Cloud Outage Analysis: AWS vs Azure vs GCP for IT Pros

Navigating Cloud Disruptions: In-Depth Comparison of AWS, Azure, and GCP Outages

The night before the most recent AWS outage that occurred on Monday, the 20th of October 2025, I started doing research into cloud outage analysis, focusing mainly on the top three providers in an in-depth comparison of AWS vs Azure vs GCP. As an IT pro navigating cloud disruptions, I was motivated by a past client experience where developers mistakenly believed AWS was immune to failures, prompting me to uncover trends, causes, and strategies for better reliability.

A few things were said in our meetings, but the biggest one that stood out to me was their belief that AWS never has outages. While I was flummoxed at their response, knowing there had just been an outage, they were serious in their belief that there had never been any.

We discussed this in-depth, and I pointed out that at the time, there were several recent outages impacting US-EAST-1.

Knowing outages have occurred across the various platforms at times, but not knowing the full details of each outage, I decided recently I would investigate how many outages have occurred and which platforms fared best.

First, I want to share that not all cloud-based outages are equal. For instance, just because an outage occurs on a cloud-based platform does not always mean your business systems will be impacted by that outage. This is especially true if you have failover capabilities built into your solutions.

For instance, within AWS, there exist regions and availability zones. If an outage occurs to US-EAST-1, you may have failover capabilities to US-WEST-2.

Along with these possibilities, outages to your business may occur due to misconfigurations or improper setting up of the secondary site. This is just as with any on-prem solution. Then you might have outages beyond this that may make failover either not possible or more complicated, depending on how the secondary system may or may not be impacted.

I have also seen “cost-savings” come into play during the business decision-making process to either delay or forgo the implementation of the secondary site for now or altogether, based on the unlikelihood of an outage ever happening “to us” type of reasoning as well.

Attempts to save on costs now usually backfire spectacularly and end up costing more in the long run, particularly with downtime costs soaring in the last few years.

As CIOs, IT managers, and business continuity professionals, we must weigh these risks against the potential for operational resilience, ensuring that short-term savings do not compromise long-term stability.

Key Insights and Findings

Let’s dive into my findings, which highlight actionable lessons for fortifying your cloud strategies for your business to increase resilience across the enterprise.

Overview of Outages on Each Platform:

AWS outages frequently center on its US-East-1 region, known for high-impact events affecting global services. These disruptions often cascade to interconnected systems, challenging even the most prepared teams to respond swiftly.

Azure incidents often involve networking or storage, with global ripple effects on Microsoft 365 integrations. Such events underscore the importance of monitoring dependencies in hybrid environments.

GCP outages are fewer but can be severe, such as those caused by data center incidents like fires or cooling failures. These remind us that physical infrastructure vulnerabilities persist in the cloud era, prompting a reevaluation of geographic diversity in deployments.

An important point about all Platforms:

Unsurprisingly, each of the cloud providers utilizes its own platforms to host its own systems, services, and applications. As such, they are often severely impacted when their own systems have an outage. This self-reliance can amplify internal disruptions, making it crucial for IT leaders to diversify vendors or architect multi-cloud solutions where feasible to avoid single points of failure.

Comparative Analysis of Cloud Outages:

Over the last 10 years, AWS, Azure, and GCP have all experienced outages, ranging from brief service disruptions to multi-hour regional failures.

Overall, Azure shows the highest number of outages (21), followed by AWS (19) and GCP (13).

However, AWS and GCP tend to have longer-duration events in some years, while Azure’s are more frequent but often shorter. For example, a 2018–2019 study of vendor-reported downtime showed AWS at 338 hours, GCP at 361 hours, and Azure at 1,934 hours, indicating Azure’s higher cumulative impact during that period (networkworld.com). This disparity invites IT professionals to assess not just frequency but also the potential severity when selecting providers, factoring in service-level agreements (SLAs) and historical performance for mission-critical workloads.

How the Data was Collected:

The analysis below is based on aggregated data from sources including official status pages, Wikipedia timelines, StatusGator reports, and Data Center Knowledge articles.

Note that these represent major, publicly reported outages. Minor or unreported incidents may not be included, and counts are approximate.

If dates in the timeline in the tables below are missing, it was determined that none of the providers had issues or outages in that specific time period, and I am only listing the time periods in which at least one provider had a known outage. For business continuity planning, this underscores the value of independent monitoring tools to capture unreported events that could still affect your operations.

The Causes Vary:

These incidents often stem from causes like power failures, network issues, software bugs, or environmental factors, such as weather or fires. Understanding these root causes can guide proactive measures, like investing in redundant power systems or automated bug detection in your own setups.

Impacts Also Vary:

Impacts typically include service unavailability, increased latency, data access errors, and cascading effects on dependent applications. These lead to business disruptions, lost productivity, and in severe cases, economic losses estimated in millions. For example, the 2017 AWS S3 outage cost hundreds of millions in lost revenue for affected companies. As IT leaders, reflecting on these can drive discussions on quantifying downtime costs and justifying investments in advanced recovery orchestration tools.

