# Learn to Budget a Free Model Tier by Building a Tiny Token Ledger

> Source: <https://dev.to/magickong/learn-to-budget-a-free-model-tier-by-building-a-tiny-token-ledger-3dde>
> Published: 2026-08-15 00:33:02+00:00

Core point: **a free model tier is not a yes/no answer; it is a budget.** Before I send a batch job to an advertised free tier, I want a deterministic ledger that predicts a quota miss instead of discovering it after 40 minutes.

Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Recent DEV threads circle around AI watermarking, agent tool gates, and whether AI is a thinking problem. My problem is smaller: I have an operator-supplied figure of **30,000,000 free tokens** and a **free server option**, and I want to know if a batch script fits without making a single live request.

Free tiers tend to advertise a raw allowance, but batch jobs fail in unhelpful ways:

A tiny ledger turns that into a pass/fail fixture before any API call.

Suppose a batch job needs 6,000 summaries where each prompt is roughly 21,000 characters. The completion target is 500 characters per call. My rough English heuristic is:

1 token ≈ 4 characters

That gives 5,250 prompt tokens plus 125 completion tokens per call, so 5,375 tokens per call. Multiplied by 6,000 calls, the job would need about **32,250,000 tokens** — over the 30,000,000 allowance.

That is the error input. The job must fail before I spend anything.

Python 3.11+ is enough. No external packages. Replace the `ALLOWANCE`

value with the limit from your own account.

``` python
from dataclasses import dataclass

ALLOWANCE = 30_000_000  # operator-supplied allowance, 30M tokens

@dataclass(frozen=True)
class Job:
    name: str
    prompt_chars: int
    completion_chars: int
    calls: int

def estimate_tokens(chars: int) -> int:
    # Planning heuristic only: 1 English token ~= 4 characters.
    # A real tokenizer will differ; use it for order-of-magnitude checks.
    return max(1, chars // 4)

def plan_job(job: Job, allowance: int = ALLOWANCE) -> dict:
    per_call = estimate_tokens(job.prompt_chars) + estimate_tokens(job.completion_chars)
    total = per_call * job.calls
    return {
        "job": job.name,
        "per_call_tokens": per_call,
        "total_tokens": total,
        "allowance_tokens": allowance,
        "fits": total <= allowance,
        "headroom_pct": round((allowance - total) / allowance * 100, 1),
    }

small = Job("small-summaries", prompt_chars=4_000, completion_chars=500, calls=10_000)
print(plan_job(small))

large = Job("long-sweep", prompt_chars=21_000, completion_chars=500, calls=6_000)
print(plan_job(large))
{'job': 'small-summaries', 'per_call_tokens': 1125, 'total_tokens': 11250000, 'allowance_tokens': 30000000, 'fits': True, 'headroom_pct': 62.5}
{'job': 'long-sweep', 'per_call_tokens': 5375, 'total_tokens': 32250000, 'allowance_tokens': 30000000, 'fits': False, 'headroom_pct': -7.5}
```

The first fixture fits with 62.5% headroom. The second one fails cleanly before I send a request.

A live probe tells you the current endpoint behavior, but it does not tell you the batch total. It also consumes tokens. A ledger is offline, repeatable, and catches shape errors:

This matches the order I use: ledger first, then one live request, then batch.

`python --version`

`ledger.py`

`python ledger.py`

If I use the operator's free server option, I can run this same script on that host and avoid installing Python locally. I still treat that host as a test target, not as a guarantee of permanence or performance.

The 1:4 character heuristic is only for planning. A code-heavy or non-English input can diverge a lot.

```
print(estimate_tokens("SELECT * FROM orders WHERE status = 'pending';"))
# chars = 48 -> 12 tokens by heuristic
# a real code tokenizer might split this differently
```

That is why the ledger is not a billing oracle. It is a cheap first filter.

Add a `retry_factor`

field to `Job`

. Calculate the adjusted total when 5% of calls fail once. Which of the two fixtures still fits?

If you want a live confirmation, follow the same pattern as my earlier request-probe post: send exactly one request, record latency, and compare the billed usage with your estimate. But do that only after the ledger says the job is plausible.

Try this against your own batch shape and drop a comment with a fixture that surprised you.