Your Team Shares One Free Model Tier: A Ledger and a One-Page Run to Keep It From Vanishing

You give four teammates access to one shared free model tier on a Monday, and by Wednesday the drafts start failing with a rate-limit error that names nobody. Nobody changed a password, nobody deployed anything, and yet the tier is gone. The failure is not technical at all: shared free capacity has no per-person meter, no visible ceiling, and no owner who notices the trend before it hits zero. This article gives you a small ledger, a dependency-free burn-down script, and a one-page run you can paste into your wiki this afternoon.

Why shared free tiers fail quietly

Free tiers are usually provisioned as one credential for the whole team, which means the provider sees a single noisy consumer instead of five distinct ones. Usage rises smoothly until it does not, and the first signal you get is a hard failure in the middle of somebody's afternoon. Because there is no invoice, there is also no weekly moment where anyone looks at the number. The fix is organizational before it is technical: you need a written meter, a threshold, and a rotating custodian.

Step 1: Define the ledger schema before you invite anyone

Start with a plain CSV that any teammate can append to from a terminal, a spreadsheet export, or a script. Keep the columns boring and stable, because the value of the ledger comes from consistency across weeks rather than from clever tooling. The purpose and ticket fields exist so a future reader can tell whether heavy usage was justified. Commit this file next to your runbook so it travels with the team.

date,actor,workload,model_class,tokens_in,tokens_out,purpose,ticket
2026-10-06,priya,docs-rewrite,general,18000,4200,release-notes,DOC-114
2026-10-06,sam,test-gen,general,42000,9600,flaky-suite,TEST-88
2026-10-07,priya,triage-bot,small,5200,900,issue-labels,OPS-7
2026-10-07,lena,spec-draft,general,61000,14000,api-v2-design,ARCH-31

Step 2: Run a burn-down check that prints a wiki block

The script below is a proposal you can verify locally in about a minute, and it uses only the standard library. It reads the ledger, slices the last seven days, and prints a Markdown table with a share flag for any actor above forty percent. Run it from cron, a Make target, or by hand on Friday, and paste the output straight into your runbook page.

#!/usr/bin/env python3
"""ledger_check.py - burn-down summary for a shared model tier.

Proposed artifact: reads a local CSV ledger, prints a wiki-ready block.
Standard library only; no network calls and no product API involved.
"""
from __future__ import annotations

import argparse
import csv
from collections import defaultdict
from datetime import date, timedelta

WINDOW_DAYS = 7
SHARE_WARN = 0.40


def load(path: str) -> list[dict]:
    with open(path, newline="", encoding="utf-8") as fh:
        rows = list(csv.DictReader(fh))
    for row in rows:
        row["spend"] = int(row["tokens_in"]) + int(row["tokens_out"])
        row["day"] = date.fromisoformat(row["date"])
    return rows


def window_slice(rows: list[dict], today: date) -> list[dict]:
    start = today - timedelta(days=WINDOW_DAYS - 1)
    return [r for r in rows if start <= r["day"] <= today]


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--ledger", default="ledger.csv")
    ap.add_argument("--today", default=date.today().isoformat())
    args = ap.parse_args()

    today = date.fromisoformat(args.today)
    window = window_slice(load(args.ledger), today)
    if not window:
        print(f"No ledger rows in the last {WINDOW_DAYS} days. Nothing to report.")
        return 0

    total = sum(r["spend"] for r in window)
    by_actor: dict[str, int] = defaultdict(int)
    by_workload: dict[str, int] = defaultdict(int)
    for r in window:
        by_actor[r["actor"]] += r["spend"]
        by_workload[r["workload"]] += r["spend"]

    print(f"### Shared tier burn-down ({WINDOW_DAYS}d to {today})")
    print(f"Window spend: {total:,} token units across {len(window)} entries\n")
    print("| actor | tokens | share | flag |")
    print("| --- | ---: | ---: | --- |")
    for actor, spend in sorted(by_actor.items(), key=lambda kv: -kv[1]):
        share = spend / total
        flag = "over share" if share > SHARE_WARN else "ok"
        print(f"| {actor} | {spend:,} | {share:.0%} | {flag} |")

    top = max(by_workload.items(), key=lambda kv: kv[1])
    print(f"\nTop workload: `{top[0]}` at {top[1]:,} token units.")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

