Do multicloud strategies actually reduce risk? Or is there a better way?

Recently, a major cloud outage disrupted services globally for hours. Organizations watched helplessly as a DNS resolution failure in the US-EAST-1 region cascaded through DynamoDB, crippling EC2 launches, network load balancers, and identity and access management (IAM) authentication.

The reaction was predictable.

Boardrooms erupted with calls for multicloud strategies. Executives demanded protection against single-provider dependency.

The instinct makes sense. But the execution rarely does.

Multicloud architecture carries appeal precisely because it addresses a legitimate fear — that concentrating infrastructure within a single provider creates unacceptable risk. What gets lost in this reaction is a more important question: Does spreading workloads across multiple cloud providers really reduce risk? Or does it simply redistribute problems while creating new ones?

What the data reveals about cloud reliability

All major cloud providers maintain exceptional availability exceeding 99.7%. Since January 2023, cumulative downtime across providers stands at:

• Amazon Web Services (AWS): 22.5 hours of downtime (99.9085% uptime)
• Google Cloud Platform (GCP): 53.6 hours of downtime (99.7821% uptime)
• Microsoft Azure: 27.4 hours of downtime (99.8886% uptime)

Outages typically affect specific services in one region, not entire platforms globally. Organizations running multi-region architectures within their chosen provider maintain operations through automated failover. Therefore, the blast radius can be significant but contained.

Multicloud proponents argue that distributing workloads across providers would have eliminated the impact entirely. That argument assumes multicloud implementations work as designed under real-world conditions — an assumption that doesn’t always hold up.

The hidden costs nobody discusses

Most organizations pursuing a multicloud strategy dramatically underestimate what a successful implementation requires. They don’t often consider that:

• Your talent gets diluted across multiple platforms. Two cloud platforms require expertise in two fundamentally different ecosystems. Organizations face uncomfortable choices: hire separate teams for each platform (expensive and siloed), cross-train existing engineers on both (creating surface-level competency without depth), or accept that one platform will always receive secondary treatment.
• Everything becomes harder. Infrastructure as code? Now you’re managing Terraform state files and wrestling with provider quirks instead of using native tools that actually work well. Networking? AWS VPC and Azure VNet operate on different models requiring separate DNS strategies and routing configs. Monitoring? CloudWatch can’t see Azure and Azure Monitor can’t see AWS, so you’re buying Datadog and still missing the deep insights you used to have.
• Security becomes a game of catch-up. AWS uses Service Control Policies. Azure uses Policy Initiatives. You’re maintaining duplicate security frameworks, separate audit trails, and parallel RBAC models. Configuration drift becomes inevitable because these environments evolve on different schedules. GuardDuty can’t protect your Azure workloads. Azure Defender can’t see your AWS infrastructure. You end up paying for expensive cloud-native application protection platforms that still don’t match what native tools deliver.
• The bills multiply faster than the benefits. Enterprise discount programs reward consolidated spending. Split your workloads and watch your discount rates shrink. Then come the data egress fees. Keeping data synced between clouds means constantly moving it across network boundaries, and cloud providers charge substantial fees for data leaving their networks. These costs compound as your data grows. FinOps teams end up managing two billing systems, two sets of credits, two optimization strategies, and still can’t explain why costs doubled.

What actually provides resilience

For real resilience, focus on multi-region deployment within your chosen provider — this offers redundancy without multicloud’s operational burdens.

Every major platform offers what you need: global DNS, database replication across regions, object storage that syncs automatically, and container registries that mirror images globally. The tooling actually works together because it’s designed to.

Failover strategies depend on your risk tolerance and budget. Active/Active keeps full production running in multiple regions simultaneously — fastest recovery, highest cost. Active/Passive (pilot light) maintains minimal standby infrastructure that scales up when needed—longer recovery time, much lower cost. For most workloads, the trade-off makes sense.

Hybrid infrastructure also deserves another look. Organizations with data center investments can use them as resilience layers without multicloud complexity. Azure Arc, AWS Outposts, and GCP Anthos all let you extend cloud capabilities on-premises. Your teams work with familiar tools across both environments instead of learning entirely separate platforms.

When does multicloud make sense?

There are situations where multicloud is the right approach.

Regulatory requirements sometimes mandate specific provider choices for certain jurisdictions. Financial services organizations may need to use regionally specific cloud providers to meet data residency requirements. Some workloads depend on platform-specific capabilities unavailable elsewhere — specialized AI accelerators, proprietary database engines, or unique managed services that justify multicloud complexity for specific applications.

Furthermore, mergers and acquisitions (M&As) often create temporary multicloud environments when organizations combine. These scenarios typically represent transition states rather than permanent architectures. Most organizations eventually consolidate onto a primary platform to reduce operational complexity and costs.

Organizations pursuing multicloud for these reasons should proceed with realistic expectations about costs, complexity, and ongoing operational demands.

Designing resilience without the overhead

Building for uptime starts with architectural clarity. That means identifying where failure is most likely to occur, understanding how it will propagate through interconnected systems, and mapping out what recovery really requires.

And that’s where SHI shines.

Our architects bring deep expertise across AWS, Azure, GCP, and hybrid environments, helping organizations move beyond assumptions to build resilience that works. We help define recovery time objective (RTO) and recovery point objective (RPO) targets that align with actual business needs, then assess how current architectures perform against them — under load, under failure, and under pressure. From there, we design recovery strategies tailored to your environment, whether that means multi-region replication, hybrid fallback, or automated failover orchestration.

We also run tabletop exercises to uncover gaps before incidents occur, model the financial impact of different failover scenarios, and validate whether your existing architecture supports the outcomes your stakeholders expect. We don’t prescribe one platform or approach. Instead, we help you see the real trade-offs, avoid unnecessary complexity, and make resilience a built-in capability.

Turning crisis into action

Major cloud incidents tend to create organizational urgency around business continuity (BC). That momentum offers a valuable opportunity to engage in honest assessments of your existing resilience capabilities.

Ask yourself: Do current disaster recovery (DR) plans work as documented? When did we last test failover procedures under realistic conditions? Do teams understand their roles during regional outages? Can we fail over critical workloads within our RTO targets? Do we have visibility into dependencies that could prevent successful failover?

Organizations that channel this into systematic resilience improvement will emerge stronger than those pursuing multicloud as a reactive solution.

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