Providers price input, output, cached input and cache writes differently, so a flat rate per request charges a long prompt and a one-line question the same.

Inference pricing burns credits for every LLM call at per-token rates. Your app sends one inference event per model call with the company, the model and the provider's token counts. Or export the call's OpenTelemetry span to c.schematichq.com/v1/traces, where Schematic reads the standard gen_ai.* token attributes and you add the company and feature event name as schematic.* attributes.

An inference feature is an event-based feature whose event source is inference events. Its credit burndown entitlement has a rate per call plus rates for input, output, cached input, cache creation and reasoning tokens, all in credits. Before you publish, the form shows what a recent call would burn.

Preflight checks price a call from its token counts, so you can refuse an oversized request before it runs. Burns go through leases and reservations, and every draw is on the credit ledger.

Docs: Inference pricing and Inference pricing with OpenTelemetry

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