Revolutionize AWS Cost Management with AI-Powered AnalyticsRevolutionize AWS Cost Management with AI-Powered Analytics
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Sanchit Agrawal
• 2d
AI-powered cloud cost intelligence platform using Claude, MCP, AWS Cost Explorer, and LangGraph.-- Project Description--
Built an advanced AI-driven AWS cost analysis platform that allows users to query multi-account AWS spending in natural language using Claude. Developed a custom Model Context Protocol (MCP) server exposing AWS Cost Explorer and Amazon Bedrock usage data as tools to AI agents. Implemented support for local and remote MCP deployments over HTTPS, cross-account IAM role assumption, and an interactive Chainlit dashboard powered by LangGraph. Containerized the entire system with Docker for production-ready deployment. Detailed Work Highlights --
1- Designed and built a custom MCP server in Python to expose AWS Cost Explorer and CloudWatch usage metrics to AI agents.
2- Integrated Anthropic Claude API for natural-language querying of AWS cost and Bedrock usage data.
3- Enabled cross-account AWS cost visibility using IAM role assumption across multiple AWS accounts.
4- Implemented detailed cost breakdowns by service, region, instance type, and day.
5- Added Amazon Bedrock usage analytics by model, user, region, and hourly invocation patterns.
6- Built a LangGraph agent capable of autonomously invoking MCP tools to answer cloud cost questions.
7- Developed an interactive Chainlit chat interface for real-time conversational cloud cost analysis.
8- Deployed the solution locally and remotely using Docker, EC2, SSE transport, and Nginx-based HTTPS reverse proxy.
9- Implemented secure multi-environment configuration using environment variables and AWS IAM best practices.
10- Authored comprehensive documentation and deployment instructions for production-ready setup. Key Deliverables (Client Side)
1. AI assistant for answering cloud cost questions in plain English
2. Unified dashboard for AWS and AI infrastructure spending
3. Multi-account cost visibility across all AWS accounts
4. Detailed breakdown of costs by service, region, and time period
5. Secure cloud deployment with Docker and HTTPS
6. Documentation for setup, usage, and maintenance
AI Agent DevelopmentCloud InfrastructureLangChainAWSClaudeAI Engineer
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