Every AI feature in production is a stream of inference calls. Each one has a model, a prompt size, an output size, a latency, a cost, a set of parameters, a cache hit or miss, and an outcome. Most companies record almost none of it in a structured way. They find out what happened when the monthly bill arrives.
AIInferences.com names the system of record for that activity.
Observability for Model Calls
The product is a small inference observability server. Applications route each model invocation through it, or report to it, and it keeps the full picture: which model, how many tokens in and out, how long it took, what it cost, which parameters were set, whether a cache answered, and whether the result was accepted, retried or flagged. Think of an OpenTelemetry-style trace for AI inference.
With that record in place, the useful questions answer themselves. Which feature is burning the budget? Did the move to a cheaper model raise the retry rate? Which prompts fail most often? Is latency creeping up for one provider? Finance wants the cost view. Engineering wants performance. Compliance wants an audit trail of what each model was asked and what it said.
A Name for Where AI Spending Goes
Inference is where most AI money now goes, and that share keeps growing as models move from experiments into everyday products. The plural fits the subject, since the business is counting and understanding thousands of individual inferences. AIInferences.com suits an observability startup, a cost management platform, or a data product tracking inference usage across the industry.