Cirrascale vs Scaleway

Cirrascale employs monthly billing on bare-metal servers, locking in costs for full-month commitments without spot or on-demand options. This favors predictable, long-term usage (e.g., 500+ GPU-hours/month per instance) but penalizes short bursts or low utilization, as partial months aren't prorated. Scaleway uses per-hour billing with on-demand instances, enabling granular scaling down to the hour, ideal for intermittent workloads. No reserved instances are highlighted for either, but Scaleway's flexibility supports autoscaling via Kubernetes. Implications: Cirrascale minimizes per-hour costs for sustained jobs (potentially 20-30% cheaper at high utilization) but incurs waste on idle time; Scaleway reduces upfront risk for experiments or variable inference, though sustained runs may cost more without discounts.

For small experiments or fine-tuning (<100 GPU-hours), Scaleway offers superior value via per-hour billing, avoiding monthly minimums. Large training runs (1,000+ GPU-hours) favor Cirrascale's monthly model for cost predictability and bare-metal efficiency, potentially 15-25% better ROI on long jobs. Batch inference benefits Scaleway's elasticity for spiky demands, while production real-time inference leans toward Scaleway's integrated services for low-latency scaling. Cirrascale shines in research HPC with 90%+ utilization; Scaleway wins for budget-conscious teams (<€5k/month) needing compliance. Overall, Scaleway provides broader value for diverse patterns, Cirrascale for specialized, high-commitment scenarios.

Cirrascale focuses on bare-metal dedicated servers, bypassing virtualization for direct hardware access, with diverse accelerators (NVIDIA H100/A100, AMD MI300, Qualcomm). Limited info on networking/storage, but supports multi-GPU nodes without shared tenancy. Scaleway offers virtualized instances on Nabu Supercomputer (NVIDIA H100 dense clusters), integrated with managed Kubernetes, block/object storage, and high-speed InfiniBand. Both likely support Docker; Scaleway emphasizes EU data centers for sovereignty.

Cirrascale provides superior consistency for multi-GPU scaling in long jobs due to non-virtualized isolation, excelling in HPC benchmarks with low jitter. Scaleway's Nabu delivers high aggregate throughput (e.g., 100s of H100s interconnected), but virtualization may add 5-10% overhead in latency-sensitive tasks. GPU availability favors Scaleway's broader inventory; Cirrascale's diversity aids specialized workloads. Both scale well to clusters, though Cirrascale edges in raw per-GPU perf for training.