CoreWeave vs Nebius

Both CoreWeave and Nebius employ per-second billing with spot instances alongside on-demand options, enabling fine-grained cost control for variable workloads. No reserved instances are highlighted for either, emphasizing flexibility over long-term commitments. Spot pricing can yield 50-90% discounts versus on-demand, but availability fluctuates with demand—CoreWeave's constrained inventory may limit spot access during peaks, while Nebius's enterprise focus suggests steadier supply. Per-second granularity benefits short experiments or auto-scaling inference, reducing waste compared to per-hour models elsewhere. Implications: intermittent fine-tuning favors both; prolonged training runs amplify spot savings but risk interruptions. Enterprises with predictable loads may find on-demand parity, while bursty users gain from per-second precision. Overall, models are closely aligned, with value tilting on GPU availability and utilization rates.

CoreWeave offers superior value for large training runs (e.g., 100+ GPUs), where InfiniBand efficiency and spot depth minimize costs per FLOP, potentially 20-30% better for scale despite inventory risks. Nebius edges out for small experiments and production inference, with managed services reducing ops overhead (equivalent to 10-20% savings) and reliable compliance. Batch inference suits CoreWeave's burst capacity; real-time inference favors Nebius's stable managed K8s. For budgets under $10k/month, Nebius provides easier entry; over $100k/month scale training, CoreWeave's networking yields better TCO. Spot-heavy strategies benefit both equally, but Nebius's transparency aids forecasting. Limited public benchmarks prevent precise quantification, but CoreWeave targets hyperscale value, Nebius enterprise predictability.

CoreWeave deploys Kubernetes-native bare-metal GPU clusters with InfiniBand for low-latency, high-bandwidth networking up to thousands of GPUs; supports NVMe storage and autoscaling. Nebius provides managed Kubernetes on virtualized or bare-metal GPUs, with emphasis on compliant storage (e.g., S3-compatible) and multi-region EU/US availability. CoreWeave prioritizes raw scale over management; Nebius adds service layers for easier ops, both leveraging NVIDIA hardware without specified virtualization overhead.

CoreWeave leads in multi-GPU scaling via InfiniBand (up to 400Gb/s), enabling top LLM training throughput; GPU availability tight for H100s/A100s. Nebius offers solid NVIDIA GPU access with managed scaling, but lacks public InfiniBand details—potentially Ethernet-based, capping at lower bandwidth for massive jobs. Both handle Kubernetes workloads well; CoreWeave reports superior all-reduce speeds, Nebius focuses on inference latency. Availability favors Nebius for smaller requests.