Cloud Provider Native Pricing APIs, Cost Management & Machine Learning Tools

Introduction

Companies operating in the cloud face a constant challenge of trying to control cloud cost growth while maintaining optimal levels of operational performance for internal applications and customers. It is a balancing act between keeping costs down and providing the appropriate cloud resources to maintain peak performance, fuel growth, and ensure compliance and data security. One of the first way companies can improve their cost savings is to leverage native cloud provider APIs, cost management and machine learning tools in their FinOps forecasting processes.

Pricing APIs

To improve your forecasting and reduce your cloud spend across all your cloud providers you need to have a thorough understanding of your provider’s pricing. Cloud hosting firms have pricing sheets that will change from month to month and overtime, so it is best to store historical copy of these pricing sheets.

Capturing historical pricing data helps companies to better understand differences in pricing trends among hosting providers and how to optimize and re-allocate spend across providers. Utilizing a tool to analyze historical pricing data can be useful to explain cost variations and fluctuations for various time periods if usage levels are consistent. Company FinOps teams should leverage these APIs across all their cloud providers, even those that they are not using as actively as others. Companies have used this method to uncover significant savings by re-deploying spending from one provider to another when technically feasible. The big three cloud hosting providers offer the following pricing APIs (1):

Cost Management Tools

The big three cloud providers Azure, AWS and GCP provide cost management tools that have some native forecasting functionality. These tools do have limitations, some of which are intentional by the cloud providers. Many of these tools allow you to evaluate your cloud costs only in one dimension. If you want to evaluate your costs in a multi-dimensional manner, then you either need to develop your own tool or use one of the several third-party available software packages.

Cloud providers' cost analysis tools already provide some forecasting options (1):

• AWS Cost Explorer Forecasting: https://docs.aws.amazon.com/cost-management/latest/userguide/ce-forecast.html
• Azure Cost Management Forecasting: https://learn.microsoft.com/en-us/azure/cost-management-billing/costs/cost-analysis-common-uses#view-forecast-costs
• GCP Cloud Billing Cost Forecast: https://cloud.google.com/blog/products/gcp/predict-your-future-costs-with-google-cloud-billing-cost-forecast

?Machine Learning Tools

Machine learning is a sub-sector of Artificial intelligence focused on using algorithms that can be used to identify patterns from raw data and help evaluate costs and find ways to optimize spending. Each of the big three cloud providers offers machine learning models including the following (1):

• AWS Forecast: https//docs.aws.amazon.com/forecast/latest/dg/what-is-forecast.html
• Azure Machine Learning AutoML forecasting: https://learn.microsoft.com/en-us/azure/machine-learning/concept-automl-forecasting-methods?view=azureml-api-2
• GCPAutoMLforecasting: https://cloud.google.com/vertex-ai/docs/tabular-data/forecasting/overview

?Conclusion

One of the first way companies operating in the cloud can improve their cost savings is to leverage native cloud provider APIs, cost management and machine learning tools in their FinOps forecasting processes.