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Optimizing the Cost AWS Savings Plans and Reserved Instances

Optimizing AWS cost savings

AWS cost-savings opportunities can be complex

Reserved Instances (RI) and Savings Plans (SP) are the major resources available from AWS to minimize your cloud bill without requiring upgrades or changes to your infrastructure. Reserved Instances provide significant discounts as payback for committing to a specified usage level over a period of time. Savings Plans offer significant cost reductions for committing to on-demand Amazon Elastic Compute Cloud (EC2) and Fargate services for a one-or-three-year period of time.

Choosing the right pricing model requires insights on multiple considerations, advantages, and drawbacks. A major concern is the ability to assign usage accurately to diverse business teams for budgeting and forecasting.


  • Tedious manual validation devours engineering time and delivers debatable results
  • A confusing array of RI and SP options blurs the ability to optimize planning and forecasting
  • Pressures on engineering from executive leadership impede smooth collaboration


  • Collaboration capabilities enable teams to implement 5X more optimization recommendations.
  • Incorporating business-specific feedback creates a comprehensive decision log for revisiting as needed
  • Investing in commitment-based discounts spurs confident decision-making for higher margins and less risk

Understanding what you need starts with understanding what you have

Conventional tools require a combination of manual ad hoc querying and guesswork to find the set of resources that each team is likely responsible for. This deficit interferes with an engineering team’s ability to review and act on cost savings and discount plan recommendations. Further complicating the process, tools often retain non-actionable recommendations month-over-month and year-over-year, creating a cluttered and untrustworthy interface.

Despite using sophisticated algorithms, legacy cost-management tools have difficulty arriving at the optimal choice of RI or SP investment. Instead, teams fall back on a “best guess” strategy when deciding on a purchase. Even in organizations where somebody owns and curates the process, assigning costs and recommendations remains sub-optimal and infrequent.

This leads to an unwillingness to trust their data, causing engineering teams to miss out on potential savings and operate less cost-efficiently. Persistent inaccurate data also ramps up tension between executive leadership and engineering organizations, taking up engineering time to explain issues and a perceived failure to optimize cost management.

Yotascale lets you confidently make budgeting and forecast recommendations, including Reserved Instance and Savings Plan considerations.

Yotascale maps the route to optimizing AWS cloud costs

Deciding which option is best for your organization may be complex, but Yotascale machine learning does the heavy work for you. Our AI automatically assesses and recommends the best way for you to save, and sends that information to the people who need to know, so you can implement these changes and scale back your rising cloud costs even as your company scales up.

Yotascale is able to deliver personalized recommendations to each engineering team—we know exactly who needs to see which recommendations because we know who is using the associated resources. Teams can connect a Slack channel to receive a weekly update on savings opportunities, so there’s no man-in-the-middle and the teams don’t have to do any work besides examine how they can save money and decide whether to take action.

Validated savings recommendations reach the right teams

Using Yotascale, it’s easy to establish sanity checks for the engineering teams who own the resources. This enables a fast, distributed feedback system from the edge of your business, where the subject matter experts for your resources live—resulting in the most accurate savings recommendations possible for your business. When completed, each validated recommendation shows up in a queue for the purchaser—often someone in Finance—to make final selections on upfront payment preferences and complete the transaction.

Now teams have the ability to respond to and suppress non-actionable recommendations, providing a clear and consistent picture of what can be done to improve their cloud cost. Yotascale identifies owners of orphaned resources so a business can decide if those resources are still needed as their costs add up, ensuring maximum resource optimization and use for minimum financial lock-in.

With Yotascale, there is no need to export reports for tedious hit-or-miss analysis. Savings opportunities automatically speed to the teams most qualified to take action, clarifying and verifying the right decisions and supporting confident collaboration.