AI USE CASE
ML-Driven Infrastructure Capacity Planning
Predict resource utilisation trends and automate scaling decisions to cut infrastructure waste.
What it is
Machine learning models analyse historical CPU, memory, storage, and network utilisation patterns to forecast demand up to weeks ahead. Automated scaling rules act on these predictions, reducing both over-provisioning and unexpected outages.
Data you need
At least 6–12 months of time-series infrastructure metrics (CPU, memory, storage, network I/O) at 5–15 minute granularity, ideally tagged by service or environment.
Required systems
- data warehouse
Why it works
- Centralise metrics collection into a single observability platform before training models.
- Start with read-only recommendations and validate accuracy for 4–6 weeks before enabling auto-scaling.
- Define cost and capacity guardrails (min/max bounds) that override model decisions automatically.
- Involve FinOps and platform engineering teams jointly in threshold and alert configuration.
How this goes wrong
- Insufficient historical metrics granularity leads to inaccurate forecasts and false scaling triggers.
- Overfitting to seasonal patterns causes poor generalisation during traffic anomalies or product launches.
- Scaling automation runs without human guardrails, causing runaway cloud spend during model errors.
- Siloed infrastructure teams do not integrate the tool with all workloads, leaving blind spots.
When NOT to do this
Do not implement automated capacity scaling if your infrastructure metrics are collected at intervals longer than 15 minutes or cover less than six months of history, predictions will be unreliable and may trigger costly scaling events.
Example vendors
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