AI Workload Autoscaling Platform
Intelligent autoscaling platform for AI/ML workloads using Ray clusters across external GPU vendors.
Overview
Built a production autoscaling platform for PetPortraitAI that dynamically provisions GPU resources from external vendors on demand. The system eliminates fixed GPU costs by scaling to zero when idle and bursting capacity during peak workloads. Full observability with Prometheus and Grafana gives real-time insight into every workload, and Cloudflare Zero Trust secures cross-vendor access without a VPN.
Challenges
- 1
Integrating with multiple external GPU vendors through disparate APIs and auth schemes
- 2
Implementing cost-aware autoscaling logic without sacrificing inference latency
- 3
Setting up Cloudflare Zero Trust to replace VPN for secure multi-vendor networking
- 4
Ensuring reliable inter-node communication in Ray clusters across vendor boundaries
Future Improvements
- ◆Add predictive scaling based on historical workload patterns using time-series forecasting
- ◆Implement spot-instance optimization across GPU vendors for further cost reduction
- ◆Add automated benchmarking pipeline to continuously evaluate GPU vendor performance