Rightsizing Automation

Automate the rightsizing.

Inputs to rightsizing

Rightsizing automation is only as good as the signals it gets. Bad inputs produce confidently wrong recommendations: undersized instances, broken latency SLOs, or no recommendation at all because the data was missing.

Utilisation per resource. CPU, memory, and IO history over multiple weeks; one busy day skews recommendations and misses steady-state usage.
Cost per resource. Instance class price, EBS cost, RDS class price pulled from billing; without cost the savings number is a guess.
Workload characteristics. Bursty, steady, or batch each need different rightsizing approaches; one-size-fits-all recommendations break workloads at the edges.
SLO per resource. Latency or throughput target on the workload; protects against over-aggressive downsizing that hits the SLO floor.

Recommendation engines vary widely in aggression. Pick the one that matches your risk appetite, your cloud surface, and how much you trust the engine to be right at scale.

AWS Compute Optimizer. Free per-account recommendations; conservative defaults make it a good baseline before reaching for paid tooling.
Vendor tools. Vantage, Cloudability, Kubecost; multi-cloud, richer reporting, more aggressive recommendations and better cost-attribution UX.
Custom scripts. Bespoke scripts against metrics and billing APIs; right when your workload pattern does not match the standard rightsizing model.
Confidence score per recommendation. Use the engine's confidence indicator to gate auto-apply versus human review; high-confidence safe changes can ship without ceremony.

Apply with safety

Applying recommendations is the high-risk step. Review, stage, and rollback are the controls; skipping any of them turns rightsizing into theatre or worse, an outage.

Engineer review. Named reviewer per recommendation; auto-apply for low-risk changes (gp2 to gp3, identical-class downsizing), manual approval for class changes.
Staged rollout. Non-prod first; observe latency, error rate, and throughput before promoting to production.
Rollback ready. Document the original size before applying; reverse the change immediately if performance degrades.
Canary window per change. 24-72 hour observation after apply; latent regressions surface in the second day, not the first.

Recurring rightsizing

Rightsizing is not a one-shot exercise. Quarterly, monthly, and annual cadences catch different classes of drift; layering them is how programs stay healthy.

Quarterly full audit. Workloads grow and shrink; the periodic check catches the slow drift that shows up between major review points.
Monthly recent-change review. Did rightsized resources still meet performance targets last month? If not, undo and investigate.
Annual deep review. New cloud instance types come out; old ones retire; the annual review captures the instance-class evolution.
Named owner per cadence. Responsible team for each review; without an owner, recurring reviews stop recurring after the second cycle.

Outcome metrics

Outcome metrics are how you prove the program is working. Without them, rightsizing is theatre that finance eventually stops funding.

Cost reduction per quarter. Savings number with a trend line; the absolute number matters less than direction.
Performance metric stability. Latency and throughput SLO trends; rightsizing should not regress them, and the metric proves it.
Engineering time per dollar saved. Program ROI; the math should justify continued automation investment over engineer hours.
Savings attribution per team. Credit the team that owns the workload; engineering buy-in tracks attribution more than aggregate numbers.