Eliminating the Opportunity Cost of Cloud Anomalies

‍From Detection to Seamless Action— The Future of Anomaly Detection in FinOps

Cloud costs are skyrocketing, now topping $650 billion per year across AWS, Azure, and GCP. Yet many organizations still rely on threshold-based anomaly detection—a system designed for a simpler time.

Our CTO Simon Mestdagh explores why traditional methods are no longer enough and how machine learning (ML) is reshaping anomaly detection in FinOps. It’s no longer about catching anomalies—it’s about knowing which ones matter, understanding them in context, and acting quickly.

The future of anomaly detection is not just about better detection.
It’s about making the response so efficient that the opportunity cost of anomalies approaches zero.

‍

The Problem with Thresholds

Threshold-based systems work like tripwires:
You set a static limit—like “alert me if costs go up by 20% in a day.” Once the limit is crossed, the system sounds the alarm.

This might work in small, stable environments. But most cloud ecosystems grow rapidly and change constantly.

The Core Issues:

• Thresholds don’t scale.
  Manual rule-setting quickly becomes unmanageable.
• They generate false positives.
  Teams get flooded with alerts about harmless, expected cost changes.
• They miss context.
  A spike could be part of a seasonal trend or a planned deployment, but thresholds can’t tell the difference.

Over time, teams stop paying attention to alerts.

‍
It’s like that check engine light in your car that keeps coming on for no clear reason. And when there is a real issue, you have no idea where to start because you’re lacking deeper context. After a few false alarms, you start ignoring it—until the real problem shows up, and by then, it’s too late.

Thresholds were useful in the past, but they are blunt tools in a fast-paced, multi-cloud world.

‍

‍

The Smarter Alternative: Machine Learning

Machine learning is purpose-built for today’s cloud complexity.

ML learns what’s normal for your environment. It adapts to seasonality, growth, and multi-service patterns. It continuously improves, getting smarter with every new data point.

ML doesn’t just detect changes—it figures out whether those changes actually matter.
It tells you:

• What’s happening.
• Why it matters.
• What to do next.

This shift is what makes ML essential for the future of FinOps.

‍

Why Machine Learning is More Than Just Detection

‍

1. ML Solves the False Positive Problem

Traditional thresholds flood teams with noise. ML reduces this dramatically by adding context and focusing only on meaningful anomalies.

‍

2. ML Keeps Pace with Cloud Change

Cloud environments evolve daily—new services, shifting workloads, and rapid scaling. ML adapts automatically, where thresholds break.

‍

3. ML Scales Effortlessly

Whether you're monitoring a few resources or thousands across multiple clouds, ML scales without overwhelming teams or requiring constant rule maintenance.

‍

4. ML Enables Faster, Smarter Action

Here’s where the biggest step forward happens.
ML-based systems don’t just detect—they prioritize, explain, and guide teams to take the right action quickly.

‍

This shrinks the opportunity cost of anomalies.
It closes the gap between detection and resolution, so cost-saving opportunities aren’t left on the table.

‍

How ML-Based Detection Works

Two Layers of Smart Monitoring:

• Broad Monitoring:
  ML scans high-level trends across services, accounts, and regions to catch large, sweeping shifts that might otherwise go unnoticed.
• Deep Tracking:
  ML drills into individual resources like databases or compute instances to uncover fine-grained anomalies that traditional systems miss.

Smart Classification:

ML systems don’t just throw alerts—they explain them.
They tell you:

• Is this a sudden spike?
• Is a seasonal peak missing?
• Is this a new, growing cost trend?

This isn’t just detection—it’s storytelling with actionable next steps.

‍

The Real FinOps Shift: From Alerts to Actionable Prioritization

The FinOps of the future isn’t about generating more alerts.
It’s about making better, faster decisions with fewer, more meaningful signals.

Here’s what’s coming:

• Frictionless anomaly resolution
  Action will become easier, clearer, and faster.
• Context-rich decision support
  Anomalies will arrive with full stories—why they matter and what’s been done in similar cases.
• Continuous learning from team decisions
  ML will not only learn from cloud data but from how teams resolve anomalies, improving prioritization over time.
• Towards autonomous resolution
  Some low-risk, repetitive anomalies will be resolved automatically, freeing engineers to focus on higher-value work.

‍

When to Use Thresholds and When to Use ML

Thresholds still have a place:

• In small, simple cloud setups.
• When basic, rough alerts are enough.
• For teams just beginning their FinOps journey.

But as your cloud footprint grows ML becomes essential.
It’s the only way to keep pace, stay accurate, and minimize wasted time and money.

Many organizations succeed with hybrid models.
They use thresholds for simple alerts and ML for complex, high-impact monitoring.

‍

The Future of Anomaly Detection: A Closed Loop System

Traditional monitoring is about sounding alarms.
Machine learning is about knowing which alarms matter—and acting on them fast.

The real FinOps goal isn’t just detecting anomalies—it’s resolving them with speed, confidence, and minimal friction.

Machine learning enables this future.
It delivers better alerts, faster prioritization, and more efficient resolution.

The future of anomaly detection is not more noise—it’s smarter, actionable insights that close the loop between detection and action.
And when that loop is tight, the opportunity cost of anomalies fades.