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AWS Recommendations​

Enablement Steps for New AWS EC2 Passthrough Recommendations​

To use EC2 Passthrough Recommendations, ensure the following services are enabled in all AWS accounts where you want to view recommendations:

These services are free of cost and may take up to 24 hours after enablement to start generating recommendations.

Additional IAM Permissions​

You will also need to update the connector IAM role in the customer's AWS account with the following permissions:

cost-optimization-hub:GetRecommendation
cost-optimization-hub:ListRecommendations
ec2:DescribeInstanceTypes
compute-optimizer:GetEC2InstanceRecommendations
cost-optimization-hub:GetPreferences
compute-optimizer:GetRecommendationPreferences
compute-optimizer:PutRecommendationPreferences
compute-optimizer:GetEBSVolumeRecommendations
compute-optimizer:GetRDSDatabaseRecommendations
compute-optimizer:GetLambdaFunctionRecommendations
lambda:GetFunction
redshift:DescribeClusters

Before You Begin​

  • Ensure that you have enabled the Resource Inventory Management feature enabled when creating a cloud connector.
  • You must add the required permissions and enable EC2 recommendations in AWS. If you are an existing customer, you need to:
    • Add ce:GetRightsizingRecommendation permission to the HarnessEventsMonitoringPolicy in your AWS Connector's IAM role
    • In AWS Cost Explorer > Preferences, enable "Receive Amazon EC2 resource recommendations" and "Recommendations for linked accounts"
  • Enable CPU and Memory Metrics:
    • CPU Metrics: CloudWatch has to be enabled for the AWS account.
    • Memory Metrics: CloudWatch agent has to be installed on every EC2 instance.
info

You can use the external metrics ingestion feature in AWS to configure the AWS Compute Optimizer to ingest EC2 memory utilization metrics from observability products like Datadog, Dynatrace, Instana, and New Relic. In this case, EC2 recommendation will be generated by taking account of Memory metrics as well along with CPU metrics.

Types of EC2 Recommendations​

1. Rightsize​

Icon: Full-screen-exit
Purpose: Optimize resource allocation by resizing to a more appropriate instance type or storage size

When to Expect:

  • Your resource is over-provisioned (CPU/Memory utilization < 40%)
  • You're paying for capacity you don't use
  • A smaller instance type can handle your workload

Example Scenarios:

  • EC2 instance running at 15% CPU utilization → Downsize from m5.xlarge to m5.large
  • EBS volume with 200GB allocated but only 50GB used → Resize to 100GB
  • RDS instance with excessive memory → Downsize to appropriate db instance class

What to Expect:

  • Monthly Savings: Typically 30-50% cost reduction
  • Implementation Effort: Low to Medium
  • Restart Needed: Usually Yes (brief downtime)
  • Rollback Possible: Yes (can resize back)

2. Stop​

Icon: Banned
Purpose: Stop or terminate idle or underutilized resources that are not actively being used

When to Expect:

  • Resource has been idle for extended periods (7+ days)
  • Zero or minimal traffic/usage detected
  • Non-production environments running 24/7

Example Scenarios:

  • Development EC2 instance running overnight and weekends with no activity
  • Test database that hasn't been accessed in 2 weeks
  • Staging environment left running after project completion

What to Expect:

  • Monthly Savings: Up to 100% of resource cost
  • Implementation Effort: Low
  • Restart Needed: N/A (resource will be stopped)
  • Rollback Possible: Yes (can restart anytime)

Important: This is a "terminated action" - the resource will be stopped. Ensure you don't need the resource before applying this recommendation.

