DigitalOcean vs Scaleway

Both providers use per-hour billing for GPUs, avoiding per-second granularity seen in hyperscalers like AWS/GCP, which suits steady workloads but penalizes short bursts (minimum 1-hour charges apply). DigitalOcean emphasizes predictable flat rates for H100/H200 Droplets (e.g., ~$2.50-$6.25/GPU-hour estimated), with no spot instances or reserved options publicly detailed, simplifying budgeting for developers. Scaleway mirrors this for its GPU instances and Nabu access, potentially offering volume discounts for large Nabu reservations, though spot/preemptible pricing is limited. Implications: Hourly suits long-running training/inference (e.g., days+), but frequent small experiments (<1h) incur waste—better for committed runs than sporadic use. No long-term commitments reduce lock-in but miss savings vs. 1-3 year reservations elsewhere.

DigitalOcean delivers superior value for small experiments and fine-tuning: predictable pricing and 1-click deployments minimize setup costs/time, ideal for budgets <$5K/month on 1-8 GPUs. Scaleway edges out for large training runs (e.g., LLM pre-training on Nabu), where cluster-scale efficiencies reduce per-GPU costs for 100+ node jobs. For production inference, DO's ecosystem integrations (DOKS/Spaces) yield better TCO via reduced ops overhead. Batch inference favors Scaleway's sovereign scale if EU-bound. Overall, DO wins short/medium sporadic use (better ROI under 100h/month); Scaleway for sustained heavy loads (value scales with volume). Acknowledge Scaleway's potential unlisted discounts may tip enterprises.

DigitalOcean uses virtualized GPU Droplets (KVM-based) with H100/H200, integrated DOKS for orchestration, Spaces S3-compatible storage, and global regions (US/EU/Asia). Scaleway offers virtual instances and bare-metal GPUs, Kapsule managed Kubernetes, Object Storage, and EU-centric DCs (Paris/Amsterdam). DO emphasizes simplicity; Scaleway prioritizes sovereignty with Nabu—a dedicated H100 supercluster for low-latency multi-node jobs. Both support standard networking (up to 10Gbps+), but Scaleway's Nabu features RDMA for AI-scale interconnects.

DigitalOcean's H100/H200 Droplets deliver strong single/multi-GPU performance (e.g., 80GB/141GB HBM), with good scaling to 8 GPUs via NVLink/SLURM on DOKS; availability can fluctuate due to smaller inventory. Scaleway's Nabu provides hyperscale performance with 100s-1000s H100s, InfiniBand/RDMA for efficient all-reduce in training (benchmarks show ~95% scaling efficiency). DO suits <8 GPU jobs; Scaleway excels at massive parallelism. Both offer similar raw FP8/FP16 throughput, but Nabu edges distributed workloads; DO's integrations boost developer productivity.