GenAI-Powered Scaling Recommendation Feature - Obzera
Bedrock
Python
Langchain
Langgraph
MCPOverview
As part of Obzera's cloud optimization toolkit, we introduced an intelligent recommendation feature that evaluates real-time cloud usage metrics to guide customers on scaling decisions. This feature helps users avoid unnecessary cost or performance issues by offering data-driven scaling suggestions instead of relying on manual monitoring or assumptions.
Primary Goals
- Make scaling decisions predictive rather than reactive
- Provide clear, actionable recommendations based on observed usage patterns
- Improve cloud cost efficiency without compromising performance
Solution Delivered
We implemented a GenAI-based engine within Obzera that continuously analyzes CPU, memory, disk and other operational metrics from active EC2 environments. The system interprets utilization trends, forecasts future demand, and recommends whether to upscale, downscale, or maintain the current configuration. The feature integrates directly into the Obzera insights dashboard and alerts workflow.
Quantifiable Benefits
- Lower cloud costs without compromising reliability
- Better user experience from stable and well-sized infrastructure
- Reduced operational load on DevOps teams
- Lower MTTR & faster MTTD with real-time insights
Conclusion
With this enhancement, Obzera evolved from a monitoring tool into an intelligent decision-support system - helping customers make smarter, proactive cloud scaling choices powered by GenAI insights.
