How to Optimize AWS Storage Costs Using Tiering & Lifecycle Policies

Setting up S3 lifecycle policies is one of the simplest ways to reduce AWS storage costs without changing how your applications store or retrieve data. The goal is to automate the movement of data across storage classes – and eventually delete or archive what’s no longer needed.

Below is a practical, step-by-step guide to creating lifecycle rules that consistently save money.

Step 1: Identify Hot, Warm, Cold, and Archived Data

Before writing any policy, map your data based on access needs:

• Hot data: Frequently accessed (keep in S3 Standard)
  
  
• Warm data: Periodically accessed (move to Standard-IA)
  
  
• Cold data: Rarely accessed (move to Glacier tiers)
  
  
• Archived data: Compliance/long-term retention (Deep Archive)

This classification can be done using:

• S3 Storage Lens reports
  
  
• Access logs
  
  
• Application insights
  
  
• Last-accessed metadata (if available)

Step 2: Choose Transition Timelines

AWS best practices recommend:

• 30–60 days → Standard-IA for warm data
  
  
• 90–180 days → Glacier Flexible Retrieval for cold data
  
  
• 365+ days → Deep Archive for compliance retention

Your exact numbers depend on regulatory requirements and application needs.

Step 3: Apply Lifecycle Rules by Prefix or Tag

You can scope rules to:

Prefix examples

• logs/
  
  
• images/2023/
  
  
• db_backups/

Tag examples

• {“retention”:”short”}
  
  
• {“project”:”analytics”}

Tags are recommended for large buckets with mixed workloads.

Step 4: Configure Noncurrent Version and Expiration Rules

If bucket versioning is enabled:

• Transition noncurrent versions to cheaper tiers
  
  
• Set expiration for very old versions
  
  
• Delete orphaned delete markers

“Ghost versions” can sometimes represent 30–40% of total S3 usage, so expiration rules matter.

Step 5: Test the Lifecycle Policy in a Controlled Environment

Before applying lifecycle rules in production:

• Create a test bucket with similar folder structure
  
  
• Apply the lifecycle policy
  
  
• Observe transitions for a few days
  
  
• Validate no application workflows break
  
  
• Confirm that retention meets compliance needs

Step 6: Monitor with Storage Lens and AWS Cost Explorer

After deployment:

• Use S3 Storage Lens to track object counts by class
  
  
• Use AWS Cost Explorer to verify decreasing S3 Standard usage
  
  
• Use AWS Budgets to set alerts for unexpected data spikes

Lifecycle policies are “set once and forget” – but monitoring ensures they continue to work correctly as datasets grow.

Sample S3 Lifecycle Policy (JSON Template)

Here’s a clean example you can reuse:

{

  “Rules”: [

    {

      “ID”: “transition-logs”,

      “Status”: “Enabled”,

      “Filter”: { “Prefix”: “logs/” },

      “Transitions”: [

        { “Days”: 30, “StorageClass”: “STANDARD_IA” },

        { “Days”: 180, “StorageClass”: “GLACIER” }

      ],

      “Expiration”: { “Days”: 1095 }

    }

  ]

}

This policy:

• Moves logs to Standard-IA at 30 days
  
  
• Moves them to Glacier at 180 days
  
  
• Deletes them after 3 years