Cost Optimization

5 Ways to Detect Cloud Waste Without Enterprise Tools

A beginner-friendly guide to spotting wasteful cloud spending using free, native tools

By CloudCostChefs Team | Published: 6/7/2025
AWSAzureOCICost OptimizationFinOps

The Reality Check: Your Cloud Bill Might Be 30% Fluff

Imagine ordering a pizza for 10 people but only 7 show up. You still pay for the whole pizza, but 3 slices go to waste. That is exactly what is happening in your cloud environment every single day. Studies show that 30% of cloud spending is pure waste - money thrown at resources that nobody is actually using.

The good news? You do not need expensive enterprise tools to become a cloud waste detective. Your cloud provider gives you everything you need to start hunting down those budget vampires today.

1Method 1: The "Ghost Instance" Hunt

What you're looking for: Virtual machines that are running but doing absolutely nothing – like a car engine idling in an empty parking lot 🅿️🚗💨

AWS (CloudWatch + Console)

  1. Open AWS CloudWatch (it's free!)
  2. Look for instances with CPU utilization under 5% for the past 2 weeks
  3. Check network activity – if it's flatlining, you've found a ghost 👻

Azure (Monitor + Metrics Explorer)

  1. Go to Azure Monitor → select Virtual Machines
  2. Use Metrics Explorer to filter by % CPU and Network In/Out
  3. Identify any VMs with under 5% CPU + no network traffic over 14 days
  4. Bonus: Check the Activity Log – no events = very suspicious 👀

OCI (Metrics + Logging)

  1. Head to OCI Monitoring → Metrics Explorer
  2. Select Compute > CpuUtilization and NetworkBytesIn/Out
  3. Filter for instances below 5% CPU and no traffic over 2 weeks
  4. Cross-check with OCI Logging to confirm inactivity 🕵️‍♂️

The Analogy:

Think of your cloud instances like employees. If someone shows up to work every day but never touches their computer, never answers emails, and produces zero output - that's your idle instance.

Real Example:

"We found 47 EC2 instances that had been running for 6 months with CPU usage averaging 0.2%. Turning them off saved us $3,400 per month." - DevOps Engineer at a mid-size startup

Quick Action Steps:

  • Tag instances showing consistent low utilization
  • Contact instance owners (if tagged properly)
  • Schedule shutdown for test instances outside business hours
  • Consider instance rightsizing for consistently underutilized production resources

2Method 2: The "Digital Hoarding" Investigation

What you're looking for: Old snapshots, unattached volumes, and forgotten backups - the digital equivalent of keeping every email you've ever received.

AWS (EBS, Snapshots, S3)

  1. Navigate to EC2 → Elastic Block Store → Volumes
  2. Filter by "available" (these are unattached and costing money)
  3. Check Snapshots section for ancient backups
  4. Review S3 buckets for data that never gets accessed

Azure (Managed Disks, Snapshots, Blob Storage)

  1. Go to Azure Portal → Disks and filter by "Unattached"
  2. Check Snapshots in the Backup center or Recovery Services Vaults
  3. Navigate to Storage Accounts → Containers
    • Use Access Tiers to find hot vs. cool vs. archive data
    • Consider lifecycle rules to clean up or archive unused blobs

OCI (Block Volumes, Backups, Object Storage)

  1. Head to Block Storage → Block Volumes
    • Filter by "Available" to find unattached volumes
  2. Go to Block Volume Backups and Object Storage Backups
    • Look for old snapshots or stale copies
  3. Review Object Storage → Buckets
    • Use "Last Modified" metadata to identify cold data
    • Use Lifecycle Rules to transition to Archive Tier or delete

The Analogy:

This is like paying rent for a storage unit full of boxes you haven't opened in 3 years. Every month, you're charged for storing your great-aunt's china set that you'll never use.

The Math That'll Shock You:

  • Unattached 100GB EBS volume: $10/month doing nothing
  • 1TB of old snapshots: $50/month for "just in case" scenarios
  • S3 bucket with 5TB of forgotten logs: $115/month gathering digital dust

Quick Wins:

  • Delete unattached volumes after confirming with teams
  • Implement lifecycle policies for snapshots (auto-delete after 30-90 days)
  • Move infrequently accessed data to cheaper storage classes

3Method 3: The "Wrong Size Syndrome" Diagnosis

What you're looking for: Instances that are like wearing XXXL shirts when you're a size Medium - way more than you need.

