Built by operators who saw the AI spend visibility gap firsthand.

We spent years inside companies where AI infrastructure was growing faster than the ability to manage it. The pattern was consistent: a CTO approved OpenAI access for one product team, then Anthropic for another use case, then Cursor for the whole engineering organization, then AWS Bedrock for a compliance-sensitive workflow. Each decision made sense on its own. The cumulative picture was never visible anywhere.

We watched engineering leaders spend hours reconstructing cost explanations for boards that had already moved on to the next question. We watched Finance teams build AI budget forecasts anchored to last month's invoice total because there was no other baseline to work from. We watched VP Engineering personas set up custom cost tagging in AWS and OpenAI, maintain it through team reorganizations, and still end up with incomplete attribution because it did not cover the SaaS tools.

Every other major infrastructure cost category had solved this problem. Cloud spend has had purpose-built observability tooling for a decade. SaaS spend management is a mature category. Headcount cost tracking is table stakes in any finance system. AI spend was the one category growing fast enough to matter and lacking the tooling to manage it. That is the gap Zelaros exists to close.