Salad vs Scaleway

Salad's per-second billing with spot instances optimizes for bursty, long-running jobs, charging only for active compute and allowing interruptions for deeper discounts (often 70-90% below on-demand rates). No reserved instances noted, suiting variable workloads like overnight training where partial utilization is common. Implications: excels for sporadic experiments or massive batches, minimizing waste, but risks sudden stops disrupt sensitive runs. Scaleway employs per-hour billing, likely on-demand with possible reservations, aligning with traditional clouds for predictable costs. No spot pricing confirmed, so better for sustained usage without preemption fears. Hourly granularity suits production but inflates small-job expenses (e.g., a 30-min task bills full hour). Overall, Salad favors irregular, high-volume patterns; Scaleway steady-state deployments, with Salad potentially 2-5x cheaper for qualifying spots per public estimates. (152 words)

Salad delivers superior value for small experiments and large training runs, where spot per-second pricing slashes costs for 100+ GPU hours (e.g., $0.10-0.30/GPU-hr vs. competitors' $1+), ideal for batch inference on fault-tolerant frameworks like Ray. However, variability reduces value for time-critical tasks. Scaleway offers better value in production inference and integrated setups, with per-hour rates providing reliability without spot risks; Nabu clusters enable efficient multi-GPU scaling for LLMs, plus bundled storage/networking cuts total ownership costs for EU teams. For fine-tuning, Salad edges on price, but Scaleway wins for compliant, mid-duration jobs. Salad bests for budget-constrained bursts; Scaleway for enterprise predictability. (148 words)

Salad's decentralized residential network uses virtualized consumer GPUs (e.g., RTX series) without dedicated bare metal, emphasizing spot access via custom orchestration; limited Kubernetes support, basic storage, and peer networking suit batch-only. Scaleway provides bare-metal Nabu Supercomputer with H100/A100 clusters, full Kubernetes, block/object storage, and high-bandwidth fabrics for integrated stacks. Salad lacks sovereignty controls; Scaleway prioritizes EU data residency. (98 words)

Salad offers high GPU availability for batches but variable performance from consumer hardware (e.g., mixed PCIe speeds, no NVLink), strong multi-GPU via fault-tolerant software yet prone to 10-20% preemptions. Scaleway delivers consistent high-end perf on Nabu (e.g., 8x H100 nodes), superior scaling/interconnects for training, reliable availability. Salad faster/cheaper for tolerant workloads; Scaleway edges production benchmarks, though capacity may queue. (96 words)