Predictable Cost

Why predictable cost matters for enterprise AI

Fixed, capacity‑based pricing:

 Avoids runaway usage fees and surprise egress charges common in public clouds. With HPE Private Cloud AI, you align spend to reserved GPU capacity and storage bandwidth, enabling clear TCO planning across training, fine‑tuning, and inference lifecycles — instead of guessing at month‑to‑month AI bills..

Tokens are units of thought:

 In an AI project, tokens represent the “units of thought” your models consume to read prompts, process context, and generate answers. As prompts get longer, context windows widen, models call tools, or retries kick in, token counts compound — and per‑token pricing can balloon rapidly in the cloud.

On‑Prem = stable cost per token:

 By running inference (and fine‑tuning where appropriate) on PCAI, you transform variable token bills into predictable capacity costs. This lets you budget at a fixed effective cost per token, right‑size clusters to demand, and avoid per‑request overages — while eliminating data egress fees between services.

Data sovereignty & locality are built in:

 Keeping prompts, embeddings, and training data on‑premises improves control over sensitive information, reduces compliance risk, and shortens data paths for lower latency — giving you both financial and operational leverage.