Adjust the sliders. Every number below is an estimate, not a measurement. We assume a hard cap stops the extra calls after N calls. N is the slider, not a hidden capture rate.
Formula: extra calls = agents × calls/day × 30 × loop frequency × N × cost per call. Savings assume a hard cap stops those extra calls. Pro is modeled at $99/month. Not a measurement.
Go deeper
The calculator shows the exposure. SatGate maps it to enforceable authority checks, receipts, and a signed receipt (Evidence Pack) before agents execute.
ROI assumptions
Agents × calls per day × average cost per call × 30 days.
Normal call volume × loop/error frequency × wasted calls before discovery.
Budget enforcement before the request goes through denies over-budget requests at the gateway before forwarding to upstream APIs or MCP tools.
Risk scenarios
The model is most useful when teams connect it to a concrete failure mode. These are the three agent-spend patterns that usually make budget enforcement before the request goes through pay back fastest.
An agent repeatedly calls a paid tool, data source, browser action, cloud task, or SaaS operation until a budget check stops it.
A parent agent creates sub-agents that multiply model, API, and tool calls faster than team-level budgets can explain.
A workflow retries failed or low-confidence calls against billable APIs, turning an exception path into a hidden invoice.
Break-even examples
The calculator is most persuasive when it ties avoided waste to a specific operating model: internal agents, MCP tools, or externally exposed agent access.
A few dozen agents using a paid API can justify enforcement when one retry loop would exceed a monthly gateway subscription.
Map this to proof →Hundreds of agents calling MCP tools need per-tool caps because provider dashboards miss spend outside LLM token invoices.
Map this to proof →Externally exposed agent access needs scoped authority, budget checks, and receipts before execution.
Map this to proof →Most LLM cost dashboards measure known spend: tokens, requests, and invoices after the fact. This calculator focuses on avoidable agent-loop exposure: the API and tool spend created when autonomous agents retry, delegate, call MCP tools, or continue a task after the economics no longer make sense.
The model is intentionally simple: agent count × daily tool calls × cost per call × loop frequency × loop duration. It gives finance, platform, and security teams a shared number for the cost of missing inline budget enforcement.
SatGate does not wait for a billing export. It checks agent identity, route, tool, request cost, remaining budget, and policy before forwarding the call. That is why the savings estimate assumes most loop waste is prevented rather than merely reported.
Start in Observe mode to measure real spend, then move high-risk routes to Control mode when you are ready to enforce hard budget limits.
FAQ
Ghost spend is estimated from active agents, tool calls per day, average cost per tool call, loop frequency, and the number of calls wasted before a loop is detected.
Autonomous agents can retry, delegate, and call paid tools faster than humans can notice. Without inline budget enforcement, dashboards and alerts usually detect the cost after it has already happened.
SatGate enforces per-agent, per-tool and per-route budgets at the gateway and blocks requests that exceed them.
You need the number of active agents, average cost per tool call, calls per agent per day, expected loop or error frequency, and average loop duration before discovery.
Turn the exposure model into rules and receipts: define authority, budget limits, MCP tool caps, scoped capability-token policy, receipts, and receipt exports.
Yes. A token cost calculator estimates model usage. This ROI calculator estimates autonomous agent spend risk across paid tools, APIs, MCP calls, retries, delegation, and loops that may happen outside a single LLM invoice.
For agentic systems with paid tool access, payback can be measured in days when a small number of runaway loops or expensive MCP calls would exceed the monthly cost of budget enforcement before the request goes through.
Platform, security, finance, and AI engineering teams should use it before giving autonomous agents access to paid APIs, model providers, MCP tools, data services, or external agent marketplaces.
SatGate checks permission before the agent acts, records every policy decision as a receipt, and packages the evidence for review.