AWS (Cost Explorer + CloudWatch)

  1. Use AWS Cost Explorer (free with your account)
  2. Enable "Right Sizing Recommendations"
  3. Look for consistent patterns of low resource utilization
  4. Check memory usage via CloudWatch agent (if installed)

Azure (Advisor + Monitor)

  1. Open Azure Advisor → Navigate to Cost Recommendations
  2. Review VM Right-sizing Suggestions (based on actual usage)
  3. Open Azure Monitor and check metrics like % CPU and Memory
    • Add Log Analytics Agent for detailed memory metrics
  4. Cross-check with Resource Health for unused or idle services

OCI (Recommendations + Monitoring)

  1. Go to Cost Analysis under Billing & Cost Management
  2. Use OCI Recommendations (under Governance & Administration)
    • Look for Compute instance downsizing suggestions
  3. Open Monitoring → Metrics Explorer
    • Check CPUUtilization and MemoryUtilization (if custom metrics are enabled)
  4. Review Auto-scaling policies – if they're never triggered, the instance may be oversized

The Analogy:

You bought a monster truck to commute to your office job. Sure, it works, but a Honda Civic would do the same job for 1/10th the fuel cost.

🧱 Common Oversizing Scenarios (AWS, Azure, OCI)

  • Web servers running on large VMs when smaller instance types would deliver the same performance
    (e.g., 8 vCPU when 2 vCPU is enough for average traffic)
  • Development databases using high-memory SKUs that are better suited for production workloads
    (e.g., compute-optimized or memory-intensive tiers that sit mostly idle in dev)
  • Test environments mirroring full production specs even though they don't handle real user load
    (often doubling cloud spend unnecessarily)
  • Idle workloads provisioned for peak capacity 24/7 instead of using auto-scaling or scheduling
    (especially for training, demos, or batch jobs)
  • "Just in case" instances left running at enterprise sizes long after the high-usage period ended

The Rightsizing Reality:

"Turns out we were running dev workloads on production-grade machines. A few tweaks later, we were saving five figures annually." — Cloud Engineer

4Method 4: The "24/7 Myth" Time Audit

What you're looking for: Resources running round-the-clock when they're only needed 40 hours a week - like leaving all the lights on in an empty office building.

AWS (CloudWatch + Tags + Scheduling)

  1. Review CloudWatch metrics for usage patterns
  2. Identify development/testing environments
  3. Look for predictable downtime patterns
  4. Check weekend and holiday utilization

🔵 Azure (Monitor + Resource Graph + Automation)

  1. Review metrics in Azure Monitor → focus on CPU, disk, and network activity
  2. Filter dev/test environments via Resource Graph queries or tags
  3. Identify low activity windows using Log Analytics workbooks
  4. Analyze usage during weekends and non-working hours
  5. Use Azure Automation or Logic Apps to schedule shutdowns

🟠 OCI (Monitoring + Tags + Scheduled Actions)

  1. Explore OCI Monitoring dashboards for CPU & network utilization
  2. Identify non-production instances using defined tags or compartment structure
  3. Look for recurring idle periods (nights, weekends, holidays)
  4. Use Custom Metrics or log data to confirm downtime
  5. Create Scheduled Start/Stop Actions using Resource Manager or Functions + Events

The Analogy:

You're paying for a gym membership that's open 24/7, but you only work out Monday through Friday, 9-5. Why pay for weekend and night access you never use?

Scheduling Opportunities:

  • Development environments: Only need to run during business hours
  • Training/demo systems: Can be stopped between sessions
  • Batch processing jobs: Often run for 2-4 hours but instances stay up 24/7
  • Analytics workloads: Might only run overnight

Automation Made Simple:

  • AWS Instance Scheduler: Free solution to automatically start/stop instances
  • Lambda functions: Create simple on/off schedules
  • Tag-based policies: Automatically manage resources by tags

Real Savings Example:

"By stopping our development environment outside of 9-5 weekdays, we cut our monthly bill by $4,200 - a 35% reduction with zero impact on productivity."

5Method 5: The "Pay-As-You-Go Premium" Audit

What you're looking for: Predictable workloads still paying premium "on-demand" prices - like buying individual concert tickets when season passes are 70% cheaper.

