AI CostSense

Your AI-powered financial guardian for cloud — optimize cost, maximize performance.

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Inspiration

What it does

How we built it

Challenges we ran into

✅ Inspiration

Managing cloud infrastructure is becoming increasingly complex, especially for startups and small teams. Most companies struggle with unexpected cloud bills, idle resources, and confusing dashboards that provide information but not actionable decisions.

We wanted to solve a real-world pain point: 👉 How can teams optimize cloud cost automatically without hiring expensive FinOps experts?

This inspired us to build AI CostSense, an AI-powered advisor that understands cloud usage patterns, predicts cost spikes, and recommends (or even performs) optimization actions — just like a smart financial guardian for your cloud.

✅ What it does

AI CostSense is a smart, autonomous cloud cost optimization agent. It:

Monitors real-time AWS spending

Detects idle, unused, or over-provisioned resources

Predicts future spending using AI forecasting models

Flags anomalies before they become expensive problems

Generates personalized recommendation plans

Suggests optimal instance sizing

Provides a clean dashboard with costs, trends & waste metrics

Can automatically optimize resources when the user approves

At its core, the system converts raw cloud data into clear, actionable insights—powered by Lyzr agents + Amazon Nova reasoning models.

✅ How we built it

We used a multi-layer architecture combining cloud analytics, agent workflows, and powerful LLM reasoning:

🔹 AI Layer

Amazon Nova for reasoning and recommendation generation

AWS Bedrock for embedding + contextual decision support

Lyzr Studio to orchestrate multi-agent operations (fetching data, analyzing patterns, generating reports)

🔹 Backend

Python (FastAPI) API

AWS Lambda for fetching CloudWatch + Cost Explorer data

DynamoDB for storing insights and historical trends

AWS Forecast for cost prediction

🔹 Frontend

React + TailwindCSS dashboard

QuickSight for interactive visualizations

🔹 Automation

Systems Manager + Lambda to execute approved optimization actions

IAM policies for secure resource-level permissions

We integrated everything into a seamless workflow where the Lyzr agent guides the entire cost optimization cycle.

✅ Challenges we ran into 1️⃣ Handling Noisy Cloud Data

Cloud cost data fluctuates heavily; building clean, understandable patterns was difficult.

2️⃣ LLM Context Window Issues

Large cost JSONs exceeded model limits, requiring chunking + summarization pipelines.

3️⃣ AWS Permissions

IAM setup for secure analysis and limited-action execution was complex.

4️⃣ Forecast Accuracy

Training the model to accurately predict cost spikes involved multiple iterations.

5️⃣ Multi-Agent Coordination

Getting Lyzr agents to collaborate (fetch → analyze → recommend → summarize) required multiple design tweaks.

6️⃣ UI Simplification

We wanted a dashboard anyone could understand — reducing complexity took time.

✅ Accomplishments that we're proud of

Built a fully functional AI-driven cost advisor in limited hackathon time

Integrated Amazon Nova + Lyzr Studio to create automated decision-making workflows

Achieved consistent and meaningful cost predictions

Designed a clean, intuitive dashboard

Implemented one-click optimizations using AWS Systems Manager

Reduced sample cloud cost by up to 38% in our test cases

Created a scalable architecture that can grow into a real SaaS product

✅ What we learned

How to build agentic AI systems using Lyzr

Advanced reasoning capabilities of Amazon Nova

Best practices for cloud cost monitoring & FinOps

Efficient usage of CloudWatch, Cost Explorer & DynamoDB

Deploying serverless architectures for real-time automation

How to convert AI insights into actionable business value

The hackathon pushed us to think beyond dashboards and build a system that acts, not just informs.

✅ What's next for AI CostSense

We have a powerful MVP — now we want to expand it:

🚀 1. Automation Without Human Approval

A fully autonomous mode that can downscale, clean, and optimize resources automatically.

🌐 2. Multi-Cloud Support

Adding Azure, GCP and Kubernetes cluster cost analytics.

🎤 3. Voice-Based Cloud Advisor

Ask your chatbot: “Why did my cost spike today?” and get instant insights.

Accomplishments that we're proud of

What we learned

What's next for AI CostSense

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