Most engineering leadership teams are grappling with the fact that reliability costs too much, teams are burnt out, and they feel something has to change. Typically, teams resort to optimization: smarter alerting, faster runbooks, better dashboards, and more training. Teams invest heavily, see modest gains, and then end up right back where they started when the next incident hits.
Optimization doesn’t fail because of execution. It’s that the underlying model is broken. Reactive SRE – managing reliability by responding to incidents after they occur— is actually really expensive. No amount of refinements will change this. Organizations need to understand why the reactive model is flawed; until they do, they will keep spending more, only to stand still.
The cost of reacting
The cost of downtime is often defined by the revenue impact: the minutes of unavailability multiplied by a per-hour rate. However, that number doesn’t accurately represent what reactive operations actually cost.
True costs include the hours engineers spend investigating incidents. It also includes senior SREs being pulled off a critical project to triage alerts that were just noise, hours-long postmortems, remediation work, meetings held to decide the next step, and burnout and the attrition rate that follow when engineers spend more time firefighting than building.
The 2024 CrowdStrike outage is considered to be the largest IT outage. A faulty software upgrade caused a global disruption for 8.5 million Windows systems globally. Flights were grounded, hospital record systems became inaccessible, and financial transactions were disrupted. On paper, Fortune 500 companies suffered a $5.4 billion financial loss, but there was a hidden cost. SRE and DevOps teams had to perform manual remediation plans for days, resulting in team burnout and fatigue. This is a big indicator of how much a reactive model takes from the people on the inside.
Reactive SRE generates all of these costs for every incident, every time. Optimization only reduces per-incident costs; it doesn’t reduce the number of incidents. Unfortunately, the distributed systems that today’s enterprises rely on mean the number of incidents won’t go down on its own.
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Complexity is outrunning teams
Modern tech environments are architecturally different from the systems that shaped traditional reliability practices. Infrastructure spans across multiple cloud and on-premise environments. Customer transactions touch multiple services.
This environment makes failure much more probable. The dependability, rate of change, and services used mean incidents are inevitable. It isn’t about whether something will break; it’s about whether your team will know about it before a customer does.
The biggest issue with reactive SRE models is that the number of potential failure modes grows faster than teams can expand. Teams end up adding more alerts, dashboards, and on-call rotations. This results in alert fatigue, burnout, and the reality that many issues are often discovered by customers well before engineers know something is wrong.
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Optimization is the wrong answer
Reactive SRE optimization is supposed to focus on doing it more efficiently. This ranges from triaging alerts more quickly to resolving incidents faster and reducing the mean time to resolution (MTTR). These changes don’t solve the bigger issue.
Hospitals try to optimize the flow of their emergency rooms with the goal of reducing wait times and improving their protocols. While it’s commendable work, it doesn’t actually reduce the number of patients coming to the ER. What does? Prevention. A stronger focus on healthy diets, exercise, and managing chronic disease can help dramatically decrease trips to the ER. And this logic applies to system reliability. While you might be good at responding to incidents, it doesn’t mean it’s not costly and leaves you wondering when something else will go wrong.
What a proactive model actually looks like
Proactive SRE is not about catching problems sooner. It’s about a system continuously understanding, rather than incident response. Reliability work happens before failure happens in a proactive model. Systems are continuously observed rather than just monitored; it’s critical to understand normal system behavior to identify incidents versus relying on alerts.
The October 2025 AWS DNS disruption provides a useful outlook. In a rare event, the system’s
automation capabilities caused the deletion of the DNS record, leaving it without a valid DNS record. The anomalous behavior signal could have been flagged as it developed with continuous monitoring, rather than causing a 15-hour global disruption. This showcases the gap that proactive models run on: the space between “something is wrong” and “something went wrong.”
Proactive approaches require two things:
- The ability to correlate signals across domains in real time
- The ability to surface risks and identify which are early warning signals outside of the norm
Unfortunately, none of this can be achieved solely through human effort. AI becomes essential to the workflow to enable a different approach.
AI as the enabler of proactive reliability
AI in reliability is often thought of as a mechanism for accelerating incident response: intelligent triage, automated root-cause analysis, and faster escalation. These applications are legitimate and provide a meaningful way to make improvements.
The biggest opportunity comes from using AI to enable operations at scale. AI can continuously analyze behavioral patterns across thousands of signals, identify combinations of leading indicators that historically precede failure, and surface those risk signals to engineers before any service-level objective is violated. Engineers are quickly notified of emerging incidents, giving them time to act.
AI also changes the nature of reliability work. Engineers now have to proactively manage risk instead of responding to incidents as they arise. They no longer have to spend their time firefighting. It’s being redirected to understand system behavior and prevent future failures.
Engineering teams that make this transition gain a structural advantage and experience:
- Reliability that scales with system intelligence rather than headcount.
- A decline in the number of incidents, not just the duration of them
- Less on-call time means less burnout
Ultimately, the relationship between engineering capability and system complexity shifts.
Structural issues require structural solutions
Organizations running distributed systems at scale will eventually pay the price for reactive reliability. Businesses lose an estimated $1.5 trillion annually due to IT service disruptions.
Engineering teams need to look at the predictable consequences of models that wait for things to go wrong. It’s not about getting better at incident response. It’s about whether organizations have fewer incidents in the first place. Optimizing a reactive model will improve its efficiency. To truly reduce the cost of reliability, organizations must shift the intervention point earlier.
This is ultimately a structural issue that requires a structural solution.
