microsoft-authored migration guidance, not an independent comparison of equivalent services across all three providers. Validate behavior in documentation and a proof of concept.
Evaluate primary and recovery locations together
Do not approve a primary region and postpone disaster recovery. The recovery region must also satisfy residency, service-availability, connectivity, and operational requirements. If a required database feature or managed service is unavailable in the permitted recovery geography, the intended RPO or RTO may be unattainable regardless of the provider’s overall regional footprint.
Compare costs on a like-for-like basis
There is no defensible answer to “Which cloud is cheapest?” without workload assumptions. Build the same model for each candidate, using identical traffic, uptime, storage growth, backup retention, redundancy, regions, and performance targets. Azure Migrate assessments can estimate resource costs and allow a pay-as-you-go or Enterprise Agreement offer to be selected; AWS’s regional guidance directs teams to calculate the cost of the workload in the chosen region. These tools support a current, workload-specific estimate, not a permanent ranking.
Include every material cost category
| Cost area | What to model | Why comparisons fail |
|---|---|---|
| Compute | Instance or container shape, autoscaling, operating hours, accelerators, and idle capacity | Different shapes can provide different CPU, memory, network, or licensing characteristics. |
| Managed platforms | Database, cache, queue, analytics, Kubernetes, serverless, and integration services | A cheaper compute tier may require more paid managed services to deliver the same architecture. |
| Storage | Capacity, I/O or request charges, snapshots, backups, replication, and retention | Redundancy and access patterns can dominate the nominal gigabyte price. |
| Network | Ingress, egress, inter-region replication, private connectivity, load balancing, and content delivery | Data transfer and cross-region traffic are often omitted from simple calculator examples. |
| Licenses | Operating systems, databases, commercial middleware, and bring-your-own-license terms | License mobility and included rights differ by software and contract. |
| Operations | Observability, security tooling, support, incident response, and on-call coverage | Lower infrastructure spend can be offset by additional tooling or staff effort. |
| Migration | Discovery, data transfer, refactoring, parallel running, testing, cutover, and rollback | One-time transition costs disappear from a steady-state-only comparison. |
Use current rates and your actual commercial terms
Record the currency, region, billing period, usage assumptions, discount or commitment terms, and date of every estimate. Apply negotiated pricing only where your organization is eligible to receive it. Recheck calculators and contracts immediately before purchase because prices, service availability, and discount programs change. A list-price table that uses different regions or resilience levels is not a like-for-like result.
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Price the architecture that meets the RPO and RTO, not a single-instance baseline. Include a second region or availability zone where required, replicated storage, standby capacity, backup copies, cross-region transfer, monitoring, and restoration tests. If one candidate reaches the target only with a more expensive design, that difference belongs in the decision rather than being hidden by comparing simpler configurations.
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Validate performance and reliability with your workload
Translate user-facing objectives into repeatable tests. AWS and Google Cloud treat performance, reliability, and operational excellence as architecture dimensions; neither framework supplies an independent benchmark of the same application on Azure, AWS, and Google Cloud.
Build a comparable test
- Choose the same application version, dataset shape, data-retention policy, and functional workload for every candidate.
- Deploy equivalent architectures in regions that satisfy your location constraints. Record instance sizes, autoscaling rules, database settings, cache behavior, and network topology.
- Generate traffic from the real user geographies or representative test locations. Measure p50, p95, and p99 latency, throughput, error rate, and cost at normal and peak concurrency.
- Exercise scale-out and scale-in, cold starts where relevant, queue backlogs, database failover, and dependency slowdowns.
- Run failure drills: remove an instance, zone, dependency, or network path according to the design. Measure detection, failover, data loss, service restoration, and operator actions.
- Restore backups into an isolated environment and time the process. Verify data integrity and application usability, not merely that a job reports success.
- Keep test scripts, configuration, timestamps, and workload shape so another team can reproduce the result after a price or service change.
