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The Cost Calculator answers “what will this agent cost to run, and which model should it run on?” with measured numbers instead of guesses. You describe an agent, AgentHub writes a batch of test conversations for it, and then plays every conversation against the models you choose. Each model’s real token usage, cost, latency and graded quality come back side by side, projected to your monthly volume. The page subtitle: “Describe an agent, estimate per-run cost, generate synthetic scenarios, and run them for measured cost and pass/fail.”

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The three steps

A stepper at the top shows 1 Configure › 2 Scenarios › 3 Results. You can click back to any step you have already reached; later steps stay disabled until you get there.

Step 1: Configure

Every field on the setup screen, the model panel and the pre-run estimate.

Step 2: Scenarios and running

How scenarios are generated and streamed, categories, and what happens when you click Run.

Step 3: Results

The recommendation, scale, comparison, forecast, TCO, Agent Audit and per-scenario detail.

Templates

Ten ready-made agents, including the Coding Agent Template.

Import from traces

Seed the configuration from real runs, or analyze real runs directly.

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Estimate first, then real spend

The Cost Calculator separates what is free from what costs money.
Running scenarios spends real money on your provider account. A batch of 30 scenarios against three models is 90 conversations, and multi-turn scenarios can play up to 16 user turns each, with the whole transcript re-sent on every turn. Check the Estimated cost before you click Generate Scenarios, and keep the scenario count small while you are iterating. The estimate is a single point estimate, not a ceiling.

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Every run leaves traces

Each executed scenario is written as a real trace in Traces, alongside your live traffic:
  • Source: simulation (live SDK traffic is live)
  • Environment: development
  • Root name: scenario:<scenario name>
  • Tags: the scenario category, cost-calculator, the batch’s simulation ID, and the model identifier
  • Spans: one LLM span per turn with real tokens, cost and latency, plus one tool span per simulated tool call, named tool_call: <Tool>.<operation>
That means you can open any scenario in the trace waterfall, and you can run evaluation collections over simulated traces exactly as you would over production ones. The Simulations page lists every run with its pass/fail breakdown.

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Recent runs

When the project has earlier runs, a Recent strip above the stepper shows up to six of them, each with its measured cost (or the estimate, if it never ran) and the model identifier (“draft” when no model was recorded). Click one to load it: a run that executed opens on its results, otherwise on its scenarios. A run that failed before executing anything shows “That run never executed any scenarios — nothing was measured”. You can also link straight to a run with ?run=<run id> on the Cost Calculator URL. Share Results on the results page copies exactly that link along with a text summary.

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Permissions

See Roles and permissions.

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Limits

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What it does not do

  • It never runs your agent’s code. Your agent is described by its prompt, tone, skills, memory and tool list, and the model under test plays that role. Your actual application is not involved.
  • Tool calls are simulated. When a scenario calls a connector such as Stripe or Jira, AgentHub writes a realistic simulated response into the conversation. Nothing is sent to the real system, even for a tool marked “real”. Tool failure scenarios inject an error mid-conversation to test recovery.
  • No scheduled or recurring runs. Every run is started by hand.