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How to Optimize Costs in Amazon RDS; Part 2

In this blog, we have continued the key steps of How to optimize costs in Amazon RDS; Part 1. Let’s get started with it.

· · 2 min read

How to optimize costs in Amazon RDS; Part 2

In the current scenario, the ability to control expenses without compromising the performance of mission-critical applications is one of the largest difficulties faced by any IT team. Users using RDS pay only for the services they actually use, that is the resources, just like any other AWS tool. As a result, it is important to regularly review the design to ensure that the resources are operating at their best and that we are getting the most out of our spending. In this blog, we have continued the key steps of How to optimize costs in Amazon RDS; Part 1. Let’s get started with it.

Key steps for the cost optimization in AWS RDS; Part 2

Setting up the Amazon RDS resource’s use guidelines

Stakeholders create policies to identify underutilized RDS instances in this stage, as well as the procedures to follow after they have been located. These guidelines address three essential Amazon RDS facets:

  • Explore replicas
  • Non-utilized occurrences
  • First-instances

For each policy, suggestions are given in the following sections.

A. View the replica policy

Improved scalability and durability are offered by Amazon RDS read replicas for RDS DB instances. The capacity to scale read traffic horizontally is offered by these read replicas. In particular, read-intensive database workloads benefit from this.

B. Policy on Non -utilized occurrences

RDS instances that are not used increase total costs without adding any benefit. It is advised that any unnecessary instances be identified and terminated in accordance with the company policy. In non-production settings, instances are occasionally set up for fast testing and are never taken down after the task is over. These underused instances continue to cost money since they are not utilized.

C. First-instances principle

Read and write traffic for your application is handled by the main RDS instance. Consequently, it must be properly scaled to satisfy the needs of your application. On the other hand, you don’t want to keep it unused. Look for primary instances with steady CPU consumption, less than 30% and I/O less than 30% to spot underutilized primary instances.

Understanding and improving policies and processes

Following the implementation of this procedure, it is critical to continue monitoring and identifying areas for improvement. This ensures that rules and processes are always evolving to meet your workloads and organizational demands. During this stage, you may want to think about building automation to auto-scale and right-size the instances in your fleet as your workload changes. Changing the instance type causes downtime, thus it’s critical to prepare ahead of time so that it has no or little impact on your workload.

Conclusion

Cost optimization is a continuous process, which is crucial. Instead, it is an ongoing process, and the sooner we implement it, the more effective our databases will be. If you still have any confusion or queries related to Amazon RDS, feel free to contact our team of experts at Princeton IT services.

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Case studies

Cloud Consulting in practice

Client names are withheld. We are happy to share details on a call.

All case studies
  • Delivered

    On-prem → Amazon RDS

    Nonprofit

    Cloud Consulting

    Oracle from on-premises to Amazon RDS for a national nonprofit

    The nonprofit's on-premises Oracle database had become hard to maintain and scale. We planned the move with automated workflows and validation tests, migrated to Amazon RDS for Oracle with minimal disruption and left a repeatable process for future changes.

    • Automated, tested migration
    • Less day-to-day database upkeep
  • Delivered

    600k files, < 3 days

    Document management

    Microsoft

    600,000 documents moved from ImageRight to SharePoint in under three days

    A one-file-at-a-time script would have needed about 22 days of continuous running. We rebuilt the migration around a SQL work queue with parallel uploads, automatic retries and crash recovery, and it finished unattended in under three days.

    • 1,125 → 8,314 files per hour
    • Under 3 days instead of about 22
  • Delivered

    Every change, flagged

    Food manufacturing

    AI Consulting

    Label comparison agent for a national food cooperative

    Compares two versions of a product label, highlights every change, annotates the PDF and returns an audit-ready list of differences. We moved it from Copilot Studio to Azure Functions when the document processing outgrew the low-code platform.

    • Audit-ready differences in one chat turn
    • Rebuilt from low-code to cloud functions

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