Software incidents—unplanned disruptions or outages of services—are an inevitable challenge in SaaS and cloud infrastructure. Such incidents can range from brief slowdowns to major outages that affect millions of users, and they carry significant business impact. A recent global survey found that organizations suffer a median of 77 hours of downtime per year, with 62% of respondents estimating that an outage costs over $1 million per hour.
Understanding how these incidents occur is critical. Studies show the leading causes span both technical failures and human factors: network issues account for 35% of outages, third-party service failures for 29%, and human error for 28%. This means that while a code bug or hardware fault might trigger an incident, gaps in processes or communication can just as easily contribute.
This article explains the typical ways incidents arise in modern cloud and SaaS systems, covering technical causes such as software bugs, misconfigurations, and infrastructure failures, as well as organizational causes like inadequate testing, poor incident response, and monitoring blind spots. Throughout, real-world examples drawn from public postmortems and industry reports illustrate how these failures manifest in practice. The goal is to inform developers, SREs, and executives about realistic failure modes, helping teams prepare for incident response drills and improve their resilience.
Technical Causes of Incidents
Even in highly engineered cloud environments, things go wrong at a technical level due to flaws in software or the underlying infrastructure. These technical issues often precipitate the incident as the immediate trigger.
Software Bugs and Flawed Updates
Software bugs and flawed updates are among the most frequent sources of incidents. A subtle error in logic can lie dormant until a certain condition is met, or a newly deployed version can introduce an unintended failure. In complex distributed systems, even well-tested software might behave unpredictably under rare conditions or heavy load. A memory leak or uncaught exception can gradually degrade a service, or a timing bug might cause a critical process to deadlock. Major cloud providers are not immune—even Microsoft has attributed outages in Azure and Microsoft 365 to code changes that didn’t behave as expected. Rigorous testing and phased rollouts are used to catch these issues, but bugs still slip through in production.
In November 2025, a hidden bug in Cloudflare’s Bot Management code was triggered by an internal database permissions change, causing their core proxy servers to crash globally. The flawed logic allowed a feature file to grow beyond its expected size—from approximately 60 features to more than 200—leading to an unhandled exception that took down many Cloudflare services until engineers rolled back the change. This incident, Cloudflare’s worst outage since 2019, shows how a latent software bug can surface during a routine update and cascade into a widespread failure.
Perhaps even more dramatic was the CrowdStrike Falcon update incident in July 2024. A defective vendor update crashed approximately 8.5 million Windows systems worldwide. The faulty security software update caused an out-of-bounds memory read, sending computers into blue-screen crashes and disrupting critical services. Delta Air Lines had to cancel over 7,000 flights over five days as a result. This case underscores that third-party software bugs can be just as devastating—a single flawed patch propagated to many clients caused widespread business and societal disruptions.
Configuration Errors and Change Mistakes
Misconfiguration is another classic cause of cloud incidents. Modern systems have countless settings, from feature flags and environment variables to network routes and access permissions. An incorrect setting or a change deployed in the wrong way can instantly create an outage. Human error is often behind these config mistakes, but even automated config changes can have unexpected side effects. Because cloud services are so interconnected, a configuration error in one component can cascade into failures in others.
In May 2025, collaboration platform Slack suffered a nearly two-hour global outage due to a misconfigured database routing layer. Slack’s infrastructure had grown, but some static configuration did not update accordingly, preventing the web application from connecting to the database gateway. Users couldn’t send messages or load channels. Slack’s post-incident analysis noted the outage highlighted “silent configuration limits” and visibility gaps that needed to be addressed.
The October 2021 Facebook outage remains a textbook example of how an internal config mistake can cascade into a massive incident. A routine configuration change to Facebook’s backbone network accidentally disconnected their data centers from the internet, causing DNS lookups to fail and making Facebook, Instagram, and WhatsApp unreachable for about six hours. Facebook’s own networking misstep made it vanish from the internet entirely.