Outages Broken Down By Year, Quarter and Month

Number of Outages by Year

Key Insights for Number of Outages by Year:

2017 was Azure’s worst year with 4 outages, often involving global services like Office 365. This highlights the interconnected risks in SaaS ecosystems. AWS peaked in 2021 with 5, mostly US-East-1 related, such as December multi-phase disruptions lasting hours to days, impacting apps like Slack and Vercel. These events emphasize the need for region-agnostic architectures. GCP had early concentration in 2015–2016, such as Compute Engine issues, but fewer recently, with 2022 heatwave and fire events causing multi-hour downtimes in London and Iowa. For CIOs, this trend suggests prioritizing providers with strong environmental safeguards in data center selections.

Number of Outages by Quarter

Key Insights for Number of Outages by Quarter:

Q4 2021 was AWS’s busiest with 4 outages, all in December, causing widespread impacts like API errors and delayed invocations. This pattern calls for seasonal stress testing in contingency plans. Azure had clusters in Q1 2017 (3, including multi-day global issues) and Q1 2023 (3, networking-focused). These reveal vulnerabilities in peak update periods. GCP’s Q1 2015 (2) involved early Compute Engine problems, with durations around 1 hour each. IT managers can use this to advocate for quarterly reviews of provider status dashboards.

Number of Outages by Month

Key Insights for Number of Outages by Month:

December 2021 saw 4 AWS outages, with durations up to 7+ hours, disrupting services like EC2 and Lambda globally. This concentration prompts monthly risk assessments during high-traffic seasons. Azure’s March 2017 (3) involved prolonged issues, such as 16+ hours for some, affecting regions like Japan East. Such insights can inform targeted training for rapid response teams. GCP’s outages are more spread out, with August 2015–2024 events often tied to physical data center problems, such as lightning or fires, leading to data loss risks in some cases. Business continuity professionals should consider these when auditing provider disaster recovery protocols.

Broken Down by Length of Outages

Number of Outages and Total Downtime by Year

Key Insights for Number of Outages and Total Downtime by Year:

Azure’s 2017 peak (4 outages, 45h total) involved prolonged global issues (e.g., 16h+ disruptions to Office 365), causing access failures for millions. AWS’s 2021 (3 outages, 16h) centered on us-east-1, with 7h+ events impacting EC2 and Lambda, affecting apps like Slack. GCP’s 2015 (3 outages, ~5h) focused on Compute Engine, with short network-related downtimes; 2022 (3 outages, 30h) included 24h heatwave failures in London, knocking sites offline. 2025 shows escalating impacts, e.g., AWS’s October 20 outage (5h estimated) disrupted thousands of sites.

Number of Outages and Total Downtime by Quarter

Key Insights for Number of Outages and Total Downtime by Quarter:

Q1 2017 was Azure’s worst (3 outages, 38h), with 16h+ global events disrupting Skype and Xbox. AWS’s Q4 2020 (2 outages, 26h) included a 22h Kinesis failure affecting Roku and Adobe. GCP’s Q3 2022 (3 outages, 30h) involved heat and fire incidents in London/Iowa, causing 24h+ degradations in Storage and BigQuery. Q1 2025 highlights Azure’s 48h networking outage in East US2, impacting Databricks and VMs.

Number of Outages and Total Downtime by Month

Key Insights for Number of Outages and Total Downtime by Month:

March 2017 saw Azure’s 3 outages (38h total), with cooling and global failures affecting regions like Japan East. AWS’s December 2021 (2 outages, 8h) caused API errors, disrupting global services. GCP’s July 2022 (1 outage, 24h) from a UK heatwave impacted Storage and GKE in europe-west2. January 2025 Azure event (48h) led to VM degradations in East US2. GCP publishes detailed postmortems fastest, while AWS is the sparsest.

Response by Provider

In terms of response, GCP publishes the most postmortems (100+ annually), offering deep transparency that aids in learning from failures. Azure is the fastest, with preliminary reports within 3 days and video retrospectives, facilitating quick internal debriefs. AWS is sparsest, rarely publishing, such as its first in 2023 after 2 years. This variance challenges IT leaders to demand more accountability from providers and integrate third-party analytics for comprehensive visibility.

Recommendations and Actionable Insights

Below, I am outlining recommendations based on our findings for CIOs, IT Managers, and Business Continuity Professionals to enhance their resilience against these findings.

