An AI supply chain includes the models, providers, data sources, tools, infrastructure, and operational processes used to deliver an AI feature. Understanding it means knowing which dependencies affect each task and who is responsible for them.
Start with the core questions, then examine the examples and tradeoffs below.
An AI supply chain includes the models, providers, data sources, tools, infrastructure, and operational processes used to deliver an AI feature. Understanding it means knowing which dependencies affect each task and who is responsible for them.
Document where data comes from, which model endpoint processes it, what tools can be called, and where results and logs are retained. An agent can introduce additional dependencies after its first inference call.
Record versions and configuration where available. A model alias is useful for integration, but it may conceal a provider-side change unless the returned model and endpoint details are captured.
Pricing and capacity terms affect cost. Rate limits and outages affect reliability. Licences, data terms, and access policies affect permitted use. A single aggregate score cannot replace the evidence behind these different decisions.
Evaluate each dependency against your own requirements and agreement. A routing decision alone does not certify legal compliance or prove that a provider handles data in a particular way.
Assign an owner to important dependencies and document the effect of changes. A new retrieval source, tool permission, or fallback route can alter behavior even when the primary model is unchanged.
o10's Know Your Inference framework organizes assessment across performance, economics, integration, strategy, and risk. Treat the score as a way to structure review; retain the underlying evidence and decisions.
Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.
Paste a week of traffic. Get the number that books the audit.
See what you're overpaying →