Even a simple typo can be catastrophic. In February 2017, Amazon’s S3 storage service in one region went down when an engineer mistyped a command during maintenance, inadvertently removing far more servers than intended. The loss of those servers knocked out critical S3 subsystems, causing a cascading failure that made dozens of high-profile websites and services unreachable. Amazon later acknowledged that a tool allowed too much capacity to be taken offline with one command, and they added safeguards afterward.
Infrastructure and Hardware Failures
SaaS and cloud services run on extensive physical infrastructure—data centers full of servers, networking gear, storage systems, power supplies, and cooling systems. Hardware can and does fail: disks crash, network cables get cut, and power equipment malfunctions. Cloud providers design for redundancy, but failures can still exceed expectations or escape fail-safes. Power outages remain the leading cause of major data center incidents, accounting for over half of severe outages in industry surveys.
In March 2025, a power subsystem failure triggered a prolonged outage in a Google Cloud region. A faulty UPS unit experienced a critical battery failure following a loss of utility power, resulting in a six-hour outage for Google Cloud services in the us-east5-c zone. Even though data centers have backup power, the failure of the UPS led to servers going down. This incident underscores that infrastructure failures, while rarer than software bugs, can be highly disruptive when they occur.
Other examples abound: extreme weather has knocked out data centers, such as storms flooding server rooms or heatwaves overwhelming cooling systems. Network provider outages can also ripple to cloud services—for instance, if a major internet backbone goes down, cloud connectivity might be impacted. While these events are less within a single SaaS provider’s control, they illustrate the fragility of underlying infrastructure.
External Dependencies and Third-Party Services
Modern software hardly operates in isolation—SaaS platforms rely on countless external services and third-party components including cloud infrastructure providers, content delivery networks, DNS services, payment gateways, and authentication providers. If a critical dependency fails, your service can fail too, even if your own code is fine. Third-party failures are consistently noted as a top cause of downtime, accounting for nearly one-third of incidents per surveys.
In June 2021, CDN provider Fastly went down, taking a large swath of the internet with it for about an hour. The root cause was a latent software bug triggered by a single customer’s configuration change. When that one client updated a setting, it activated the bug, causing 85% of Fastly’s network to return errors. Websites relying on Fastly—including news sites, e-commerce platforms, and government sites—all went dark. Fastly’s quick incident response, detecting within one minute and rolling out a fix within 49 minutes, limited the duration, but the incident highlighted the risk of concentrated infrastructure dependencies.
The CrowdStrike incident also illustrates third-party impact. Delta Airlines had to cancel over 7,000 flights because the CrowdStrike software used on critical systems rendered Windows machines unusable. Delta later sought damages from CrowdStrike, underlining that customers directly bear the fallout of a vendor’s mistake. This example shows that SaaS and enterprises entrust key functions to external providers; when those providers ship a bad update or go down, it directly becomes an incident for all their clients.
Consider also platform-level outages: if your SaaS runs entirely on a cloud provider and that provider has an outage, your service is effectively out. For instance, a several-hour outage in an AWS region in December 2021 disrupted many SaaS products simultaneously, simply because they all depended on that region’s infrastructure.
Security Attacks and Overload Events
Not every incident is internal—sometimes external malicious events create an incident by overwhelming or exploiting systems. DDoS attacks, where attackers flood a service with traffic, can degrade performance or knock services offline. Cloud and SaaS companies face frequent DDoS attempts and usually deflect them with mitigation systems. However, if an attack is large enough or if mitigation tools malfunction, it can cause a service outage.
In July 2024, Microsoft Azure experienced an incident where a DDoS attack triggered automated mitigation, but an error in the implementation of their defenses amplified the impact rather than mitigating it. A localized power outage at one European site then prevented network routes from updating properly, contributing to a multi-hour outage of Azure Front Door and related services.
Overload events can be unintentional as well—a sudden spike in legitimate traffic might overwhelm capacity if autoscaling or limits aren’t configured correctly. While these scenarios are somewhat different from software fault causes, they are realistic incident triggers that operational teams must anticipate in cloud environments.
Organizational and Process-Related Causes
Beyond immediate technical triggers, organizational factors and process failures are often at the heart of serious incidents. Studies show that human and process issues contribute to the majority of outages—between two-thirds and four-fifths of major incidents involve some human factor. It’s rarely just bad luck; often there were latent issues in how the system was designed, how changes were managed, or how the team responded.
