You Probably Don’t Know How Much You’re Overspending on Cloud

Most companies significantly underestimate how much they’re overspending in the cloud. Studies suggest that 20-40% of cloud spending is wasted, yet organizations often fail to recognize the full extent of inefficiencies. The root cause? Traditional monitoring and cost reporting tools are not designed to detect, quantify, or prioritize waste effectively.

Why Traditional Cost Monitoring Falls Short

Tracking cloud spend is not the same as understanding cloud efficiency. While cost monitoring tools show where your money went, they fail to highlight how it is evolving, what the total impact is and how you can proactively mitigate the risks. Even worse, traditional threshold-based anomaly detection generates too many false alerts or misses real waste entirely.

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The Need for Smarter Anomaly Detection

Cloud cost optimization is fundamentally about understanding patterns in time-series data. However, most anomaly detection tools are too simplistic. Traditional threshold-based approaches only detect basic cost spikes and exponential growth but fail to identify the seasonalities, trends and correlations resulting in more subtle inefficiencies.

The 20% of Anomaly Types Threshold-based Detection can Identify:

1. ‍Sudden Spikes: Your typical anomaly. A sharp, unexpected increase in cost compared to previous values.‍
2. Exponential Growth: Costs increasing at a growing rate, often unnoticed until too late. But, while basic systems can detect this they can usually not classify it as an exponentially growing cost allowing you to assess the future impact and prioritize an action. For this you need a more advanced monitoring strategy.

While these are useful detections, they provide only a fraction of the insights needed to know how cost-efficiency your cloud operations are and control cloud waste effectively.

Additional Anomaly Types AI-Driven Detection can Identify:

A self-learning, AI-powered system can dynamically model expected behavior and detect 8 additional anomaly types that thresholds typically overlook:

1. Broken Seasonal Pattern: Deviations from recurring trends, such as a missing expected peak in a workload.
2. Steady Growth: Cost increases that are gradual but sustained over time.
3. Sudden Drop: Unexpected cost reductions that may indicate a failure or misconfiguration.
4. Trend Shift: Shifts in long-term cost trends, helping teams react before inefficiencies escalate.
5. Seasonal Peak/Dip Missing: Cases where expected cyclical patterns don’t appear, indicating workload changes.
6. Steady Decline:  Workloads that are gradually becoming underutilized.
7. Volatile Jumps: Cases where cost fluctuates erratically, suggesting instability.
8. New Normal Identified: When a cost pattern fundamentally shifts to a new baseline.

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The Power of Self-Learning AI Monitoring

Unlike rules and guardrails, AI-driven anomaly detection continuously learns from your data including your cloud usage, filtering out predictable spikes and seasonal variations to surface and prioritize only meaningful cost deviations.

How This Transforms Cloud Cost Efficiency

• Less Noise, More Actionable Insights: Reduces alert fatigue by focusing on real cost inefficiencies.
• Proactive Waste Reduction: Flags inefficiencies before they spiral into excessive costs.
• Granular Cost Understanding: Analyzes and benchmarks inefficiencies across different dimensions (macro and micro).
• Automated Root Cause Analysis: Correlates cost anomalies across workloads for deeper insights.

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Why Most Companies Are Still Wasting Cloud Spend

Even with cloud cost tracking in place, many organizations still struggle to reduce waste due to several key challenges:

1. Lack of Oversight & Granular Cost Insights

• Simple cost reports tell you where you spent money but not why your costs are inefficient, how they are evolving and what you can do about this.
• Companies need efficiency benchmarking, not just spend analysis.

2. Misalignment Between Finance & Engineering

• Engineers focus on performance, while finance teams focus on cost. It’s up to the Cloud Center of Excellence (CCoE) to monitor cloud operations, but they usually lack a macro view and efficiency KPIs.
• Without an overarching cloud operations strategy and view, there is no clear ownership of cloud cost efficiency.

3. Basic Cost Anomaly Detection (Too Many Alerts, No Prioritization)

• Threshold-based tools generate alert fatigue, making it hard to identify real inefficiencies.‍
• AI-driven anomaly detection is necessary to filter out irrelevant spikes and highlight high-impact cost anomalies.

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The Reality: You’re Likely Wasting More Than You Think

Without proactive monitoring, AI-powered anomaly detection and efficiency benchmarking most organizations are only scratching the surface of their cloud overspend.

"Today’s monitoring tools only detect 20% of the most common anomaly types. Smarter anomaly detection is a must for a much better understanding of your cloud efficiency which is the only way towards real cost control."

Want to discover your real cloud performance?  Let’s run a cloud efficiency scan and start cutting cloud waste intelligently today!

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