Do not convert a benchmark from one region, instance family, or application version into a universal provider claim. Publish the conditions with every result.
Compare achieved recovery outcomes
Reliability is a combination of service design and your implementation. Compare the recovery plan available in each candidate region, the automation needed to operate it, the amount of standby capacity, and the evidence from restoration and failover tests. For regulated or geographically distributed systems, verify that both primary and recovery locations meet the same obligations.
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Account for migration effort and future operations
An existing workload has more than source code. Identity, DNS, certificates, network routes, data pipelines, integrations, licensing, monitoring, deployment processes, and undocumented dependencies can determine whether a migration succeeds.
Choose a transition strategy deliberately
Microsoft describes three useful strategy categories in its migration-strategy guidance:
- Rehost: move with little or no code change. This can shorten the initial move but may preserve inefficient architecture or licensing costs.
- Replatform: make limited changes to use managed platform services. This can reduce operational work while retaining much of the application design.
- Refactor: restructure code to improve qualities such as performance or maintainability. This can unlock a better target architecture but requires more time, testing, and change management.
The business driver, application complexity, deadline, risk tolerance, and desired amount of change should determine the strategy. These categories do not establish that any one provider is easier to migrate to in every situation.
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Estimate the work that happens before cutover
- Discover and validate application and infrastructure dependencies.
- Design identity, network segmentation, connectivity, secrets, keys, and access roles.
- Choose database migration, replication, conversion, or replacement methods.
- Move and validate data while protecting consistency and retention requirements.
- Rebuild CI/CD, infrastructure-as-code, monitoring, alerting, backup, and incident procedures.
- Test security controls, performance, failure recovery, and rollback.
- Plan a cutover window, communications, DNS or routing changes, and a measurable rollback threshold.
Microsoft’s migration overview and Azure Migrate assessment overview emphasize dependencies, readiness, and cost estimation. Use the same discipline when evaluating AWS or Google Cloud rather than assuming a tooling report replaces engineering analysis.
Price the skills needed after migration
List the skills required to design, secure, deploy, monitor, tune, and troubleshoot the target. Include on-call coverage, governance, identity administration, network operations, database administration, cost management, and incident response. AWS and Google Cloud place operational excellence among their framework concerns; Microsoft’s assessment guidance also calls for evaluating team readiness. A platform that looks inexpensive but requires scarce specialists may have a higher practical total cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a consistent comparison scorecard
Score only candidates that pass hard constraints. The following axes turn the decision into evidence collection rather than brand preference.
| Axis | Evidence to collect | Decision use |
|---|---|---|
| Compliance and data location | Required standards, eligible services, exact primary and backup regions, data and log locations, access controls | Eliminate combinations that cannot meet mandatory obligations. |
| Service fit | Required features, limits, database and runtime support, APIs, integrations, and operational responsibilities | Avoid choosing on product-name similarity alone. |
| Performance | Representative latency by user geography, throughput, concurrency, scaling behavior, and measured cost at load | Select only after testing the actual workload shape. |
| Reliability | Failure-domain design, backup and restore results, RPO/RTO evidence, automation, and operator workload | Compare achieved recovery outcomes, not a provider label. |
| Cost | Equivalent usage, region, transfer, resilience, licenses, support, discounts, migration, and staff effort | Compare total workload cost under current terms. |
| Migration effort | Dependencies, code changes, data movement, downtime, testing, cutover, and rollback complexity | Estimate risk and time to value for an existing system. |
| Team capability | Available skills, tools, governance, on-call processes, and training needs | Reflect the practical cost of day-two operation. |
| Sustainability | Region and workload-design measurements using your organization’s chosen method | Compare alternatives when sustainability is an explicit goal. |
The axes align with the six pillars in the AWS Well-Architected Migration Lens, the corresponding concerns in Google Cloud’s framework, and Microsoft’s workload-assessment guidance. Set score weights from your own goals. The cited material does not prescribe a neutral universal weighting formula.
