The 10M Token Budget: How to Meter Every AI Code Edit on a Free Server

You open your dashboard. The agent stopped after three hours. Ten million tokens gone. The log shows only forty-two requests.

This is the free-tier story I hear too often. Token budgets hide in context windows. A single large edit can cost fifty thousand tokens. And the free server adds its own delays. I believe most developers do not overspend willingly. They just never see the meter.

MonkeyCode offers free models and a free server for this kind of experiment. That combination lets you test an AI coding agent without a credit card. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

But free doesn't mean unmetered. You need a measurement loop. Here is a workflow that takes about twenty minutes to set up.

What You Actually Pay for in Tokens

A code review prompt carries a few parts. There is the system instruction. There is the file content. There is the diff. There is the response.

Each part costs tokens. A file with eight hundred lines consumes roughly twelve thousand tokens. A response of two hundred lines consumes eight thousand tokens.

One review can easily burn thirty thousand tokens. Do this forty times and your budget is gone. The problem is not the model. It is the unawareness.

Step 1: Build a Token Counter

You can count tokens before sending. Use tiktoken as a proxy. Here is the script I keep in every agent directory.

import tiktoken

enc = tiktoken.get_encoding("cl100k_base")

def count(text: str) -> int:
    return len(enc.encode(text))

system_prompt = "You are a senior code reviewer. Find bugs only."
diff = open("changes.diff").read()

total = count(system_prompt) + count(diff)
print(f"Estimated tokens for this request: {total}")

Save as meter.py. Run it before every agent call. This gives you a prediction. Later you can compare it with real usage.

Step 2: Make the Agent Log Its Use

Token counting before the request is a prediction. The real number appears in the response meta. Most agent frameworks expose usage in the result. Wrap that call with a small logger.

import json, datetime

def log_usage(usage: dict):
    record = {
        "ts": datetime.datetime.utcnow().isoformat(),
        "prompt_tokens": usage.get("prompt_tokens"),
        "completion_tokens": usage.get("completion_tokens"),
        "total_tokens": usage.get("total_tokens"),
    }
    with open("usage.log", "a") as f:
        f.write(json.dumps(record) + "\n")

Put this after every model call inside MonkeyCode. Check the code path in the open-source repository to find the exact place. The repository is public. You can read it.

Step 3: Run a Review on the Free Server

Use the free server to run a batch review. Keep the task small. One file, one diff. Create a test file with three deliberate bugs.

function sum(a, b) {
  return a + b + 1; // bug one
}

const total = sum("3", 4); // bug two
console.log(total.toFixed(2)); // bug three

Send this diff to the agent with a strict prompt. The free model should catch all three. If it doesn't, inspect your prompt. A shorter prompt usually works better on a free model.

Step 4: Watch the Numbers

Run the meter, the agent, the logger. Now you get a picture. Here is a sample log from a real session on a free server:

{"ts":"2026-08-31T09:04:11Z","prompt_tokens":10520,"completion_tokens":2041,"total_tokens":12561}
{"ts":"2026-08-31T09:06:33Z","prompt_tokens":10830,"completion_tokens":1877,"total_tokens":12707}

Each call costs about twelve thousand tokens. Your ten-million budget covers eight hundred such calls. That sounds like plenty, but a full refactor can use twenty calls per hour.

What the Numbers Mean

Look at the ratio. In the log, prompt tokens are five times larger than completion tokens. That is normal. The model reads your whole file before it writes one line.

To reduce costs, shrink the input. Send only the changed function. Use git diff -U5 to limit context lines.

git diff --unified=5 -- legacy.js > small.diff

Smaller diff, smaller prompt, lower cost. The free allowance lasts longer.

The Decision Table for Free-Tier AI Coding

Use this table before you point an agent at a task.

Task type	Free models + free server?	Why
One-file lint fix	Yes	Small context, quick retry
PR review under 500 lines	Yes	Diff fits in context
Large refactor (many files)	No	Token burn too high
Continuous integration	No	Free server may be evicted
Sensitive data handling	No	Shared infra, no SLA
Teaching / experimenting	Yes	Low cost, repeatable

The Real Cost of Free

Let me be plain. The free server is shared. Load varies. A neighbor can slow your job to a crawl. You cannot rely on uptime. The free models are capable, but they have limits. They can produce wrong code. They can be slow. They do not replace a human reviewer.

Still, for learning and for small tasks, the combination works. You just have to meter everything. A free server is a sandbox, not a production machine.

How to Make the Free Tier Last

A few habits keep your budget alive.

First, trim the diff. Send only the changed function, not the whole file. That alone cuts your token use by half.

Second, set a hard token cap. If a response exceeds your limit, it should be truncated. Most agent libraries support a max_tokens setting.

Third, run one task at a time. Parallel agents multiply burn. Sequential works better on a free server.

Fourth, cache the system prompt. Some providers charge for prompt tokens every time. If you can reuse a cached prompt, your cost drops.

When You Should Pay

I use free models daily for small reviews. For a multi-day migration, I pay for a dedicated server. The decision is simple: if losing the server costs me more than the server price, I pay. The same logic applies to tokens. A free allowance is a sandbox. Production work deserves a contract.

A Clean Workflow, Not a Leap of Faith

You do not need a dashboard screenshot. You need a log you can read.

Clone MonkeyCode, add the meter, run one task, and watch the numbers. That is the whole method.

Try it on a small repo this week. The free models and the free server will tell you the truth about your workflow. What you do next should depend on that truth, not on marketing words.

That is the only CTA I care about.