Recommendations

Smart Optimization, Not Guesswork

AI-powered recommendations with confidence scores and safety analysis. Know exactly what to optimize and how much you'll save.

app.cloptima.ai/recommendations
Recommendations
Ranked by savings · with confidence and safety
Illustrative
Potential monthly savings
$9,420
across 14 actions
High-confidence
11
Low-risk to apply
9
Rightsize api-server (m5.2xl → m5.xl)
P95 CPU 31% over 14d · 92% confidence
-$2,180/moLow risk
Compute Savings Plan (1yr, no upfront)
steady baseline detected
-$3,640/moLow risk
Delete 6 unattached EBS volumes
idle 30+ days
-$410/moLow risk
Downsize prod-db replica
stateful · review impact
-$1,290/moHigh risk
Why it's hard

Recommendations only matter if you trust them enough to act.

Most cost tools generate a long list of suggestions that teams ignore — because acting on the wrong one breaks production. Cloptima scores every recommendation on confidence (from real utilization) and safety (risk to the workload), shows the evidence behind it, and flags high-risk changes like downsizing a stateful database. The easy wins become obvious; the risky ones are clearly marked.

  • Confidence scored from actual usage, not list prices
  • Safety analysis — low / medium / high risk per change
  • Evidence shown: current vs recommended, with projected savings
  • Rightsizing, commitments, idle cleanup, and storage tiering in one place

Rightsizing

Downsize overprovisioned instances based on actual CPU and memory utilization. Each recommendation shows current vs recommended size with monthly savings.

Reserved Instances & Savings Plans

Identify steady-state workloads that benefit from commitments. Cloptima recommends the optimal commitment level based on 30-90 day usage patterns.

Idle Resource Cleanup

Detect resources running at <10% utilization, unattached volumes, idle load balancers, and unused elastic IPs. Each flagged with estimated monthly waste.

Storage Optimization

Recommend S3 lifecycle policies, EBS volume type changes (gp2→gp3), and storage tiering based on access patterns.

Safety Analysis

Every recommendation includes a safety score: Low/Medium/High risk. High-risk changes (like downsizing production databases) are flagged with detailed impact analysis.

Confidence Scoring

AI confidence level (1-100%) based on data quality, observation period, and workload stability. Only high-confidence recommendations are surfaced by default.

FAQ

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