AI spend climbs month on month, and the only artefact anyone can point to is the invoice.
AI spend without attribution can neither be defended nor cut. Attribute cost per agent, task and version next to a quality verdict, and budget decisions run on evidence instead of totals.
The bill is real. Everything that would explain it is missing.
The monthly AI bill reaches five figures with minimal usage data behind it. When someone asks what the spend produced, the answer is a reconstruction, not a report.
Token spend sits in the platform invoice; what the agents produced sits everywhere else. Nobody can put cost next to a completed task, so nobody can say whether the number is high or a bargain.
One user request can fan out into dozens of model calls, tool retries, and sub-agent steps. Forecasting next month's bill from last month's is the only method available, and it keeps being wrong.
When the bill spikes, teams move agents to a cheaper model purely to cap spend. Nobody measures what that costs in quality, because nothing measures quality per run.
Each gap is structural, not careless. Platform billing was never built to answer per-agent questions.
Per-seat and usage-based pricing report spend by account or API key, not by agent, version, or task. The unit finance asks about does not exist in the invoice.
A single completion has a knowable cost; an agent run does not. Loops without terminal conditions, retries on failing tools, and context history that grows every turn mean two identical requests can differ in cost many times over.
Finance sees the invoice, engineering sees the traces, and no tool holds both. "Expensive but right" and "cheap but wrong" collapse into one number: spend.
Monitoring that would attribute the spend stalls in procurement because nobody can prove in advance what it will save. The gap that causes the problem also defends it.
Prefactor watches every run and records what it cost as it happens, so spend stops being a monthly surprise.
Cost recorded per run, as runs happen. Every model call, retry, and tool step lands in one record with its token spend, tagged to the agent, the version, and the task that incurred it.
The verdict sits next to the spend. Every run is evaluated against the agent's job, so cost per run becomes cost per outcome. Expensive but right and cheap but wrong become different numbers.
Trends, per agent and per version. Cost per passed task over time shows which prompt edit, model swap, or integration moved the number, in either direction.
Attribution finds the money. The loop decides what to do with it.
Budgets pause an agent before the overrun. A per-run or per-agent budget stops a looping run while it is a line item, not an escalation. The paused run keeps its record, so the decision to resume or fix starts from evidence.
Fix the pattern, not the invoice. The record shows where the money goes: a tool retried dozens of times, context growing without bound, a step a smaller model handles at the same quality. Each fix is a measured change, not a hunch.
Model swaps become evidence decisions. When a cheaper model is proposed, the quality trend and the cost trend for that agent answer the question before the swap ships. What it saves and what it gives up are both on record.
An agent's cost per task tripled in a week with no code change. The record showed a knowledge-base update had made one retrieval step return far more documents, and context grew on every turn after it. The team trimmed the retrieval step, set a budget that pauses runs past a threshold, and cost per passed task fell below where it started. Illustrative, but this is what attribution turns a billing dispute into.
See what each agent costs per run and per task, and find the loop or retry pattern behind a spike without a reproduction hunt.
See the solution →Product leadersPrice a feature on cost per successful outcome, not a share of one invoice, and defend the spend with the quality it bought.
See the solution →Heads of AIOne view of spend across the portfolio: which agents earn their cost, which do not, and what a model swap would actually change.
See the solution →Book a demo and we will attribute a bill like yours: cost per agent, per version, per task, with the quality it bought beside it.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.