Inadequate Change Management and Testing
A large portion of incidents stem from changes made without sufficient oversight or testing. In fast-paced SaaS deployments with continuous integration and deployment, changes occur frequently. When proper change management processes are not followed, the risk of an outage rises significantly.
A post-incident analysis by the U.S. FCC of a February 2024 AT&T outage found the cause to be procedural failures: a network update was applied with a lack of peer review, failure to adequately test, and insufficient approval safeguards, leading to a massive outage that blocked over 92 million calls including 25,000 calls to 911. This case shows that had standard change controls been in place, the outage might have been avoided.
Many organizations find that not following established procedures is a common root cause when things go wrong. Nearly 40% of enterprises reported a major outage in the past three years caused by human error, and in 58% of those cases, staff failed to follow procedures or best practices according to Uptime Institute’s analysis. Sometimes the procedures themselves are faulty—45% of those human-error outages involved a process design flaw.
Gaps in Monitoring and Incident Detection
If something starts failing and your monitoring doesn’t detect it promptly, the incident can silently grow until customers are screaming. Lack of observability is an often-cited factor that worsens incidents. When teams lack proper monitoring, logging, or alerting for certain failure modes, they may not realize there’s a problem until users are badly impacted. This delay in detection translates to more downtime.
Research backs this: organizations with full-stack observability tools experienced 79% less downtime on average and were 51% more likely to catch disruptions through their monitoring systems rather than from user reports. In practice, that means if you have good dashboards and alerts watching all key aspects—latency, error rates, resource usage—you’ll notice anomalies and intervene before it becomes a full-blown outage.
Monitoring gaps can cause incidents in subtle ways. Imagine a microservice quietly hitting an internal error repeatedly due to a hidden bug—if no alert is set on its error rate or if logs aren’t watched, the service might enter a failure state. By the time someone notices, the incident is more severe. Real incidents have occurred where an alarm that should have fired didn’t exist or was misconfigured, allowing an issue to go unchecked.
Poor Incident Response and Communication
How an organization responds once an incident is underway can strongly influence the outcome. A poorly managed incident response—lacking clear roles, coordination, and communication—can turn a small hiccup into a lengthy disruption.
In the heat of an outage, teams need to diagnose and fix the issue under pressure. If there is no predefined incident process, confusion can reign. Multiple people might try uncoordinated fixes, sometimes making things worse, or everyone might assume someone else is handling it. Teams that run regular incident drills and have runbooks for common failure scenarios tend to resolve issues faster and with less chaos.
Communication during an incident happens on two fronts: internal, between engineers and teams, and external, to customers and stakeholders. Both need to function well. Internally, poor communication can mean important information isn’t shared. Externally, failing to update customers can damage trust. Lack of timely communication is one of the biggest sources of criticism during incidents. Users will tolerate some downtime, but being kept in the dark frustrates them far more.
When Slack had an outage, their team provided frequent status updates acknowledging the problem, which was praised by users. In other incidents across various industries, companies that failed to acknowledge an outage for hours faced heavy backlash. The lesson is that transparency and communication are part of incident management—not an afterthought.
Organizational Complexity and Silos
Large cloud and SaaS environments are inherently complex, with many interdependent services often owned by different teams. This complexity itself can be seen as a meta-cause of incidents: it increases the chances of mistakes and makes troubleshooting harder. The growing complexity of hybrid IT environments is increasing exposure to operational errors and other failures.
Microservices architectures mean an application might have dozens of components; a failure in one can have knock-on effects that are hard to trace without deep knowledge. If each microservice is owned by a separate team, a lack of collaboration or knowledge sharing can slow down incident resolution. Similarly, if documentation is poor, engineers might not know the exact behavior of a legacy component when it gets overloaded, leading to trial-and-error during an outage.
Furthermore, complex organizational processes can cause delays—for example, if a critical fix needs approval from a change advisory board that only meets infrequently, that process can turn an incident into a prolonged one. The key point is that organizational design and culture can either mitigate or amplify the impact of technical problems. A culture that encourages knowledge sharing, cross-team support, and continuous learning will handle incidents more gracefully.
