What Factors Contribute to the Cost of Downtime?

Every IT team I've worked with has a version of the same conversation eventually: something breaks, nobody planned for it, and the fix costs more than it would have to prevent it. That's not a knock on IT staff. It's what happens when infrastructure grows faster than the budget and attention available to manage it.

The numbers on this have gotten genuinely alarming. According to Splunk's 2026 Hidden Costs of Downtime study, conducted with Oxford Economics across 2,000 executives at Global 2000 companies, aggregate downtime costs for those firms have hit $600 billion annually, a 50% jump in just two years. The average cost per organization now runs $15,000 per minute, and companies report an average 3.4% drop in stock price following a disclosed outage.

"Downtime is inevitable; prolonged disruption is not," said Kamal Hathi, SVP and GM of Splunk, a Cisco company, in the report's release. "The most resilient organizations are not the ones with the most tools or the biggest vision for AI. They are the ones that align technology with business outcomes, empower people with context, and design systems that bend, but do not break, under pressure."

That framing matters, because a lot of older guidance on downtime cost treats it as a purely financial spreadsheet exercise: multiply hourly revenue by hours down, add a fudge factor for reputation. The real picture in 2026 is messier and, in some ways, scarier. Let's get into what actually drives the number up or down.

What is downtime cost, really?

Downtime cost is the full financial impact, direct and indirect, of a system or process being unavailable when it's supposed to be running. It's not just lost revenue. Splunk's research found that organizations now absorb an average of $95 million in lost revenue annually from downtime alone, nearly double what was reported just two years earlier. And that figure doesn't include what happens after the outage: the regulatory fines (averaging $51 million per organization in the same study), the ransomware payouts where relevant (now averaging $40 million, nearly tripled since 2024), or the slower bleed of customer churn.

Eighty-one percent of technology leaders in the Splunk survey cited customer loss as a direct consequence of downtime, and 47% admitted customers are often the first ones to notice an outage before internal monitoring catches it. That last stat is worth sitting with for a second. If your customers are your alerting system, your observability has a real gap.

What factors actually drive downtime cost up or down

1. How fast you detect the problem in the first place

Only 38% of technology executives in the Splunk study report consistently identifying the root cause of a downtime incident. Worse, about a third (36%) of security leaders admit downtime often gets misclassified as a plain IT issue rather than a security incident, which hands attackers a head start while the wrong team investigates. This is where teams usually trip up: they jump straight to remediation before confirming what actually broke, and end up fixing the symptom while the real cause keeps running.

2. Recovery time, and what recovery actually costs beyond the obvious

The relationship between recovery time and cost isn't linear, it accelerates. The longer an outage runs, the more it drags in adjacent costs: overtime pay for emergency response teams, contractual penalties if you blow through SLA thresholds, and the operational drag Splunk describes as needing "large numbers of personnel to fix issues," cited by 89% of tech leaders in the survey. Ninety percent reported increased demand on customer support during an incident, with 76% of finance and 74% of marketing teams feeling the pressure too. Downtime is rarely contained to the department where it started.

3. Business model dependency on uptime

A SaaS company or an e-commerce platform loses revenue the second a system goes dark. A consulting firm running mostly in-person meetings barely notices the same outage. If your business model depends on software delivering continuous value, your downtime tolerance is effectively zero, and your cost curve reflects that.

4. Whether the outage becomes a disclosed security event

This is the factor that changed the most in the last two years. Publicly disclosing a data breach is now viewed as the single most severe hidden cost of an incident, with 71% of technology executives rating it as very or prohibitively disruptive, up sharply from 23% in 2024. That's not a typo. The reputational calculus around disclosure has shifted enormously in a short window, likely driven by tighter regulatory disclosure requirements and more aggressive media coverage of breaches.

5. Regulatory and legal exposure

Beyond the direct fines mentioned above, litigation risk deserves its own line item. Drug manufacturer Merck lost more than $1.3 billion in the 2017 NotPetya attack, and spent over six years fighting its own property insurers over whether the "act of war" exclusion applied to a state-linked cyberattack. Merck and its insurers settled the dispute for $1.4 billion in January 2024. If your organization is relying on cyber insurance as a backstop for a major outage or breach, read the war and state-actor exclusions in your policy now, not after the fact.

6. Third-party and SaaS dependency failures

The perceived frequency of cybersecurity-related downtime caused by SaaS and third-party application failures has nearly tripled since 2024, with 56% of security leaders now experiencing these issues often or very often, according to Splunk's data. This tracks with a broader shift: your infrastructure resilience is now only as strong as the weakest vendor in your dependency chain, and most organizations still cannot fully map that chain.

