Optimizing Infrastructure for Cost and Performance Efficiency
A tech startup approached us with a clear mission: reduce the high costs of hosting AI workloads without sacrificing performance. Cloud and on-prem infrastructure expenses, particularly at the VM level, were becoming a barrier to scalability for many businesses, especially startups entering the AI space.
The Opportunity
With infrastructure costs soaring across the AI ecosystem, we saw an opportunity to deliver a solution that not only optimizes general IT workloads but is also purpose-built for AI model deployment and inference.
Our Approach
We developed a lightweight predictive model that analyzes infrastructure usage and delivers actionable optimization strategies focusing on two areas:
- Infrastructure Usage Insights: Real-time visibility into current compute resource utilization.
- Forecasting Needs: Predictive analytics based on 3–12 months of usage trends to inform scaling decisions.
While initial experiments with machine learning were hindered by limited GPU/CPU benchmark data, we pivoted to a mathematical optimization approach, using correlation analysis to generate Pareto-efficient options balancing cost and performance. These outputs help clients make smarter, faster infrastructure decisions for AI workloads.
Challenges & Adaptation
Access to real-world GPU/CPU data was limited, preventing us from training a reliable ML model. Instead, we built our foundation on publicly available benchmarks, focusing on precision and practical implementation while ensuring scalability for AI-heavy environments.
Looking Ahead
This project is an early step toward a more intelligent infrastructure layer for AI. As we collect client-specific data, we plan to enhance prediction accuracy and reintroduce machine learning to unlock deeper optimization, not just for general computation, but for AI model training, inference, and hybrid workloads.
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