Recommendations and Actionable Insights for CIOs

As strategic leaders, CIOs must balance innovation with risk management in cloud environments. Drawing from the 10-year outage trends—where Azure led with 21 incidents, followed by AWS (19) and GCP (13)—and recent events like the October 2025 AWS DynamoDB disruption that caused billions in global losses, here are five actionable insights to enhance organizational resilience:

1. Adopt a Multi-Cloud Strategy Proactively: Assess your current vendor dependencies and pilot integrations across at least two providers (e.g., pairing AWS’s US-East-1 vulnerabilities with GCP’s more stable regions). Start by mapping 20% of critical workloads to alternate clouds within the next quarter to reduce single-point failures, as seen in the cascading effects of AWS’s regional outages.
2. Quantify Downtime Costs in Vendor Negotiations: Use historical data, such as Azure’s cumulative 1,934 hours of downtime in 2018-2019, to benchmark SLAs. Demand customized credits and penalties in contracts that reflect your business’s hourly loss estimates—aim to negotiate for at least 200% reimbursement on proven impacts—and review these annually to align with soaring downtime expenses.
3. Invest in Decentralized Infrastructure Alternatives: Explore blockchain-based or DePIN (Decentralized Physical Infrastructure Networks) solutions for non-critical apps, inspired by how platforms like Chainlink remained operational during the AWS outage. Allocate budget for a proof-of-concept in the next six months to test resilience against centralized cloud failures, potentially cutting long-term costs by 15-20%.
4. Conduct Enterprise-Wide Risk Audits with Outage Simulations: Lead cross-departmental exercises simulating high-impact scenarios, like GCP’s 2022 fire-induced downtimes. Incorporate findings into board-level reports, targeting a 25% improvement in recovery objectives by integrating AI-driven predictive analytics to forecast provider-specific risks.
5. Foster Vendor Accountability Through Transparency Demands: Push providers for more frequent postmortems, noting GCP’s 100+ annual reports versus AWS’s sparsity. Establish quarterly review meetings with vendors to discuss response times and root causes and consider tying executive bonuses to achieving zero unplanned outages in key systems.

Recommendations and Actionable Insights for IT Managers

IT Managers are on the front lines of implementation, where misconfigurations and regional failures—like AWS’s frequent US-East-1 issues—can amplify disruptions. With these cloud-based outages often stemming from network bugs or environmental factors, these five insights focus on operational fortification:

1. Build Automated Failover Mechanisms: Configure active-active setups across regions (e.g., AWS US-East-1 to US-West-2) and test them monthly. Use tools like Terraform to automate deployments, ensuring seamless switches that could have mitigated the 7+ hour AWS disruptions in December 2021.
2. Implement Real-Time Monitoring and Alerting Systems: Integrate provider status pages (e.g., AWS Health Dashboard) with third-party tools like Datadog for proactive notifications. Set thresholds for latency spikes and aim to reduce mean time to detection by 50% through customized dashboards tracking historical patterns, such as Azure’s networking-focused clusters. See our Patent Pending Pristine DR Environment™ tool to manage misconfigurations. 
3. Optimize Configurations to Prevent Self-Inflicted Outages: Audit setups quarterly for common pitfalls, like improper secondary sites, which exacerbate cloud incidents. Roll out training programs on best practices, targeting a 30% reduction in configuration-related downtime by leveraging automation scripts to enforce standards.
4. Diversify Geographic and Vendor Redundancy: Based on GCP’s data center vulnerabilities (e.g., 2022 heatwaves), distribute workloads across at least three global zones. Initiate a migration plan for 15% of assets to hybrid models, incorporating on-prem fallbacks to handle environmental risks like fires or power failures.
5. Run Regular Chaos Engineering Drills: Simulate outages inspired by real events, such as Azure’s March 2017 multi-day issues, to stress-test teams. Document lessons in a shared repository and aim for bi-annual full-scale exercises, improving response times by integrating feedback loops for continuous refinement.

Recommendations and Actionable Insights for Business Continuity Professionals

Business Continuity Professionals focus on risk mitigation and recovery, where trends like Azure’s frequent but shorter outages versus AWS’s longer ones highlight the need for strategic planning. These five insights emphasize preparedness amid rising economic impacts, as evidenced by the 2017 AWS S3 outage’s hundreds of millions in losses:

1. Incorporate Historical Trends into Risk Assessments: Analyze the data showing AWS’s 2021 peak (5 outages) and Azure’s 2025 surge (5 incidents) to prioritize high-risk periods, like Q4. Update your business impact analysis (BIA) annually, weighting scenarios by cumulative downtime hours to allocate resources effectively.
2. Develop Layered Disaster Recovery Plans: Create multi-tiered strategies that include failover to secondary providers, addressing causes like software bugs or fires. Test plans quarterly, targeting recovery point objectives (RPOs) under 15 minutes for critical apps, and simulate cascading effects on dependencies like Microsoft 365.
3. Quantify and Mitigate Economic Exposure: Estimate outage costs using benchmarks (e.g., $72.8 million per hour for AWS in October 2025) and integrate cyber insurance reviews. Aim to cover 80% of potential losses through policies, conducting gap analyses to justify investments in resilience tools.
4. Promote Cross-Functional Training and Simulations: Based on provider response variances (e.g., Azure’s fast preliminaries), train teams on rapid postmortem adoption. Roll out organization-wide drills biannually, focusing on communication protocols to minimize productivity losses during events like global ripple effects.
5. Explore Hybrid and Decentralized Continuity Options: To counter self-hosted provider impacts, pilot hybrid models or decentralized networks that stayed up during recent outages (e.g., certain blockchain platforms). Set a goal to integrate these for 10-20% of operations within a year, enhancing overall continuity against unforeseen disruptions.