Background
When RealTheory generates right-sizing recommendations, it analyzes time series metrics for CPU and memory usage from your Kubernetes workloads. Recommendation Confidence reflects how much observed data was available at the time the recommendation was made.
More available data results in higher confidence. Less available data results in lower confidence.
Why Confidence Matters
The Confidence level helps you interpret a recommendation with the right level of caution or trust. It’s a signal — not a verdict — that blends RealTheory's automated analysis with your operational insight.
- RealTheory brings the statistical view.
- You bring the workload knowledge.
Example 1: A Stable, Predictable Workload
Suppose RealTheory has only 2-3 days of data for a workload with steady, uniform usage, such as a background job with minimal variance.
Even if the confidence level is reported as Low or Medium (due to the smaller data sample), the recommendation might still be trustworthy, because the workload behavior is consistent.
Your knowledge of the workload adds valuable context to the reported confidence level.
Example 2: A Spikey, Variable Workload
Now consider a workload that runs a batch job or an on-demand API with bursty, unpredictable usage.
If RealTheory has only 24 hours of data, there's a good chance those spikes weren't captured.
The resulting recommendation might underestimate actual resource needs, and confidence will be Low — a signal that it's best to wait for more data before taking action.
How to Use Confidence Effectively
| Confidence Level | Meaning | Guidance |
|---|---|---|
| High | Sufficient data for reliable analysis | Safe to act |
| Medium | Some data is available, but not yet ideal | Generally reliable — verify against known workload patterns |
| Low | Limited data available | Use caution — consider waiting for more metrics. |
Best Practices
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Don’t ignore low confidence, but don’t fear it either — it's just a signal.
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Review workload behavior alongside confidence to make informed decisions.
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Use RealTheory recommendations iteratively — apply, observe, refine.