Solving AWS CUR Data Analysis Challenges with Generative AI and Amazon Bedrock

Managing cloud costs and understanding resource usage are critical aspects of optimizing AWS deployments for organizations. However, the complexity of querying and interpreting AWS Cost and Usage Reports (CUR) data can be a significant challenge, especially for non-technical users. In this blog post, we explore a solution that leverages generative artificial intelligence (AI) to simplify the process of querying CUR data stored in an Amazon Athena database using natural language questions.

The solution utilizes Amazon Bedrock, a fully managed service that offers a variety of high-performing foundation models (FMs) from leading AI companies. By integrating generative AI capabilities with SQL query generation, database querying, and a user-friendly web interface, this solution aims to streamline the analysis of CUR data for organizations.

### Challenges Addressed:
1. **Complexity of SQL queries:** Writing SQL queries to extract insights from CUR data can be challenging, especially for non-technical users.
2. **Data accessibility:** Accessing and analyzing structured data in databases can pose potential threats to data protection.
3. **User-friendliness:** Traditional methods of analyzing CUR data may lack a user-friendly interface, making it difficult for non-technical users to leverage valuable insights.

### Solution Overview:
The solution we discuss is a web application (chatbot) that allows users to ask questions related to their AWS costs and usage in natural language. The application generates SQL queries based on the user’s input, runs them against an Athena database containing CUR data, and presents the results in an easy-to-understand format. By combining generative AI, SQL generation, database querying, and a user-friendly web interface, this solution provides a seamless experience for analyzing and interpreting CUR data.

### Prerequisites:
To set up this solution, the following prerequisites are required:
1. Create an Athena database and table to store CUR data.
2. Set up a compute environment to call Amazon Bedrock APIs.
3. Install necessary libraries for working within the environment.

### Deployment Steps:
The deployment steps involve creating a Streamlit web application, connecting it to the LangChain application, and deploying the solution to a hosting environment. The interaction between the user input, SQL query generation, and query results is orchestrated to provide a seamless experience for users.

### Benefits:
– **Simplified data analysis:** Analyze CUR data using natural language, eliminating the need for advanced SQL knowledge.
– **Increased accessibility:** Non-technical users can efficiently access and gain insights from CUR data without needing database credentials.
– **Time-saving:** Quickly get answers to cost and usage questions without manually writing complex SQL queries.
– **Enhanced visibility:** Gain visibility into AWS costs and usage for better cost optimization and resource management decisions.

### Summary:
The AWS CUR chatbot solution leverages generative AI, SQL generation, and a user-friendly web interface to simplify the analysis of CUR data. By enabling users to ask natural language questions, this solution empowers organizations to make informed decisions, optimize cloud spending, and improve resource utilization. With Amazon Bedrock, organizations can accelerate their journey in building powerful generative AI applications for a wide range of scenarios.

In conclusion, the integration of AI technologies with cloud cost management can provide valuable insights and streamline processes for organizations with complex AWS deployments. By leveraging generative AI capabilities, organizations can optimize their cloud costs, improve resource utilization, and make informed decisions.