"What does the bot cost us?" is the question that decides whether an agent program grows or gets quietly shut down — and most platforms can only answer it with a provider invoice four weeks later.
Botmanor answers it per run. Every execution records its token usage and computed cost alongside the model that ran, so cost attribution is native: by agent, by app, by workspace, by time window. The unit economics of "support deflection bot, last week" is a filter, not a finance project.
For agencies, this is pricing infrastructure. With one workspace per client, per-client cost is structural — Marco's team can see which clients' bots are heavily used, what each costs to serve, and where margin is leaking, then decide deliberately: move the high-volume FAQ traffic to a cheaper model binding, keep the complex escalation agent on the premium one. Each change is an agent version with a changelog, so the cost optimisation itself is auditable.
For internal platform teams, the same records defend the budget. When finance asks why the LLM line item doubled, the answer is specific: this agent, this usage growth, this cost per resolved conversation — with the execution ledger to back it.
Botmanor rides the Burdenoff platform's billing for plan management and subscriptions. Automated quota enforcement — per-plan caps that gate usage at the metering layer — is on the roadmap and labelled as such; the per-execution cost ledger you would want underneath any quota system is already writing, on every run, today.
Do it yourself
Answer "what does the bot cost?" per run: read token-level cost on every execution, roll it up by agent and time window, and manage plans and subscriptions through platform billing.
Go to Executions, where every run records its token usage and computed cost.
You should see: Cost attribution is native — visible per run alongside the model that ran.
Ready to make this your story?