Real-World Case Studies
Several real-world incidents illustrate how technical and human factors interplay in practice.
In April 2022, a maintenance script at Atlassian intended to disable a legacy app was run with incorrect parameters, accidentally deleting 883 customer sites across Jira, Confluence, and other Atlassian Cloud products. The outage lasted up to two weeks for some customers as Atlassian worked to restore data from backups. The script lacked proper validation, and the deletion propagated before it could be stopped.
The February 2024 AT&T outage knocked out internet and phone service for millions for over 12 hours. A misconfigured network element was added to the production network during a routine nighttime maintenance window. The process did not follow AT&T’s established install procedures, which require peer review. AT&T responded by auditing their network for missing controls and tightening approval processes.
The May 2025 Slack outage revealed how scaling an architecture without updating configurations can break communication between components. The incident also exposed a gap in monitoring—Slack’s team detected the issue quickly, but the root cause wasn’t obvious until they dug into how the routing layer was set up.
The October 2021 Facebook outage demonstrated how a technical config error combined with a lack of safe recovery methods could result in a six-hour global outage. A command issued during routine maintenance misconfigured Facebook’s internal backbone routers, and the cascading effect locked out Facebook’s own engineers from remote access.
The February 2017 AWS S3 outage showed how human error combined with insufficient tooling safeguards can bring down critical infrastructure. An engineer unintentionally entered a command with the wrong target, and the servers terminated included those running critical subsystems. Amazon admitted the tooling allowed too much to be taken down at once and has since added limits.
Each of these case studies shows that incidents often result from multiple factors aligning: a technical flaw or mistake combined with lapses in process or unforeseen system interactions.
Preparing for Incidents and Learning from Failures
Software incidents in SaaS and cloud environments are caused by a mix of tangible technical problems and the more subtle issues of human and organizational systems. Bugs, glitches, and hardware failures will happen—they are inherent to complex technology. However, whether those initial faults turn into prolonged customer-impacting outages depends greatly on preparation and process.
Strong engineering practices like thorough testing, staged rollouts, and redundant architecture reduce the frequency of failures. Equally, strong operational practices like robust monitoring, clear incident response plans, and a culture of learning from mistakes minimize the impact of failures that do occur.
For a broad audience ranging from developers to executives, it’s important to recognize that reliability is not just a technical issue; it’s a socio-technical issue. An incident might start with a server crash, but it could be precipitated by an earlier decision to skip a code review, and it could be resolved quickly or painfully depending on the team’s communication. Executives should note that investments in reliability have real ROI—full observability can cut downtime by 79%, and 80% of operators believe better management and processes could have prevented their worst outage.
Running incident response drills is one of the best ways to prepare. By simulating scenarios like a bad deployment causing errors or a primary database going down without monitoring catching it, teams can practice their technical fixes along with their coordination and communication. These drills often uncover gaps—maybe the runbook is outdated, or perhaps the on-call engineer didn’t know who to notify. It’s far better to find those in a rehearsal than during a real outage.
Finally, post-incident learning closes the loop. Every incident should lead to a blameless postmortem that asks: What caused it? Why did our defenses fail? How can we improve? Over time, this continuous improvement makes incidents less frequent and less severe. Companies like Google, Amazon, and others attribute much of their reliability to this culture of learning from failures.
Software incidents arise from the complex interplay of technology and people. Bugs, misconfigurations, and failures will happen, but a well-prepared organization can prevent most from becoming major outages and can respond deftly to those that do slip through. In the high-stakes world of SaaS and cloud services, resilience is not an accident; it’s engineered and managed. Armed with this knowledge of how incidents typically happen, your incident response drills can be more realistic and effective, ensuring that when the real pager goes off, your team is ready to handle whatever comes their way.
Explore our Famous Incidents collection to study real-world failures and practice handling them in realistic simulated environments.
Sources: Uptime Institute 2025 Annual Outage Analysis, New Relic 2024 Observability Forecast, Cloudflare, Fastly, Amazon AWS, Meta, FCC, and Google Cloud.