Why modern outages are increasingly caused by financial constraints, not technical ignorance
The Financialization of Reliability
In the cloud era, reliability engineering used to be a purely technical discipline — uptime targets, error budgets, chaos testing, and redundancy planning. Today, cost is inseparable from reliability: every replica, every region, and every autoscaler decision now has a dollar sign attached. What was once a purely engineering decision has become a financial one.
Cloud-native architectures changed not only how we deploy systems but also how we pay for them. The result? Many modern outages aren’t caused by missing skills or broken tools — they are caused by budget decisions that trade resilience for cost savings.
In this world, engineers aren’t just fighting bugs — they’re wrestling with bills.
When Cost Decisions Become Reliability Decisions
Site Reliability Engineering (SRE) introduced error budgets to balance reliability and velocity. An error budget quantifies acceptable unreliability — e.g., 99.9% uptime yields 43 minutes of allowable downtime per month — and informs when teams should prioritize reliability over new features. But this concept assumes reliable resources are available when needed. When those resources are rationed by finance, error budgets become financial budgets that restrict engineering choices, not safety margins for technical risk.
SREs are now being asked to manage cloud spend as a core practice. According to recent industry analysis, cloud cost optimization is no longer a peripheral FinOps issue; it has become a core part of SRE practice, because the same architectural and operational choices that ensure availability also drive cost. Redundancy, autoscaling, multi-region failover, and observability — all fundamental to high availability — are also the biggest drivers of cloud spend.
The direct link between cost and reliability shows up in surprising places:
- Autoscaling policies that throttle instances to reduce spending can lead to under-provisioned production environments when demand spikes, increasing outage risk. Research shows autoscalers are sensitive to metric faults, leading to misallocation that degrades service while lowering cost.
- Over-engineered redundancy (e.g., provisioning multi-region failover for all services) often yields minimal actual benefit but massive cost overheads — studies estimate 30–60% cloud waste from unnecessary resilience patterns.
In other words, reliability without cost awareness is unaffordable, and cost awareness without reliability awareness can cause outages.
Misalignment Between FinOps and Engineering Increases Failure Risk
One of the key reasons cost becomes a reliability problem is organizational silos. FinOps and SRE teams often operate with different metrics and incentives:
- FinOps focuses on reducing spending and budget adherence.
- SRE focuses on uptime and error budgets.
Without alignment, cost-cutting recommendations may suggest removing replica capacity or scaling back monitoring — both decisions that increase fragility. Rootly’s analysis of FinOps–SRE misalignment shows that such disconnects can cause increased automation toil, delayed incident response, and budget overruns that negatively impact both cost and reliability.
The lack of shared understanding leads to situations like
- Cost alerts recommending resource cuts that engineers ignore because they don’t impact reliability metrics.
- Engineers provisioning resources for safety that don’t get challenged because FinOps lacks context on operational necessity.
If reliability and cost are measured separately, systems get optimized in silos—which is how outages caused by cost decisions happen.
The Hidden Costs of Reliability Over-Engineering
Reliability practices that once improved uptime can now inflate costs disproportionately. A recent analysis shows that over-engineered cloud resilience efforts — like applying multi-region failover and active-active deployments indiscriminately — often increase cloud waste by 30–60% with minimal benefit to real SLAs.
Consider a company running every non-critical microservice in multiple regions by default. Their monthly cloud bill spikes, but their actual uptime improvement is negligible because real outage risks come from upstream dependencies or control plane failures, not from individual region unavailability.
Understanding where to invest in reliability — and where to accept limited risk — is now as much a financial decision as a technical one.
Cost Visibility as a Reliability Signal
New approaches treat cost itself as an observable engineering metric — on par with latency, error rate, and throughput. New Relic, for example, built cost visibility into its observability pipelines, correlating spending directly with performance inefficiencies. This integration helped the company reduce cloud costs by 60% while preserving reliability.
Treating cost as a first-class signal means teams can answer questions like
- Which services are consuming a disproportionate budget relative to impact?
- Which scaling policies result in unnecessary costs without reducing incidents?
- How much of the cloud spend is “benefit vs. insurance”?
This transforms cost from an afterthought to a runtime metric that informs engineering decisions.
Cloud Outages Have Financial Consequences
Cloud outages don’t just hurt service availability — they carry real financial impact that often exceeds direct cloud spend. Hidden costs from downtime include lost revenue, engineering recovery work, reputational damage, and SLA compensation — costs that are usually not reflected in simple budget dashboards. One analysis shows that recovery costs from downtime — engineering time, rollback validation, customer support — can add 40–60% on top of immediate outage losses if not tracked.
Yet, cloud billing often doesn’t reflect these business impacts because SLA credits seldom equate to actual losses — a structural mismatch that hides financial risk.
A New Discipline: Cost-Aware SRE
To address this, a new hybrid discipline is emerging: Cost-Aware SRE — the intersection of reliability engineering and financial optimization. This approach unifies engineering and finance, treating cost and reliability as co-dependent metrics rather than competing targets.
Best practices include:
- Attributing cloud cost to applications, teams, and features.
- Incorporating cost into SLIs/SLOs alongside latency and error rates.
- Evaluating autoscaling policies with cost and reliability tradeoffs.
- Aligning engineering and FinOps goals through shared dashboards and cost-incident tagging.
Successful teams adapt automation and policy to reflect these tradeoffs, avoiding brittle setups that save money today but break tomorrow.
Conclusion — The New Reliability Landscape
Reliability engineering no longer exists in a vacuum. Cloud economics have turned every infrastructure choice into a cost decision — and every cost decision into a reliability risk.
If your engineering org still treats cost as a finance problem, you will eventually face a crisis where:
- Reliability is compromised to save money
- Outages cost more than the savings justified
- Engineering morale collapses under conflicting goals
Instead, leaders must adopt a holistic model where cost signals are first-class metrics, error budgets reflect both technical and financial realities, and cross-functional alignment ensures decisions are both budget-aware and reliability-safe.
Ultimately, treating cost as part of reliability engineering is not optional — it’s essential for building systems that are not just technically robust but financially sustainable.