Multi-Cloud Reliability: Monitor AWS, Azure & GCP Together

Running workloads across AWS, Azure, and GCP is increasingly common — analyst firm Flexera's 2025 State of the Cloud report found that 87% of enterprises use multiple public cloud providers. But multi-cloud adoption creates a monitoring fragmentation problem that's rarely discussed in architecture decisions: each provider has its own status page, its own incident communication format, and its own alert channels. When something goes wrong across two providers simultaneously, the operational picture is split across three separate tabs.

The naive solution — manually checking AWS Health Dashboard, Azure Service Health, and GCP Status Page during every incident — breaks down under pressure. During a complex incident involving multiple providers, the last thing your on-call engineer should be doing is tab-hopping between three status pages, each with different terminology and different severity levels. The cognitive overhead compounds exactly when clarity is most needed.

A unified multi-cloud monitoring layer solves this by normalizing status data from all three hyperscalers into a consistent schema: the same severity levels, the same incident timeline format, the same alert routing pipeline. When S3 in us-east-1 and Azure Blob Storage in East US are both degraded simultaneously, you see one unified incident view — not two separate tabs with different terminology.

Before configuring multi-cloud monitoring, map your actual dependency footprint across providers. Most teams significantly underestimate it. A common pattern: primary compute and storage on AWS, Microsoft 365 and Azure AD for identity, Google Workspace and GCP for analytics and ML workloads. Each cloud touches different critical paths — a GCP BigQuery outage might affect your analytics pipeline without touching customer-facing features, while an Azure AD degradation could lock out your entire engineering team.

Document your cloud dependency matrix: list each provider, the specific services you use, the regions you depend on, and the product features that depend on each. This matrix determines your monitoring configuration and your alert routing priorities. An Azure outage that only affects your internal BI tools warrants a Slack notification; an AWS outage affecting your primary compute warrants an immediate PagerDuty page.

Component-level granularity matters as much for Azure and GCP as it does for AWS. An Azure outage is almost never 'Azure is down' — it's 'Azure AD B2C in West Europe is experiencing authentication delays' or 'Azure Service Bus in East US has elevated message delivery latency.' PulsAPI monitors all three hyperscalers at the component and region level, so your alerts tell you exactly which piece of which cloud is affected, not just a generic provider-level degradation.

The risk of multi-cloud monitoring is alert volume. AWS, Azure, and GCP collectively generate hundreds of status events per month — most of which affect services or regions you don't use. Unfiltered multi-cloud monitoring quickly becomes indistinguishable from noise. The key is precision: subscribe only to the specific services and regions your architecture uses, not to every component each provider offers.

Apply the same tier model to multi-cloud components as you would to any vendor. Tier 1 (page on-call): your primary compute layer, your database services, your authentication provider across whichever cloud hosts them. Tier 2 (notify team): supporting services like logging, monitoring, and CDN. Tier 3 (dashboard visibility only): services used by internal tools, analytics, and non-critical features. The tier assignment should follow your dependency criticality, not the cloud provider identity.

One practical advantage of unified multi-cloud monitoring is cross-cloud correlation. If your primary API latency spikes at the same time AWS CloudFront shows degraded performance, the correlation is immediately visible. Without a unified view, your team might spend 20 minutes investigating application code before checking CloudFront's status. PulsAPI's alert routing can deliver a combined context message: 'API latency spike correlates with AWS CloudFront degraded performance in us-east-1, CloudFront status updated 4 minutes ago.'

One of the most valuable outputs of multi-cloud monitoring is comparative SLA data. AWS, Azure, and GCP each publish uptime SLAs for their services — but those commitments are not identical, and neither is their actual delivery. With 90-day historical uptime data across all three providers, you can make architecture decisions based on observed reliability rather than marketing claims.

Concrete questions this data answers: which provider has historically delivered better uptime for object storage in Europe? Which has the faster MTTR for compute incidents? Which regions have the most consistent reliability records? This isn't academic — if your disaster recovery strategy involves failing over from AWS us-east-1 to Azure East US, you should know whether Azure East US has a historically better reliability record for the specific services involved in that failover.

Multi-cloud SLA benchmarking also strengthens vendor negotiations. Enterprise customers with significant spend across multiple providers are in a position to negotiate SLA terms — but only if they arrive with data. A quarterly reliability report showing that provider A delivered 99.94% uptime against a 99.99% SLA commitment, while provider B consistently exceeded their SLA, is the foundation for a data-driven contract conversation. PulsAPI's SLA export feature generates this report in a format ready for vendor review meetings.