$ python3 ledger_check.py --ledger ledger.csv --today 2026-10-07
### Shared tier burn-down (7d to 2026-10-07)
Window spend: 154,900 token units across 4 entries

| actor | tokens | share | flag |
| --- | ---: | ---: | --- |
| lena | 75,000 | 48% | over share |
| sam | 51,600 | 33% | ok |
| priya | 28,300 | 18% | ok |

Top workload: `spec-draft` at 75,000 token units.

Step 3: Make the custodian a weekly rotation, not a permanent role

A permanent quota owner becomes a bottleneck, and a permanent quota auditor becomes resented. Rotate the job weekly across the people who actually use the tier, so everyone learns how fast it drains. The custodian's whole job is three lines: update the ledger, run the check, and write one sentence about the largest consumer. Hand the role over with a name and a date, never with "whoever is around".

Step 4: Decide in advance who yields when the tier runs low

Arguments about shared capacity are always worse when they happen live, so write the rule down before you need it. The principle is simple: the largest recent consumer yields first, and the custodian decides whether a workload moves or simply pauses. That is fairer than first-come-first-served, and it is easy to justify because the ledger makes the ranking public.

Situation	Who yields first	Recorded action
Rate-limit errors appear mid-day	Largest 7-day consumer	Pause batch jobs until the window rolls
One actor above the share threshold	That actor	Move heavy runs to off-peak hours
Ledger has gaps for two weeks	Custodian	Freeze new invites until entries resume
A run fails with no ticket reference	The requester	Re-run only after adding a purpose line

Step 5: Paste the one-page run into your wiki

Keep the run short enough that a new teammate reads it in ninety seconds, and link the ledger and the script rather than embedding them. Ask every custodian to overwrite the same block each week, so the page shows current state instead of a growing archive. The block below is the whole playbook, and it fits in a single screen.

## Shared model tier run (custodian: <name>, week of <date>)
- Ledger freshness: last entry <date> (stale if > 7 days)
- 7-day spend: <token units> / stated limit: unknown -> check project terms page
- Actors over share threshold: <names or none>
- Top workload this week: <workload name>
- Decision taken: <none / pause / re-schedule / escalate>
- Handoff: <name> -> <name> on <date>

Where MonkeyCode changes the practical math

Two operator-stated facts matter for this workflow: the project offers free model access and a free server option. Free model access lowers the barrier for the teammate who only needs occasional drafts, so you can add a fifth contributor without a procurement conversation. A free server option means the weekly check does not have to live on somebody's laptop cron, which is the usual reason these ledgers quietly stop updating after three weeks. I am describing the two availability claims as the operator supplied them, and I am deliberately not quoting quotas or hardware here, because those terms change and a blog post is a bad place to read them. Check the project's current terms page before you promise anyone a number.

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

Treat the tier as a shared workshop resource rather than a metered utility, and it will teach your team something useful about how agent workloads actually consume capacity. If you want to stand the ledger up against real work instead of a toy CSV, the free model access and free server option are the cheapest way to try it; just confirm the current limits first.

Limitations and who should skip this

This ledger only counts what people record, so an unlogged script can drain the tier invisibly until someone notices the failures. Token units also measure consumption rather than value, and the teammate burning the most capacity may be doing the highest-value work. Teams with real billing, per-seat accounts, or provider-side usage reports should use those instead, since they are authoritative and require no manual discipline. Skip this approach entirely if your organization cannot ask people to log usage honestly, because a ledger nobody trusts is worse than no ledger at all.