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Cost Optimization

Fix Overprovisioned Cloud Instances and Right-Size Resources

Right-size overprovisioned cloud instances to match actual workload needs and reduce unnecessary compute spending.

overprovisioned instances fix
right-size cloud instances
cloud instance optimization
reduce compute costs
Fix Confidence
98%

High confidence · Based on pattern matching and system analysis

Root Cause
What's happening

Cloud instances are provisioned with significantly more CPU and memory than workloads require.

Why it happens

Teams default to large instance types as a safety margin, and sizing is rarely revisited after initial deployment.

Explanation

Overprovisioning is the most common cloud cost waste. Teams select instance types based on peak-load assumptions or copy configurations from other services without profiling actual resource utilization. Instances running at 10-20% CPU utilization represent 80% wasted spend on compute.

Fix Plan
How to fix it
  1. 1.Analyze CPU and memory utilization metrics over the past 30 days to establish actual requirements
  2. 2.Downsize instances to the smallest type that sustains P95 utilization with comfortable headroom
  3. 3.Use auto-scaling groups to dynamically adjust capacity based on real-time demand
  4. 4.Consider burstable instance types (e.g., T3, T4g) for workloads with variable utilization patterns
  5. 5.Use cloud provider recommendation tools like AWS Compute Optimizer or GCP machine type recommendations
Action Plan
1 action
0 of 1 step completed0%

Right-size compute

Match instance types to actual utilisation to cut waste.

# AWS — get utilization recommendations
aws compute-optimizer get-ec2-instance-recommendations

# Kubernetes — check resource requests vs actual
kubectl top pods --containers

Always test changes in a safe environment before applying to production.

Prevention
How to prevent it
  • •Require utilization data review before provisioning new instances
  • •Schedule quarterly right-sizing reviews as part of FinOps practices
  • •Implement auto-scaling by default for all non-static workloads
Control Panel
Perception Engine
98%

Confidence

High (98%)

Pattern match strengthStrong
Input clarityClear
Known issue patternsMatched

Impact

Medium

Est. Improvement

-30% cost reduction

cloud spend

Detected Signals

  • Spending anomaly pattern
  • Resource utilization imbalance
  • Billing threshold indicators

Detected System

Infrastructure / Cloud

Classification based on input keywords, error patterns, and diagnostic signals.

Agent Mode
Agent Mode

Enable Agent Mode to start continuous monitoring and auto-analysis.

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Frequently Asked Questions

How do I know if my instances are overprovisioned?

Check average and peak CPU/memory utilization. If average utilization is consistently below 30%, the instance is likely overprovisioned and can be downsized.

Will right-sizing cause performance issues?

Not if done with data. Size based on P95 utilization plus a safety margin. Use auto-scaling to handle unexpected peaks.

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