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Cost Optimization

Updated Apr 30, 2020 ·

Overview​

Using cost optimization strategies can greatly reduce cloud expenses. Below are the main ways to achieve savings.

  • Use resources correctly
  • Scale capacity based on demand
  • Explore pricing models and alternatives

With the right approach, costs can go down without losing performance.

Cost Optimization Framework​

There are five main steps to optimize cloud spending.

  • Right-size resources for the job
  • Scale elastically to match demand
  • Use pricing models like Reserved or Spot
  • Replace with lower-cost services where possible
  • Remove unused storage to cut costs

These steps ensure resources are efficient and expenses stay under control.

Sample Scenario​

Consider the example below:

A company runs 12 servers, a database, and storage for about $15,000 each month.

  • Four servers for web requests
  • Four for image processing
  • Four for analytics tasks

Each set was sized for peak demand, which leaves room for optimization.

Optimize size and capacity​

Web servers do not all need to run 24/7.

  • One server must stay on at all times
  • Extra servers are only needed during traffic spikes
  • Usage shows only 24 server-hours are required for surges

With auto scaling, the company can save nearly half of its server time.

aws autoscaling create-auto-scaling-group \
  --auto-scaling-group-name web-scaling-group \
  --launch-configuration-name web-launch \
  --min-size 1 \
  --max-size 4

Expected result: One server always runs, and more start only when traffic increases.

Leverage pricing model​

On the other hand, analytics servers run constantly but can tolerate interruptions.

  • On-demand costs about $4,000/month
  • Spot instances offer 66% lower prices
  • Moving to spot saves up to 94%

To optimize: Switch to Spot instances. This keeps analytics running while reducing cost.

Explore alternatives​

Some workloads run better on different services.

  • AWS Lambda runs short tasks without servers
  • Great for image processing or uploads
  • One million free requests per month

Replacing 4 EC2 servers with Lambda can save $4,000 monthly while still processing tasks reliably.

Optimize storage​

Storage costs increase if unused files are left behind.

  • Lifecycle policies can purge old data
  • Auto-purge rules save time
  • Cuts $500/month from costs

Using lifecycle rules keeps storage lean and prevents wasted expenses.

aws s3api put-bucket-lifecycle-configuration \
  --bucket mybucket \
  --lifecycle-configuration '{
    "Rules": [{
      "ID": "ExpireOldFiles",
      "Prefix": "",
      "Status": "Enabled",
      "Expiration": {"Days": 30}
    }]
  }'

Expected result: Files older than 30 days are automatically deleted.

Savings Summary​

By applying all strategies, costs drop from $15,000 to about $4,722 each month.

  • Elastic capacity saved server hours
  • Spot pricing cut analytics costs
  • Lambda replaced EC2 for short tasks
  • Storage policies reduced waste

The savings show how cost optimization can transform cloud spending when applied to real workloads.