Fine-tuning sits between training and inference: short bursts of GPU work interleaved with eval, dataset shuffling, and human review. Billing granularity (per-second vs per-hour), persistent storage, and dataset egress hit harder than the headline $/hr. Three picks across the axes.
Live pricing + 7-day reliability update on every page load. Curation refreshed manually.
LoRA / QLoRA on a 70B model fits a single A100 80GB. Region-aware availability across 3 DCs (CANADA-1, NORWAY-1, US-1), 100% data coverage in our 7-day window. Free egress.
Per-minute billing, persistent filesystems on 1-Click Clusters, snapshot workflow that doesn't require a manual EBS dance. You can stop a 4×H100 box overnight without losing your dataset cache.
Per-second billing + persistent volumes at $0.07/GB-mo. Iterative fine-tuners burn 30-50% of wall-clock idle (debugging, eval, waiting for human review). Per-second vs per-hour on a 14-min run = pay for 14 min vs 60 min — a ~4× difference invisible in the listing price.
Consumer cards (24 GB VRAM) for anything past 7B QLoRA force aggressive memory offload that 5×'s wall-clock time vs an A100 80GB at 2× the hourly rate. Net loss on TCO every time.
Dataset egress is the silent killer. If your dataset lives in S3 and you fine-tune on Lambda, AWS charges egress every time you re-pull. A 2 TB dataset = $180 one-time exit fee at $0.09/GB. Co-locate compute with storage, OR park your dataset in Cloudflare R2 ($0 egress) as a hub.
Honesty about gaps beats false confidence. We add data as it becomes structurally available.