AWS, Cloud Computing

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Cost Optimization Techniques for Running SAP Workloads on AWS


For many firms, cost optimization is a top goal in the present economic environment. Optimizing the cost of operating SAP workloads can free up resources that can be used for innovation when businesses upgrade their SAP deployments. Using best practices from SAP Lens for the AWS Well-Architected Framework, this article offers tips and tactics for maximizing SAP workload expenses and operations.

The top consumed services for SAP are typically Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Block Store (Amazon EBS), Amazon Simple Storage Service (Amazon S3), Amazon EBS snapshots and Amazon Elastic File Store (Amazon EFS). This blog focuses on optimization guidance for reducing costs and running SAP workloads optimally.

Amazon EC2

  1. Right-Sizing

The right sizing of an Amazon EC2 instance should be done by considering the business peaks and quarter closure time and the actual use over the previous three months. Use SAP Early Watch Reports with Amazon CloudWatch for utilization analysis to do this.

Turn on AWS Compute Optimizer’s improved infrastructure metrics functionality to leverage three months’ worth of Amazon CloudWatch data to produce suggestions. (By default, AWS Compute Optimizer creates suggestions by storing and utilizing up to 14 days of your Amazon CloudWatch metrics data.) To improve accuracy, cross-reference the suggestions with SAP Early Watch Alert reports. Replace the active instance with the appropriate SAP-certified NetWeaver or HANA instance based on the analysis.

  1. Leverage savings plans

In exchange for a one- or three-year hourly spend commitment, savings plans offer a flexible pricing mechanism that can help you cut your cost by up to 72% when compared to on-demand prices. If the workload and instance usage are consistent, see if any Amazon EC2 instances are operating on-demand and add them to savings plans. Your current savings plan use and recommendations can be obtained using AWS Cost Explorer and AWS Trusted Advisor.

  1. Automate Start & Stop of the Non-Production Amazon EC2 Instances

Purchasing a Savings Plan may not be cost-effective for development, training, sandbox, and project-related instances because they may only require a small amount of uptime (a few hours per day, or just on specific days) or have a temporary function in the project cycle. In this situation, scheduling the stopping and starting of the instances depending on the uptime needs can be done cost-effectively using AWS Systems Manager or SAP Landscape Management. You can reduce your AWS expenses by automating the start and shutdown of non-critical SAP workload instances. You can avoid paying for idle instances by only operating your instances when necessary.

  1. Optimize DR architecture with AWS Elastic Disaster Recovery based on Recovery Time Objective (RTO) and Recovery Point Objective (RPO)

Organizations can quickly and effectively establish a new disaster recovery plan or migrate an old one to AWS with the help of AWS Elastic Disaster Recovery (AWS DRS). Disaster recovery costs are minimized by leveraging shared servers, inexpensive storage, and low processing power to sustain continuous replication of the source systems to replication servers in a staging area.

If the necessary RTO and RPO can be met, use AWS DRS for Disaster Recovery (DR) rather than active-active for SAP Application servers and databases. To manage the resiliency of your SAP system on AWS, consider alternative design patterns if your RTO and RPO options differ.

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Amazon Elastic Block Storage

  1. Use latest Amazon EBS volume generation type.
  2. Use Amazon EBS GP3 volume type over IO2: If the IOPS utilization is under 16,000, consider migrating the volumes from IO2 to GP3. You can also consider striping GP3 volumes to bypass the 16,000 IOPS limitation and achieve higher IOPS and throughput.
  3. Delete unattached Amazon EBS volumes from terminated Amazon EC2 instances
  4. Backup database using AWS Backup or directly to AWS storage via AWS Backint Agent
  5. Optimize EBS snapshots for Non – Database volumes

Amazon Simple Storage Service

  1. Redefine database backup schedule and retention periods

Create a backup schedule for every environment that combines incremental and full backup kinds. Depending on what you have deployed, this should cover production, staging, pre-production, quality, development, sandbox, and project landscape. Considering the timeliness and recentness of the data needed for recovery, the backup schedule should be established on a retention period that corresponds with each environment’s recovery requirements.

For a sandbox environment with a 30-day retention period, for instance, a monthly full backup and weekly incremental backups ought to be adequate. These systems can be constructed with production/quality systems backups if necessary.

  1. Enable lifecycle policies in Amazon S3

To determine when to move the appropriate data to the appropriate storage class with a retention period (specified above), Amazon S3 analytics Storage Class Analysis is used to examine storage access patterns.

Create lifetime policies based on each environment’s recovery needs and retention period. For instance, if there is very little chance of restoring a backup older than five days, production backups could be kept in Amazon S3 Standard for five to seven days before being moved to a lower tier, like Amazon S3 Glacier or Amazon S3 Glacier Deep Archive with six months of retention.

  1. Lower-tier storage in the DR region

Use lower-tier storage in the disaster recovery (DR) region, such as Amazon S3 Glacier or Amazon S3 Glacier Deep Archive, depending on the recovery time objective (RTO) and recovery point objective (RPO), if Amazon S3 Cross-Region Replication (CRR) is enabled for business continuity and disaster recovery (BC/DR).

Amazon Elastic File System

  1. Check for unattached EFS
  2. Enable lifecycle policies


By implementing these cost optimization strategies, businesses can effectively manage and reduce the total cost of ownership (TCO) associated with running SAP workloads on AWS. By leveraging the scalability, flexibility, and cost-effective pricing models offered by AWS, organizations can achieve significant cost savings while benefiting from the performance, reliability, and security of the AWS cloud platform.

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1. What are the key benefits of running SAP workloads on AWS?

ANS: –

  • Enhanced scalability to accommodate fluctuating workloads.
  • Improved performance through AWS’s robust infrastructure.
  • Cost savings through optimized resource allocation and pay-as-you-go pricing.

2. How do organization in the financial sector benefit from Cost Optimization for Running SAP Workloads on AWS

ANS: – Banks, insurance companies, and other financial institutions use SAP for managing transactions, compliance, and risk management. Cost optimization on AWS enables these organizations to enhance their scalability, security, and resilience while optimizing costs associated with running SAP workloads.



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