Attributing LLM inference costs across teams in production

You might assume that tracking costs for large language models is straightforward — just count tokens, right? Well, not exactly. According to /u/Extreme_Tangelo8336 on Reddit, as LLMs move beyond basic features into internal tools and workflows, attribution gets messy. Companies can see raw usage in infrastructure, but finance only sees invoices, making it hard to pin costs to specific teams or projects. That’s where the real challenge lies — how do you fairly assign these expenses? The article suggests that a mix of app-level tagging and internal reporting could be the solution, but many teams still treat it as shared infra costs, with no formal system. This lack of clarity isn’t just an accounting headache; it impacts how teams manage and optimize their AI usage. So, if the pattern holds, the next wave of successful teams might be those that figure out transparent, scalable ways to allocate costs — turning expense tracking into a strategic advantage.