Why AI Alone Isn’t Enough for AWS Cost Optimization

 Are you considering using artificial intelligence (AI) and machine learning (ML) for AWS cost optimization? While AI can be a powerful tool for this complex task, we believe the best approach currently involves a combination of human expertise and automation. In fact, relying solely on AI can increase financial risk. Here's why.

The limitations of relying solely on AI and ML for AWS cost optimization

While the capabilities of AI models are impressive, it is still important to have human oversight when it comes to AWS cost optimization. There are many levers that can be used to control the average price paid for AWS usage, including turning off unused resources, resizing oversized resources, committing to future usage, committing to future spend, and making prepayments. While AI can effectively address some of these cost optimization factors in AWS, it requires meticulous attention to optimize all of them. Specialized startups have emerged to solve these challenges, and at Strategic Blue, we are experts in AWS price & commitment optimization. We encourage our customers to also pay attention to the other cost optimization levers, and offer guidance on which cloud financial management tools can be used unsupervised, and which ones need attention.

The importance of a blended approach to AWS cost optimization

At Strategic Blue, we use our proprietary technology behind the scenes to automate many of the activities that AWS customers typically adopt to keep track of reserved instances, savings plans, and any private pricing arrangements they have in place. However, we have found that human expertise in combination with automation leads to deeper savings while retaining greater flexibility and less financial risk.

AI and ML forecasting models can be trained with historical data to predict future usage patterns, identify areas for cost optimization and make automated decisions based on this data. However, features of some commitment products mean that commitment decisions cannot be reversed, leading to potentially adverse financial consequences, and this is a significant reason why the human element is important in cost optimization. For example, an ML model might indicate that a Savings Plan would be a good way to commit 99% of historical usage for the next 3 years, but it would be unlikely to identify that there is a scheduled step down in cloud usage by 20% in 3 months time because you've made a business decision to decommission or migrate something.

This is the kind of thing that regular conversations with technical teams would identify. Once committed, it is not possible to sell back Savings Plans - leaving the cloud user with significant wastage for the rest of the Savings Plan term. 

While both AI and ML can be powerful tools in your arsenal, it is therefore important to allow human oversight as operational decisions and changes are made by people based on their business needs now and so cloud forecasting, hedging strategy formulation and commitment buying tools need more than just history. This ensures the most accurate decisions are made.

In summary, while AI and ML can certainly assist with AWS cost optimization, they should only ever be viewed as part of the answer. Any approach that relies purely on the past to guide future action introduces lags between decision-making and action and fails to account for known organisational changes. At Strategic Blue, we have found that a blend of automation and human experience provides deeper savings and greater flexibility, resulting in less financial and technical risk. Our proprietary technology allows our team to apply insights from relationships with our customers to align their business strategy with the cost optimization strategy we tailor to them.  Interested in optimizing your AWS costs? Enquire now to learn more about our AWS cost optimization services and how we can help you achieve deeper savings with less financial risk.