The Ultimate Guide to Simplifying AWS Cost Analysis with Generative AI

Managing cloud costs and understanding resource usage can be a daunting task, especially for organizations with complex AWS deployments. AWS Cost and Usage Reports (AWS CUR) provide valuable insights, but interpreting and querying the raw data can be challenging.

In this blog post, we delve into a cutting-edge solution that harnesses the power of generative artificial intelligence (AI) to generate SQL queries from user questions in natural language. This innovative approach simplifies querying CUR data stored in an Amazon Athena database, running SQL queries, and presenting results on a user-friendly web portal for effortless comprehension.

The solution leverages Amazon Bedrock, a fully managed service that offers a range of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. It provides capabilities to create generative AI applications with a focus on security, privacy, and responsible AI.

Challenges Addressed

The following challenges can hinder organizations from effectively analyzing their CUR data, leading to inefficiencies, overspending, and missed cost optimization opportunities. We aim to address and simplify them using generative AI with Amazon Bedrock:

• Complexity of SQL queries – Writing SQL queries to extract insights from CUR data can be complex, especially for non-technical users.
• Data accessibility – Accessing databases for data insights can pose security risks.
• User-friendliness – Traditional data analysis methods lack a user-friendly interface for non-technical users.

Solution Overview

The solution discussed in this article is a web application (chatbot) that allows users to ask questions about their AWS costs and usage in natural language. The application generates SQL queries based on user input, executes them on an Athena database containing CUR data, and presents results in a user-friendly format. By combining generative AI, SQL generation, database querying, and intuitive web interfaces, the solution offers a seamless experience for CUR data analysis.

The solution utilizes the following AWS services:

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Summary

The AWS CUR chatbot solution leverages Anthropic Claude on Amazon Bedrock to simplify CUR data analysis. By enabling natural language queries, the solution empowers users with valuable insights into AWS costs and resource usage. With this solution, organizations can optimize cloud spending, improve resource utilization, and make informed decisions. It is recommended to thoroughly set up the solution, considering production readiness and customization options based on specific needs.

Unlock the power of generative AI with Amazon Bedrock. Explore the quick start guide on GitHub to accelerate your generative AI applications and deploy cutting-edge solutions.

About the Author

Anutosh is a Solutions Architect at AWS India, dedicated to helping customers navigate their AWS journey. With expertise in migration, analytics, cybersecurity, and machine learning, he focuses on building cloud solutions that drive business success.