AWS (Cost Explorer + RI Coverage)

  1. Open AWS Cost Explorer
  2. Filter by "On-Demand" instances
  3. Look for instances running continuously for 30+ days
  4. Check your Reserved Instance coverage percentage

Azure (Cost Management + Advisor)

  1. Go to Azure Cost Management → Cost analysis
  2. Filter by Pay-as-you-go VMs or non-reserved compute spend
  3. Identify long-running VMs with consistent uptime
  4. Use Azure Advisor to check Reserved Instance recommendations
  5. Review Reservation Coverage in Azure Cost Management + Billing

🟠 OCI (Cost Analysis + Commitment Plans)

  1. Navigate to Billing & Cost Management → Cost Analysis
  2. Filter by Compute → Shape → VM.Standard or similar
  3. Look for instances running continuously for 30+ days
  4. Review Commitment Plan opportunities and monthly spend trends
  5. Use OCI Usage Reports or Budgets & Recommendations to assess cost efficiency

The Analogy:

You're staying in hotels every night for work travel instead of signing a monthly apartment lease. Hotels are convenient for occasional use, but if you're there every night, you're paying 3x more than necessary.

The Numbers Don't Lie:

  • On-Demand m5.large: $0.096/hour = $70.08/month
  • 1-year Reserved Instance: $0.057/hour = $41.61/month
  • 3-year Reserved Instance: $0.038/hour = $27.74/month
  • Savings: Up to 60% for the exact same performance

Coverage Sweet Spots:

  • Aim for 70-80% Reserved Instance coverage for production workloads
  • Leave 20-30% on-demand for scaling and flexibility
  • Consider Savings Plans for more flexibility across instance types

⚡ Quick Assessment: AWS, Azure, and OCI

Take a few minutes to check your cloud billing tools and ask yourself:

  • What percentage of your compute spend is on-demand or pay-as-you-go?
    (Hint: anything consistent should probably be reserved)
  • Which instances have been running continuously for 60+ days?
    (Long-running = great RI or commitment plan candidates)
  • Are you paying premium prices for predictable workloads?
    (Steady-state workloads are the easiest to optimize — and the most expensive if ignored)
Where to Look:
  • AWS: Cost Explorer → Savings Plans & RI Reports
  • Azure: Cost Management + Billing → Reservation Recommendations
  • OCI: Cost Analysis → Commitment Plan Suggestions + Usage Reports

🎯 Your 30-Day Waste Detection Challenge

Week 1: The Survey

  • Run through all 5 detection methods
  • Document findings in a spreadsheet
  • Estimate potential monthly savings

Week 2: The Low-Hanging Fruit

  • Delete obvious waste (unattached volumes, old snapshots)
  • Stop unused instances in development environments
  • Implement basic scheduling for non-production resources

Week 3: The Strategic Moves

  • Right-size oversized instances (start with development/test)
  • Purchase Reserved Instances for obvious candidates
  • Set up automated scheduling for development environments

Week 4: The Monitoring Setup

Create CloudWatch dashboards for ongoing monitoring
Set up budget alerts for cost anomalies
Document your new processes for the team

🚨 Red Flags That Scream "WASTE!"

Watch out for these telltale signs:

  1. Cloud bill increasing but traffic staying flat
  2. Hundreds of untagged resources (nobody knows what they do)
  3. Development environments that cost more than production
  4. Zero Reserved Instance coverage with predictable workloads
  5. Storage growing faster than your actual data needs

The Bottom Line: Start Small, Save Big

You don't need a PhD in cloud architecture or expensive tools to start saving money. Begin with just one method from this list, spend 2 hours this week investigating, and you'll likely find enough waste to pay for a nice dinner.

Remember: Cloud waste isn't a one-time problem you solve - it's an ongoing challenge that requires regular attention. But with these simple detection methods, you can become your organization's cloud waste detective, armed with nothing more than curiosity and your cloud provider's free tools.

Next step: Pick one method above and spend 30 minutes investigating right now. Your future self (and your budget) will thank you.

💡Pro Tip: Start a "Waste Wednesday" tradition where you spend 30 minutes each week hunting for cloud waste. Make it a team effort and celebrate the savings you find!

Cloud Cost OptimizationFinOpsAWSAzureOCICost Savings