Quota Exhaustion Date: A Capacity Calculator for Free Model Quotas

A team deploys on a free tier. Day one, everything works. Day twelve, the quota is gone. Users see errors. The team checks the dashboard. They never measured their consumption.

Free model quotas are not infinite. Ten million tokens is a capacity budget. Like any budget, it needs accounting. This article builds a workload calculator that turns token consumption into a concrete capacity number.

MonkeyCode offers a free server and 10 million free tokens. Those are the claims under test here. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Token Consumption Is Not Uniform

Every workload has a different token profile. A summarization task averages 800 tokens. A classification task uses 400. Code generation runs 1,500 or more. Averages hide the spread.

The workload mix decides how long a quota lasts. Daily requests times tokens per task. That is daily consumption. Divide ten million by that. That is the exhaustion date.

Most teams skip this step. They deploy, then they wonder.

The Workload Calculator

The calculator is a Python script. It takes a JSON config file. It outputs daily consumption and a projected exhaustion date.

# capacity_calculator.py
import json
import sys
from datetime import date, timedelta

TOKEN_GRANT = 10_000_000

def load_workload(path):
    with open(path) as f:
        return json.load(f)

def calculate(workload):
    daily_tokens = 0
    rows = []
    for task in workload["tasks"]:
        tokens_per_task = task["avg_tokens_per_task"]
        requests_per_day = task["requests_per_day"]
        daily = tokens_per_task * requests_per_day
        daily_tokens += daily
        rows.append({
            "name": task["name"],
            "tokens_per_task": tokens_per_task,
            "requests_per_day": requests_per_day,
            "daily_tokens": daily,
        })
    return rows, daily_tokens

def main():
    workload = load_workload(sys.argv[1])
    rows, daily_tokens = calculate(workload)

    print("Per-task daily consumption:")
    for row in rows:
        print(f"  {row['name']}: {row['daily_tokens']:,} tokens/day")

    print(f"\nTotal: {daily_tokens:,} tokens/day")
    if daily_tokens == 0:
        print("No consumption. Check your workload config.")
        return

    days = TOKEN_GRANT / daily_tokens
    exhaustion = date.today() + timedelta(days=days)
    print(f"Grant: {TOKEN_GRANT:,} tokens")
    print(f"Quota exhaustion: ~{days:.1f} days ({exhaustion.isoformat()})")

    retry_rate = workload.get("retry_rate", 0.1)
    effective_daily = daily_tokens * (1 + retry_rate)
    effective_days = TOKEN_GRANT / effective_daily
    print(f"With {retry_rate:.0%} retry overhead: ~{effective_days:.1f} days")

if __name__ == "__main__":
    main()

The Workload Config File

The config file describes your tasks. Each task has three fields: name, average tokens per task, and requests per day.

{
  "retry_rate": 0.1,
  "tasks": [
    {
      "name": "email_summary",
      "avg_tokens_per_task": 800,
      "requests_per_day": 2000
    },
    {
      "name": "ticket_classification",
      "avg_tokens_per_task": 400,
      "requests_per_day": 5000
    },
    {
      "name": "code_review_comment",
      "avg_tokens_per_task": 1500,
      "requests_per_day": 300
    }
  ]
}

Run it:

python capacity_calculator.py workload.json

Example output:

Per-task daily consumption:
  email_summary: 1,600,000 tokens/day
  ticket_classification: 2,000,000 tokens/day
  code_review_comment: 450,000 tokens/day

Total: 4,050,000 tokens/day
Grant: 10,000,000 tokens
Quota exhaustion: ~2.5 days (2026-08-24)
With 10% retry overhead: ~2.2 days

This workload burns the quota in two and a half days. The team needs to know that on day one, not day twelve.

Reading the Numbers

The calculator outputs a date. That date drives your action.

Exhaustion horizon	Verdict	Action
More than 30 days	Comfortable	No immediate action
7-30 days	Cautious	Monitor weekly consumption
2-7 days	Dangerous	Cut requests or add caching
Less than 2 days	Critical	Pause non-critical workloads

Thresholds depend on your business. The point is the calculator forces a choice.

Measuring Real Consumption

The config file numbers are estimates. Real consumption comes from production. Log token usage per request. Aggregate weekly.

# token_logger.py
import sqlite3
import time

def init_db():
    conn = sqlite3.connect("token_usage.db")
    conn.execute("""
        CREATE TABLE IF NOT EXISTS usage (
            id INTEGER PRIMARY KEY,
            task_name TEXT,
            prompt_tokens INTEGER,
            completion_tokens INTEGER,
            total_tokens INTEGER,
            timestamp REAL
        )
    """)
    return conn

def log_usage(conn, task_name, usage):
    conn.execute(
        "INSERT INTO usage (task_name, prompt_tokens, completion_tokens, total_tokens, timestamp) VALUES (?, ?, ?, ?, ?)",
        (task_name, usage.prompt_tokens, usage.completion_tokens, usage.total_tokens, time.time()),
    )
    conn.commit()

def daily_report(conn):
    cursor = conn.execute("""
        SELECT task_name, 
               COUNT(*) as requests,
               AVG(total_tokens) as avg_tokens,
               SUM(total_tokens) as daily_total
        FROM usage
        WHERE timestamp > ?
        GROUP BY task_name
    """, (time.time() - 86400,))
    return cursor.fetchall()

This logger is template code. Insert log_usage calls into your model client. Run daily_report weekly. Feed the real numbers back into the calculator.

Budget Alerts

Exhaustion dates move. Workloads grow. Request volumes spike. Set an alert so you hear about the quota before it dies.

#!/bin/bash
# check_quota.sh
# Run daily via cron. Requires jq and bc.

USAGE_URL="${USAGE_URL:-https://your-endpoint.example/v1/usage}"
THRESHOLD_PCT="${THRESHOLD_PCT:-80}"

usage_json=$(curl -s "$USAGE_URL")
used=$(echo "$usage_json" | jq '.total_used_tokens')
grant=$(echo "$usage_json" | jq '.total_grant_tokens')

pct=$(echo "scale=2; $used * 100 / $grant" | bc)
echo "Used: $used / $grant ($pct%)"

if (( $(echo "$pct > $THRESHOLD_PCT" | bc -l) )); then
    echo "WARNING: Token usage above ${THRESHOLD_PCT}%" | mail -s "Token quota alert" [e-mail]
fi

The URL is a placeholder. Replace it with your actual usage endpoint. Or read from the log database.

When the Quota Runs Out

Quota exhaustion is not the end of the world. It is a planned event.

• Degrade to a smaller model.
• Queue non-critical tasks to the next cycle.
• Switch to paid access.
• Notify users of a maintenance window.

The key is making these decisions before exhaustion. The calculator gives you the timeline.

Who Should Not Use This Approach

Capacity planning assumes predictability. Some teams do not have it.

• Teams with strict latency SLAs. Free tiers are shared. Latency varies.
• Teams with bursty workloads. Spikes burn quotas fast.
• Teams handling regulated data. Data residency matters.
• Teams that cannot tolerate retries. Retries amplify consumption.

The calculator will tell you if you are in one of these buckets. That is a valid conclusion.

The Bottom Line

Free model quotas are capacity budgets. Like any budget, they need accounting. The calculator turns ten million tokens into a date. The date drives your actions.

Run the calculator against MonkeyCode's free tier. Measure your real consumption. Know the exhaustion date before the quota knows you. That is the cheapest insurance for free access.