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.

At the heart of our platform lies a strict adherence to the principles of cost engineering, adapted for the unpredictable nature of Large Language Models. Unlike traditional software where compute costs are deterministic and tied directly to traffic, AI feature costs are highly variable. They depend on model selection, input token length, output token variability, the frequency of retries due to hallucinations or malformed JSON, and dynamic fallback chains.

To capture this complexity, Class1 employs Monte Carlo simulations using Common Random Numbers (CRN). By modeling the system before and after a pull request across thousands of synthetic scenarios, we isolate the true cost delta of your architectural change from background noise. This approach allows us to assign an AACE (Association for the Advancement of Cost Engineering) estimate class to the forecast, communicating both the expected cost and the confidence interval.

Furthermore, this estimation does not happen in a vacuum. The Blue Book ledger records historical execution data, creating a closed-loop calibration system. As your team merges changes and actuals are observed in production, the engine updates its actuarial tables. This continuous feedback loop ensures that our pre-merge budget gates remain accurate and actionable, preventing catastrophic cost overruns before they reach production while maintaining developer velocity.