3. Upgrade​

Icon: Upgrade-custom
Purpose: Upgrade to newer generation instances or storage types for better price-performance

When to Expect:

  • You're using older generation instances (e.g., m4, t2)
  • Newer generations offer better performance at same or lower cost
  • Your workload can benefit from latest AWS features

Example Scenarios:

  • Upgrade from t2.medium to t3.medium (same price, better performance)
  • Upgrade from m4.large to m5.large (20% better performance, 10% lower cost)
  • Migrate EBS from gp2 to gp3 volumes (20% cost savings with same performance)

What to Expect:

  • Monthly Savings: 10-30% cost reduction
  • Performance Improvement: 10-40% better performance
  • Implementation Effort: Low to Medium
  • Restart Needed: Usually Yes
  • Rollback Possible: Yes

4. MigrateToGraviton​

Icon: CPU
Purpose: Migrate to AWS Graviton-based ARM processors for superior price-performance

When to Expect:

  • Your workload is compatible with ARM architecture
  • You're running general-purpose or compute-optimized workloads
  • Your application supports ARM64 architecture

Example Scenarios:

  • Migrate from m5.xlarge (x86) to m6g.xlarge (Graviton2) - 20% cost savings
  • Move web servers to Graviton instances for better efficiency
  • Containerized workloads that can run on ARM

What to Expect:

  • Monthly Savings: 20-40% cost reduction
  • Performance: Up to 40% better price-performance
  • Implementation Effort: Medium to High
  • Restart Needed: Yes
  • Rollback Possible: Yes (but requires architecture change)

Important Considerations:

  • Requires ARM64-compatible applications and dependencies
  • May need code recompilation or Docker image updates
  • Test thoroughly before production migration

5. Delete​

Icon: Code-delete
Purpose: Permanently delete unused or orphaned resources

When to Expect:

  • Resource is completely unused and abandoned
  • Orphaned volumes not attached to any instance
  • Snapshots or backups no longer needed
  • Resources from deleted/terminated projects

Example Scenarios:

  • Unattached EBS volumes from terminated EC2 instances
  • Old RDS snapshots beyond retention policy
  • Unused Elastic IPs
  • Abandoned test resources

What to Expect:

  • Monthly Savings: 100% of resource cost
  • Implementation Effort: Low
  • Restart Needed: N/A
  • Rollback Possible: No (permanent deletion)

⚠️ Critical Warning: This is a permanent, destructive action. Ensure you have backups and no longer need the resource before deleting.

How are EC2 Recommendations computed?​

Harness CCM provides EC2 recommendations by integrating directly with AWS services:

  • Data Source: Recommendations are fetched directly from AWS Cost Explorer APIs rather than being computed natively by Harness. These recommendations do not factor in Reserved Instance (RI) or Savings Plan (SP) discounts you may already have
  • Analysis Method: AWS analyzes historical CPU and memory utilization patterns of your EC2 instances to generate optimization suggestions
  • Savings Calculations: Based on AWS EC2 Recommendations methodology, which compares current instance costs against recommended instance costs. For more details, see Savings Calculations

Recommendation Drilldown​

  • Potential Monthly Spend: Projected cost if recommendation is applied
  • Potential Monthly Savings: Expected monthly cost reduction
  • Configuration Comparison: A detailed table comparing current and recommended configurations for:
    • Instance Family: The category of EC2 instance types that share similar hardware configurations but vary in size.
    • CPU (vCPU): The number of virtual CPU cores allocated to the instance.
    • Memory (GiB): The amount of RAM available to the instance, measured in gibibytes.
    • CPU Utilization: The percentage of allocated compute capacity being used by your instance over the analyzed period.
    • Memory Utilization: The percentage of allocated memory being used by your instance.
    • Region: The AWS region where the EC2 instance is located.
    • Potential Monthly Cost: Calcuted on On-demand cost
  • EC2 Details: Resource metadata including any associated tags
  • CPU and Memory Utilization Graph: Visual representation of historical resource usage
  • Tune Recommendations: This section allows you to customize how EC2 recommendations are generated based on your specific requirements. A default preset has been applied to your account via recommendation references, which may result in some fields being disabled or set to custom values. The tuning parameter for EC2 recommendations is the instance family scope:
    • Within the same instance family: When selected, recommendations will only suggest instance types from the same family as your current instance (e.g., if you're using a t3 instance, only other t3 instances will be recommended).
    • Across instance families: When selected, recommendations can suggest any suitable instance type across all AWS instance families. These settings can be saved as presets for consistent application across your organization's recommendations. See Manage Presets