Set Your Own Rates for LLMs

set your own custom rates public pricing is rarely what large organizations actually pay if you have negotiated pricing with a provider, you can define custom contracted rates for any model in the catalog what you can override for each model and provider combination, you can set custom input price custom output price custom unit price for non token models overrides are applied only to your account and do not affect the public catalog how custom rates are applied once a contracted rate is configured all usage for that model is rated using your contracted price input and output costs are calculated separately aggregated cost data reflects your real spend, not list prices no changes are required in your applications or ai gateway configuration multi provider model comparison the ai catalog makes it easy to compare pricing across providers for the same model for example the same model may be available directly from openai the same model may also be offered through aws bedrock or azure ai pricing, units, and discount structures may differ across providers this enables provider level cost comparisons routing decisions based on real cost accurate chargeback across teams using different providers who this is for developers understand why two calls to the same model cost different amounts see how input and output usage affects cost validate pricing when switching providers finops and finance teams ensure ai costs reflect negotiated contracts eliminate pricing assumptions and spreadsheets enable accurate chargeback and showback platform and ai infrastructure teams standardize pricing across the organization enforce cost accuracy at the source support multi provider ai strategies without manual reconciliation key takeaway accurate ai cost management starts with accurate pricing amberflo’s ai catalog centralizes ai model pricing across providers applies pricing automatically with zero setup supports contracted rates for real world enterprise pricing enables trustworthy ai cost analytics and chargebacks this is the foundation for making ai spend measurable, explainable, and optimizable