Looking for ways to optimize your AI applications usage of your cloud compute? Here's the first 5 steps to take.
1. Profile AI Workloads for Better Cloud Management
Understanding your AI workloads is the first step to cloud optimization. Use AWS CloudWatch, Google Cloud Monitoring, Azure Monitor, or open-source tools like MLflow and Prometheus to monitor GPU, CPU, memory, and storage usage in real time.
Tag every job by project and team. This helps you see which models or pipeline stages are most resource-intensive and identify optimization opportunities.
Tip: Use project tags and labels to break down usage by team or use case.
Credit: Amazon Cloudwatch
2. Right-Size Compute Resources for Optimal Performance
Achieve optimal AI performance, without overspending. by benchmarking jobs across various instance types. Compare AWS A100, GCP A100, Azure NCas_T4, or even NVIDIA’s cloud benchmarking tools.
Automate resource scaling using auto scaling groups or Kubernetes autoscalers. This ensures workloads adapt to demand in real time.
Pro Tip: Re-benchmark periodically, cloud hardware and pricing evolve rapidly!
Provider | Instance | On-Demand $/GPU‑hr |
|---|---|---|
Azure | A100 v4 (8 × A100) | $3.40 |
AWS EC2 | 8 × A100 | $4.10 |
Google Cloud | 1 × A100 40 GB | $4.27 |
Lambda GPU Cloud | 1 × A100 40 GB | $1.29 |
3. Detect and Eliminate Idle Resources
Idle resources, unused VMs, endpoints, or storage, are silent budget killers. Use automation such as AWS Lambda, Google Cloud Functions, or Azure Automation to regularly detect and shut down these “zombie” resources.
Schedule routine cloud audits and leverage scripts to flag and clean up idle assets.
Common Pitfall: Forgetting to delete temporary storage after training can quickly rack up costs!
4. Schedule Jobs for Maximum Efficiency
Smart scheduling can deliver major cost and sustainability gains. Use cloud schedulers, batch job APIs, and spot pricing to batch or queue non-urgent AI tasks. Time jobs for off-peak hours or periods with higher renewable energy availability.
Pro Tip: Batch low-priority jobs to maximize compute utilization and minimize idle time.
5. Integrate Real-Time Sustainability Metrics
Bring real-time ESG metrics (energy, carbon, water) directly into your AI workflows. Tools like CodeCarbon, Power BI, Grafana, or a unified solution like Pebble Insights let you track and report your environmental impact side-by-side with accuracy and performance.
Checklist: Add energy and carbon tracking to every ML run and visualize results in your reporting dashboards.
Credit: CodeCarbon
AI Cloud Optimization Checklist
Profile and tag all AI jobs by project and team.
Benchmark and right-size all compute resources.
Routinely detect and eliminate idle resources.
Batch and schedule jobs for cost and sustainability.
Integrate ESG metrics into all reporting.
Ready to Automate Every Step?
Why stitch together dozens of tools when Pebble can help you automate everything: profiling, right-sizing, cleanup, scheduling, and sustainability reporting, across your entire cloud AI stack?
Connect with us to see how Pebble Falcon can transform your cloud AI efficiency, cost, and ESG performance, without the manual work.
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