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Choose a cloud provider by matching its services and operating model to the failures your workload must survive—not by comparing uptime headlines alone. Define the workload’s availability, recovery-time, data-loss, latency, residency, and budget requirements first; then verify that the specific services and configuration you plan to use can meet them.
Define what “high availability” means for your workload
Start with the user-facing service, not the provider catalog. Identify the user journeys that must keep working and decide what counts as a failed request or an unusable service. A server responding to health checks does not necessarily mean customers can complete the task they came to do.
Turn those business needs into measurable objectives before you compare providers:
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- Recovery time objective (RTO): how long the workload can be unavailable after a disruption before recovery must be complete.
- Recovery point objective (RPO): how much recent data the business can afford to lose, expressed as a time interval or other measurable limit.
- Latency: the response times users need, including where those users are located.
- Constraints: data residency, compliance, service-tier, and budget requirements that limit viable designs.
These are workload requirements, not provider promises. Microsoft Azure’s reliability guidance recommends understanding a workload’s SLO when deciding whether to deploy across zones or regions. Its guidance also cautions that a service SLA’s definition of availability may not match the workload owner’s measure of health.
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Choose redundancy for the failure domain you need to survive
Redundancy only helps against failures that the design actually spans. A workload confined to one zone does not, by itself, protect against an outage of that zone. Resilience also depends on the complete request path: application instances, data services, networking, traffic routing, and any other critical dependencies.
| Design scope | Failure it is intended to address | Google Cloud guide’s example availability target | Design consideration |
|---|---|---|---|
| Single zone | Does not provide protection from an outage of the selected zone. | 99.9% for a single-zone workload, as an example infrastructure availability target in Google Cloud’s design guide, last reviewed 2026-09-23 UTC. | Use only when the workload’s requirements permit this failure exposure. |
| Multiple zones in one region | Zone-level outage, if the relevant application path and dependencies are distributed or zone-redundant. | 99.99% for a multi-zone workload, as an example infrastructure availability target in Google Cloud’s design guide, last reviewed 2026-09-23 UTC. | Confirm how each service implements zone redundancy; services do not all behave identically. |
| Multiple regions | Regional outage, when routing, application resources, and data recovery or replication are designed accordingly. | 99.999% for a multi-region workload, as an example infrastructure availability target in Google Cloud’s design guide, last reviewed 2026-09-23 UTC. | Plan traffic steering, data behavior, residency, and whether recovery is active-active or failover-based. |
The Google Cloud figures are targets stated in that guide for example deployment scopes. They are not a cross-provider comparison, a service-specific SLA, or a guarantee that an application will achieve the same availability. Actual workload availability depends on the services selected, their conditions, and the application’s own failure behavior.
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Google Cloud’s guidance describes multi-zone deployments for workloads that need to withstand a zone outage but can tolerate downtime during a regional outage. It presents multi-region deployment for business-critical workloads where high availability is essential. Treat those as design choices tied to your own recovery objectives, not as automatic rankings of one scope over another.
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Once the failure scope is clear, compare the specific compute, database, storage, networking, and other services each candidate design needs. A provider’s broad geographic footprint or headline SLA cannot establish that every required service supports the redundancy mode, replication behavior, or failover path your workload needs.
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For each dependency, verify the following in current provider documentation for the intended region and service tier:
- Whether the service is available in the regions you can use.
- Whether it supports the zone-redundant, multi-zone, or cross-region configuration the design requires.
- How replication and failover work, including whether failover is automatic or requires customer action.
- Relevant service limits and any prerequisites for the chosen configuration.
- The SLA definition, eligibility conditions, and whether the deployed configuration qualifies.
Do not add together service-level SLA figures and treat the result as an application guarantee. First establish how your workload defines health, then assess whether all critical dependencies and their failure modes support that objective. Azure’s reliability guidance notes that services implement availability zones differently and that SLAs have conditions.
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Account for the work your team still owns
A cloud provider operates infrastructure, but that does not automatically make a customer workload resilient. AWS describes resilience as a shared responsibility: AWS operates the cloud infrastructure, while customers configure workload resilience according to the services they select. With infrastructure services such as EC2, customers are responsible for deployment choices and workload architecture. Customers also make data-resilience decisions for managed services, including choices such as backup, versioning, and replication.
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Applications also need to behave well during disruptions. For example, code should handle connection retries and failover gracefully rather than turning a brief dependency interruption into cascading failures. Multi-region designs add operational and architectural choices, including how traffic is routed and how data behaves during recovery.
Use a consistent evaluation process
- Write down the workload objective. Record the user-facing health definition, availability SLO, acceptable RTO and RPO, latency needs, residency constraints, and budget.
- Map critical dependencies to failure domains. For each part of the request path, identify whether the proposed design spans resources, zones, or regions, and what happens if that scope fails.
- Check service fit in the intended locations. Confirm regional availability, redundancy and replication options, failover behavior, limits, service tier, and SLA eligibility for the exact configuration.
- Assign operational ownership. Document who configures failover, retries, backups, replication, monitoring, and recovery actions for each dependency.
- Assess geography and trade-offs. Check user latency and data residency alongside the extra services, traffic distribution, and operational work required by the design.
- Compare candidates against the same requirements. Reject designs that cannot meet a mandatory recovery, residency, or service requirement; quantify costs and complexity for the actual workload rather than assuming a universal premium or provider ranking.
How to make the final choice
The right provider is the one whose documented service behavior, geographic options, operating model, and workload-specific cost fit your requirements and your team’s capabilities. There is no universally best provider established by the availability targets or guidance described here. Provider regions, service features, SLAs, and configuration rules can change, so confirm the current documentation for every service you plan to deploy before making a decision.
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