Please Wait...
Please Wait...
Why hyperscaler architectures fall short for distributed AI workloads
Major cloud providers' business models, developed before the AI era, create vendor lock-in and unnecessary complexity for edge inference. Enterprises need composable, globally distributed infrastructure that supports both public and private workloads while maintaining cost efficiency as AI usage scales.
Get started with
world’s largest privately-held cloud
infrastructure company
AI Inference at the Edge is the New Architecture for Apps
CIOs and CTOs who can more efficiently manage inference at the edge stand to improve their enterprise's bottom line significantly.
With 90% of MLOps investments focused on inference rather than training, organizations need new architectural approaches to handle massive data volumes, ultra-low latency requirements, and strict data governance.
Vultr's purpose-built AI infrastructure, composable architecture, and integrated container registries deliver the flexibility and cost efficiency hyperscalers can't match for distributed AI deployment.
