Most teams running AI in production can tell you their monthly OpenAI bill. Very few can tell you what a single successful outcome actually cost.
That gap is the problem Botanu is trying to solve. The startup has launched a tool that tracks cost per outcome rather than cost per API call - a distinction that matters more than it sounds. In real AI workflows, a single business result (processing a document, resolving a support ticket, generating a qualified lead) often takes multiple attempts. There are retries when the model hallucinates, fallbacks to a larger model when the cheaper one fails, tool calls that chain together, and async workers running in the background. Standard billing dashboards from OpenAI or Anthropic show you what each individual call cost. They don't show you that getting one clean invoice extraction took seven calls across three models because the first four hit edge cases.
The practical value here is straightforward: if you're running AI agents or multi-step workflows, you need to know whether the whole pipeline is profitable per unit of work, not just whether your average API call is cheap. A $0.02 call that succeeds 30% of the time costs a lot more per outcome than a $0.08 call that succeeds 95% of the time.
Botanu is early-stage and the product is still finding its shape, but the underlying question it asks is one that every team scaling AI workflows will eventually need to answer. As AI agents get more complex - chaining tools, retrying with different prompts, escalating to human review - the gap between "what did the API cost" and "what did the result cost" will only widen.