Context — the vocabulary this report assumes

Decision-makers shouldn’t need an ML background to use this evidence. Plain-language definitions of the terms doing the heaviest work:

• Token — the unit of text models read and write (~¾ of a word). All pricing starts here, which is why token counts, not just rates, matter.
• PEFT / LoRA / QLoRA — techniques that adapt a large model by training a tiny fraction of it. Makes fine-tuning compute cheap enough to run on one GPU — but doesn’t shrink the surrounding programme costs (data, evaluation, refits).
• Neocloud — a specialist GPU cloud (RunPod, Lambda, Together AI) renting raw GPU-hours at roughly half of hyperscaler prices, typically without the enterprise contracting layer.
• On-demand / reserved / spot — three rental tiers: pay-per-hour at list; 30–62% off for committing 1–3 years; and 60–91% off with interruption risk (the machine can be taken back mid-job).
• Prompt caching — paying once to process a repeated context (system prompts, documents), then 50–90% less to re-read it. Cache writes cost 1.25–2× base, so it only pays when context repeats.
• Batch API — half price on all tokens in exchange for results within 24 hours instead of seconds.
• Blended price — input and output rates combined into one number using a fixed ratio, so different models can be compared on one axis.
• Cost per successful task — what this report argues you should actually measure: total tokens (including retries and verbosity) divided by tasks completed acceptably. The only unit that reflects quality differences.
• Artificial Analysis Intelligence Index — an independent composite score (nine benchmarks, editorially weighted, text/English-centric) used here as the quality axis. Useful for locating the non-linear region; not precise enough to rank models to a decimal.
• Jevons paradox — the pattern where cheaper units increase total consumption enough that total spend rises. Bet FC3 asserts it applies here.
• Control premium — the 2–4× effective cost sovereign/compliance buyers knowingly pay for owning infrastructure rather than renting it.
• Procurement scope — whether “owning” means buying GPUs into existing infrastructure (~$30K/GPU) or procuring complete nodes with networking and financing (~$94K/GPU). The single assumption that swings every ownership crossover band.
• Utilization — the share of an owned GPU’s time doing paid work. The load-bearing variable in every own-vs-rent comparison.
• :free variants — aggregator-hosted model endpoints priced at literally $0/token with request caps. Real usage flows through them; they are also the first thing subsidy contraction would remove.