Private LLM pricing intelligence and rate-basis methodology

LLM vendors change prices, aggregators rename models, context tiers appear, cache discounts differ by provider, and frontier model releases can reset capability assumptions overnight. A PR estimate that uses a live feed without freezing the basis is difficult to reproduce later. Without a frozen basis, two identical pull requests evaluated on different days could yield completely different cost forecasts, leading to confusion and mistrust between engineering and finance.

Class1 treats the pricing basis like an estimator treats a cost book. Raw artifacts are preserved with provenance, normalized into silver rows, and curated into a gold snapshot. The estimate can say which basis it used, how old that basis is, and which items were measured, inferred, assumed, excluded, or unpriced. This rigorous approach ensures that every budget decision is defensible and traceable.

That makes the Blue Book commercially important. A public basis proves the open-core product. A private basis becomes the paid pilot: customer-specific rates, private actuals, internal model allowances, team allocation rules, and variance reports that cannot be copied from a public dashboard. It transitions AI cost management from an observational exercise to an active governance framework.