During a festival flash sale in Bengaluru, graphs light up, alerts multiply, and the on-call channel turns into a firehose. After two years of AI-powered operations, why does the pager still wake people for the same issues while the observability bill grows quarterly? In 2024, India's festive e-commerce gross merchandise value was close to 14 billion US dollars, and several sources said that traffic and sales went up by double digits. When alert noise covers up real events during these surges, customers immediately feel the pain.  

FinOps teams have shifted priorities toward reducing waste and managing commitments. That means any AIOps initiative that increases data ingest and license costs without cutting toil or improving resolution time will face more challenging questions.

SRE practice offers a simple test. Page only when a service level objective is at risk and tie alerts to the burn rate of the error budget, not to every metric that twitches. If an alert does not drive a human action, it should not page. This blog describes the failure patterns that turn AIOps into fancy alerting with a bigger bill. 

Where AIOps goes wrong and how to spot it early

Most AIOps disappointments stem from four habits that grow alert volume and cost without improving reliability. The fixes begin with the SRE basics and cost awareness that FinOps teams now expect.

Paging without SLOs

If pages are not tied to service level objectives and error budget burn, you alert on noise instead of user impact. SRE guidance is clear: alert on symptoms customers feel, not on every internal metric.

How to spot it early

• Less than three-quarters of paging policies reference an SLO or burn-rate threshold. 
• Many pages trigger on CPU or memory without any link to user error rates or latency.

Pages that are not immediately actionable

A page must tell the responder what to do next, or it trains teams to ignore it. Attach runbooks and make the first step obvious.

How to spot it early

• More than 20 percent of pages have no linked runbook or checklist.
• Median time from page to first action does not improve as tools increase.

Duplicate, uncorrelated alerts create fatigue

One fault becomes many pages when events are not deduplicated or correlated by service topology. Industry reports link high alert volume to fatigue and slower responses.

How to spot it early

• Incident timelines show many alerts from the exact root cause across tools.
• First responders regularly close multiple duplicates before any fix begins.

High-cardinality metrics drive runaway spend

Exploding labels and tags increase time series counts and inflate bills. Vendors advise active governance of custom metrics and cardinality.

How to spot it early

• Observability cost per service rises faster than traffic or revenue.
• Dashboards depend on dimensions that are rarely queried in practice. 

An Engineering- Led observability model that cuts noise

The fix is not another rule set. It is a system that pages only when customer experience is at risk, routes a single correlated incident to the right owner, executes a safe first action, and treats telemetry as a budgeted resource. 

SLO first paging with multi-window burn rate alerts

When the service uses up its error budget, not when an internal metric moves. Use rules with several windows and burn rates to catch quick surges and slow leaks without overwhelming people. Write down an error budget policy so everyone understands what to do when burn speeds up.

Intelligent anomaly detection with service topology correlation

Baselines that consider seasonality cut down on false positives, but correlation stops a chain of duplicates. A dependency graph reveals signals that are tied to a group. Thus, one fault becomes one event with one owner. Reports from research and business reveal that many alerts and signals that are not useful cause tiredness and delayed response; thus, correlation is necessary.

Auto remediation triggers with guardrails

Write code for the initial step of a runbook and let the platform run it automatically when it's safe to do so. Limit actions by scope, blast radius, time window, and rollback plan. Use tried-and-true methods, such as linking automation runbooks to incident response plans, so you first carry out the plan instead of looking for a wiki.

Cost-aware alert placement and telemetry budgets

Most "fancy alerting" failures don't take costs into account. High cardinality measurements and limited logs raise costs without improving things. Consider labels, sampling, retention, and log verbosity design decisions that cost money, and ensure that alert rules align with business value. 

Intelligent anomaly detection that reduces work

Anomaly detection should reduce human toil, not inflate pages. The winning pattern is simple: detect seasonality correctly, correlate signals by service topology, and suppress anything that does not threaten an SLO.

• Seasonality-aware baselines per service. Stop paging for predictable diurnal swings and focus on genuine outliers. Pair with multi-window burn-rate alerts so slow leaks and fast spikes both surface without flooding responders. 
• Multi-signal correlation on a service map. Group related metrics, logs, and traces to promote one incident with one owner. This reduces alert storms that delay real response.
• SLO-gated anomalies. If the anomaly does not move the error budget burn, it should not be paged. Alert on user-visible symptoms first, causes second. 

Auto remediation triggers with guardrails

Automation should turn the first step of a runbook into code and execute it safely before waking humans. Most teams lose time in investigation and diagnosis, so even a small set of preapproved actions can cut response times.

• Preapproved actions for common faults. Examples include restarting a failed pod, rolling back a bad deploy, or scaling a hot pool, all captured as runbooks first, then automated.
• Trigger on symptoms tied to SLO burn. Only fire automation when user impact is likely, not on every metric twitch. Google SRE guidance explicitly favors symptom-based alerting.
• Measure outcome, not clicks. Track time to first action and MTTR before and after introducing automation. Vendors report meaningful MTTR reductions when repetitive fixes are automated.

Cost aware alert placement and telemetry budgets

FinOps teams now prioritize reducing waste and managing commitments. That means AIOps must treat alert rules, metric labels, log volume, and trace sampling as budgeted choices, not defaults.

• Price-aware alerting. Favor SLO burn alerts and keep the query fan out low. Providers now document explicit charges for alert conditions, the number of time series an alert query returns, and guidance to consolidate policies to control cost.
• Govern metric cardinality. Avoid “every label on every metric.” High cardinality multiplies distinct time series and drives custom metric spend; cloud docs advise limiting dimensions.
• Sample and right-size retention. OpenTelemetry head or tail sampling and sane log retention can cut tracing costs while preserving proper signals.
• Tame scrape frequency. Longer scrape intervals reduce data points per minute and lower billable volume without losing the ability to detect SLO burn.

Conclusion

Many AIOps programs page on internal metrics, multiply duplicates, and grow telemetry cost without improving customer outcomes. The way out is an engineering-led operating model. Anchor paging in SLO burn so humans wake only when the user experience is at risk. Correlate signals by service topology so one fault becomes one incident with one owner. Convert the first step of your most common fixes into safe, pre-approved automation. Treat labels, log verbosity, trace sampling, and alert queries as budgeted design choices.

Ready to cut noise and the bill at the same time? Contact the Parkar Digital expert team for a perfect game plan strategy. 

FAQs

Our on-call channel is drowning in alerts. How can we tell if our AIOps is just fancy alerting?

You are paging on noise if most pages are still threshold-based and not tied to SLO burn. Repeated duplicates in one incident, missing runbook links, and costs that grow faster than traffic are clear signs that the system adds volume, not value.

What does a safe, preapproved auto-remediation look like in practice?

Start with a repetitive, low-risk fix such as restarting a failed pod or rolling back a bad release, gated by simple health checks and change windows. Execute one instance at a time, verify recovery, and roll back and page the owner if health does not return.

How can we cut the observability bill without losing visibility?

Set per service telemetry budgets, cap high cardinality labels, and use trace sampling on low criticality paths while keeping full fidelity for SLO critical flows. Prefer SLO burn alerts that query fewer time series and right size log retention so cold data costs less.

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