Document assumptions beside every score
For each candidate, record the service and edition, region, configuration, measured result or source date, price assumptions, and unresolved risk. A score without its assumptions cannot be audited when requirements, prices, or services change.
A practical decision sequence
- List mandatory controls and outcomes. Write data locations, compliance obligations, supported software, RPO/RTO, latency, throughput, availability, and downtime limits.
- Inventory the workload. Baseline components, dependencies, databases, storage, network flows, peak usage, integrations, and current operating costs.
- Eliminate failed combinations. Check the exact provider, region, service, edition, and recovery location against every hard requirement.
- Design comparable architectures. Use equivalent resilience, security, observability, backup, and scaling assumptions rather than the cheapest possible prototype.
- Estimate current total cost. Include compute, managed services, storage, transfer, licenses, support, resilience, migration, and staff effort. Apply actual discounts and contracts.
- Run representative tests. Measure latency, throughput, scaling, failure recovery, backup restoration, and cost under normal and peak load.
- Assess migration and operations. Estimate code and data changes, cutover and rollback, dependencies, staffing, governance, and on-call capability.
- Make the decision traceable. Publish the scorecard, assumptions, test results, risks, and approval thresholds.
- Revisit on a defined trigger. Reassess when workload shape, regulations, regions, service availability, negotiated pricing, or organizational capability changes.
Common mistakes and how to correct them
Picking a provider before defining the workload
Symptom: The team starts by searching for a favorite provider’s product equivalent. Correction: Finish the inventory and hard-constraint list first; only then shortlist regions and services.
Comparing advertised unit prices
Symptom: A single virtual-machine or storage price decides the purchase. Correction: Model the full architecture, including transfer, backups, redundancy, observability, support, licenses, commitments, and labor.
Assuming a service exists everywhere
Symptom: A feature works in a demonstration region but is unavailable where data must remain. Correction: Verify regional availability, quotas, limits, and recovery-region support before approving the design.
Using provider compliance as a workload certificate
Symptom: A general compliance page is treated as proof that the selected configuration is compliant. Correction: Map each control to the exact service, region, data path, logging, key configuration, and organizational process.
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Symptom: An architecture claims an RTO or RPO that has never been measured. Correction: Perform controlled failover and backup-restore exercises, record elapsed time and data loss, and fix gaps before production.
Ignoring the team’s operating capacity
Symptom: A technically elegant design depends on skills or on-call coverage that do not exist. Correction: Include staffing, training, automation, governance, and support costs in the scorecard.
How to decide when two candidates both qualify
When multiple candidates pass all hard constraints, choose the one with the strongest evidence for your highest-weight outcomes. For a latency-sensitive service, measured user-geography latency and scaling behavior may outweigh a small list-price difference. For a heavily regulated system, eligible regions, auditability, and recovery-location compliance may dominate. For a migration under a fixed deadline, dependency risk, data-movement tooling, and the team’s existing skills may matter more than long-term architectural elegance.
Make trade-offs explicit. If one candidate costs less but requires more refactoring, show the one-time engineering cost and the expected operating difference. If another offers a simpler managed design but has a higher recurring rate, show the reduction in patching, backup, and on-call work. The decision is sound when stakeholders can see which assumption produced the result and can change that assumption without rebuilding the analysis.
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A cloud decision is not permanent. Re-run the material parts of the assessment when user geography changes, traffic becomes burstier, a database version reaches end of support, a required service expands or leaves a region, regulations change, contracts renew, or the team’s skills and operating model change. Keep the baseline measurements and test harness so a later comparison is against the same workload definition rather than a new set of assumptions.
The practical answer remains workload-specific: define constraints, filter by region and service fit, compare complete costs, test performance and recovery, and account for migration and operating capability. Only then can Azure, AWS, or Google Cloud be selected with evidence rather than a universal claim.
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