From GPU pricing to storage, networking, and efficiency—compare the true cost of running AI workloads between hyperscalers and CoreWeave Cloud.
This important Signal65 study details a comprehensive TCO analysis of AI cloud deployments. It compares CoreWeave to general-purpose hyperscalers—across compute, storage, networking, orchestration, observability, and support—showing how the right cloud platform fundamentally improves AI efficiency, performance, and total cost for training and inference.
over 3 years
running DeepSeek-R1
Across 17 models
This comprehensive report by Signal65 evaluates the true cost of running AI for small, medium, and large workloads, comparing CoreWeave with leading hyperscaler cloud providers. The findings show how purpose-built AI infrastructure can deliver significantly lower costs and greater efficiency over time.
CoreWeave provides transparent, predictable pricing with no surprise charges for data transfer, API calls, or orchestration.
CoreWeave delivers efficiency at every layer of the full stack—across compute, storage, networking, and observability—with direct-to-expert support.
Higher GPU utilization, industry leading performance, and lower cost per token for inference reduces total cost at scale.
CoreWeave's bare-metal stack of compute, storage, and networking delivers higher performance and lower cost per token for inference than the hyperscalers we evaluated. Lower GPU pricing and the removal of data egress and storage request fees compound that advantage, delivering up to 65% lower three-year TCO on NVIDIA GB200 NVL72.
CoreWeave Cloud enables greater efficiency, simpler deployment, and lower total cost at scale
To evaluate one key aspect of the TCO analysis, Signal65 modeled performance of NVIDIA GB200 inference performance and the cost per million tokens served. CoreWeave delivered the lowest cost per million tokens and achieved a per token savings ranging from 39% to 66% compared to the other hyperscalers.