7. Legacy infrastructure nobody wants to touch

A SnapLogic survey of 750 IT decision-makers found that legacy tech upgrades cost the average business $2.9 million in a single year, and nearly two-thirds of companies spend more than $2 million annually just maintaining old systems before any modernization even happens. "Maintenance and development costs continue after the initial implementation phase," said Jeremiah Stone, CTO of SnapLogic, in comments to CIO Dive. "Companies also struggle to dissolve technical debt when legacy systems are intertwined with existing workloads, making them imperative to everyday operations." Nearly a third of respondents said up to 25% of their legacy systems can't support current AI tooling either, which matters more each quarter as vendors push AI features into core products.

8. Whether AI is actually helping or just adding a new failure mode

This one didn't exist as a meaningful factor a few years ago. Organizations are now spending a median of $24.5 million annually on AI tools meant to prevent and respond to downtime, per the Splunk data. The payoff for doing this well is real: firms classified as "AI Workflow and Triage Experts" avoided public breach disclosure 74% of the time versus only 54% for everyone else, and were nearly three times as likely to report they'd never lost a customer to downtime. But there's a real tradeoff here worth being honest about: every single technology leader surveyed reported experiencing some form of AI-related downtime, and 68% expressed concern their own AI agents would behave unpredictably. AI is not a free resilience upgrade. It's a new system that needs its own monitoring and its own failure playbook.

How to actually calculate your own downtime cost

The general Global 2000 average of $15,000 per minute is not useful for a 40-person business. You need your own number, and the formula, while not glamorous, still works:

Cost of Downtime = Lost Revenue + Other Losses (recovery costs, lost productivity, regulatory exposure, reputational impact)

Lost Revenue = Hourly Revenue x Number of Downtime Hours x % Uptime Dependency

Say your company makes $4,000 an hour on average and needs 60% uptime dependency to keep operating (meaning 60% of your revenue-generating activity requires the system in question to be live). A 10-hour outage in a month costs you: $4,000 x 10 x 60% = $24,000 in lost revenue alone. If your business needs 100% uptime, like most e-commerce operations, the same outage costs $40,000 in lost revenue before you even start counting recovery labor, SLA penalties, or customer churn.

Lost Employee Productivity = Hourly Pay Rate x Number of Employees x Downtime Hours x % Uptime Dependency

Using the same 10-hour outage, with an average pay rate of $15 an hour across 40 employees: $15 x 40 x 10 x 60% = $3,600 in paid-but-unproductive labor. That's before overtime during recovery, which tends to run higher.

The honest limitation of this formula: it assumes your revenue is smoothly distributed by the hour, which most businesses aren't. If your outage hits during your highest-traffic window, multiply accordingly. If it hits overnight for a B2B operation with no after-hours customer activity, the real cost may be lower than the formula suggests, mostly deferred rather than eliminated.

What actually reduces downtime cost, based on what's working for higher performers

The Splunk data draws a clear line between organizations that handle this well and those that don't, and the differentiators are specific rather than vague:

• End-to-end observability, treated as the top investment priority. Roughly three-quarters of ITOps and engineering leaders now rank this above hardware or data center upgrades. You cannot fix what you cannot see, and 98% of organizations with the lowest downtime costs confirmed that full visibility across their stack was very or extremely important to that outcome.
• Automation aimed specifically at human error. Human error remains the leading cause of downtime across the technology stack, and 66% of ITOps leaders are now prioritizing automation investment specifically to reduce it, not to replace staff, but to remove the repetitive manual steps where mistakes happen.
• A named root-cause process, not just an incident response plan. The gap between the 38% who can consistently identify root cause and the rest is where a lot of recurring outages live. If your postmortems keep ending in "we're not entirely sure why," that's the problem to fix before buying new tooling.
• Actual mapping of third-party and SaaS dependencies. Given how much downtime now traces back to vendor failures rather than internal systems, know which of your critical functions depend on someone else's uptime, and have a documented fallback for each.
• Modernizing legacy infrastructure on a schedule, not a crisis. Track end-of-support dates the same way you track software licenses, and budget for replacement before failure forces the issue. Waiting for the outage to justify the spend is the most expensive way to make this decision.

The honest bottom line

You cannot get downtime to zero. Not even organizations spending tens of millions on observability and AI-driven triage have eliminated it, they've just gotten faster at catching it and more disciplined about not letting one failure cascade into ten. That's the realistic target: not prevention of every incident, but containment fast enough that the cost curve stays flat instead of compounding.

If your organization doesn't have the internal bandwidth to build that kind of observability and root-cause discipline, an experienced managed IT services provider handling multiple environments will typically catch patterns and vendor-specific failure modes faster than an internal team seeing a given failure for the first time. Given the numbers above, that's not a minor efficiency gain, it's often the difference between a contained incident and a headline.