Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Why this is worth reading: a free model endpoint with a large token allowance is a good place to validate a new CLI workflow, but it can burn through the allowance in a single retry loop before you notice.
I built a small stateful budget guard that checks the projected cost before the call, records actual usage after the call, and refuses to touch the ledger when the endpoint sends an unexpected response.
It works as a disposable first pass on a free endpoint and leaves you a clean exit when the shape changes.
MonkeyCode's outreach describes an open-source project with a free model route and a free hosted server.
I do not treat either as a permanent dependency.
I treat them as a test target: an endpoint I can call without a contract while I am still changing prompts, timeouts, and schemas.
The tool below is independent of MonkeyCode's exact model list; it assumes only an OpenAI-style chat completion path and usage accounting in the response.
Swap one function if the free server does not follow that shape.
The problem with a free allowance Most model dashboards report aggregate usage after the fact.
That is enough for casual work, but it is not enough when you wire an endpoint into a loop.
I have seen two avoidable failures in my own drafts.
A retry-on-timeout wrapper restarted a slow request four times before the first response arrived, multiplying total token spend.
A long context buffer kept sending the same 6k-token history on every turn because I forgot to trim old messages.
The dashboard showed the total drop, but not which call caused it.
A local ledger fixes that by refusing to send the request when the projected total exceeds the budget.
It does not replace the provider dashboard.
It makes the decision before the endpoint gets a chance to consume tokens.
The artifact The script below does three jobs: load a budget and already-used amount from a JSON file make a conservative preflight estimate for the next call record the actual usage returned by the endpoint and save the ledger atomically Preflight is deliberately rough: prompt bytes divided by four, plus the requested max response tokens, plus a 15 percent margin.
That is not tokenizer-accurate for non-English text or code-heavy prompts, but it is intentionally conservative because the goal is to stop accidental waste, not to replace metering.
If you need precise preflight numbers, add a local tokenizer for the model you are calling.
Then source it and call: : Test the guard before you trust it Use a failure fixture that does not hit the real endpoint.
The expected result is a non-zero exit and an unchanged ledger.
If you want a decision table for a canary suite, keep the checks tiny: Scenario Expected exit Ledger change Missing base URL or model non-zero none Unreachable endpoint or timeout non-zero none Projected spend over budget non-zero none Valid response with zero increases Response without a usable token count non-zero none I run this once before I allow any larger script to call the endpoint.
A failed run tells me which part of the integration changed instead of leaving me to guess from a balance chart.
Where the free server fits For a solo build, the free server is most useful as a canary target, not as a permanent backend.
I point this script at the free route first, keep the model name in an environment variable, and store all results in the local ledger.
If the endpoint changes one day, the only change is a URL or model name.
If the endpoint reports different usage fields, the script stops instead of silently undercounting.
I also set a hard mental exit: if the free endpoint is slow enough that I need a timeout above 30 seconds, it is not ready for the actual CLI.
The ledger cannot fix latency; it only prevents it from getting expensive while I measure.
One important context about the 30,000,000-token figure The reference I was given describes a free tier with a 30,000,000-tok