Audit Your Token Burn Before a Free Model Endpoint Hosts Your Next Side Project

Why this is worth reading

This week's AI threads keep circling two themes: agents that need tool calls, and free model endpoints that behave differently once real traffic hits. Useful as those discussions are, they rarely tell you the number that matters for a small project: how many tokens do you actually send in a week? The rest of this article gives you a repeatable way to measure that from your existing logs. You'll build a small Python token auditor, compare the result against an advertised 30 million token allowance, and deploy a small canary server if a free server option is available. The goal is not to pick a vendor; it is to stop making an infrastructure decision on vibes.

Separate the offer from the assumption

The open-source MonkeyCode project advertises a free access tier with 30 million tokens and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Treat the token figure as a cap to verify against your own traffic, not as a promise that your exact payload patterns will clear. Free tiers often fail for reasons that have nothing to do with the headline token count: request size limits, tool-call schema overhead, rate limits per minute, queue time on shared hardware, or a model refusing malformed JSON after one retry. Before you point a side project at any free endpoint, the useful move is to measure your normal request shape and then run one deliberate failure fixture.

Step 1: Capture your actual request shape

The audit starts with data you probably already have. If your CLI or agent logs requests as JSONL, export the last 500 records to a file named requests.jsonl. If you don't have a log yet, add one line to your request loop:

import json
log = open("requests.jsonl", "a")
log.write(json.dumps(payload) + "\n")

Keep the file local, sanitize keys, and don't commit it. The script below only needs one field per line, and it serializes the full record with ensure_ascii=False so non-English text isn't double-counted. The sample record should include the fields you actually send: prompt, tools, history, and system.

Step 2: Count tokens from your own logs

Exact token counts differ by tokenizer, so use a stable approximation first. The cl100k_base encoding is a reasonable baseline for many current instruction models, but it is an estimate. You are looking for order-of-magnitude truth: 4,000 tokens per request vs 40,000 changes the conclusion.

Create token_audit.py:

#!/usr/bin/env python3
import argparse, json
from pathlib import Path

def counts(path):
    import tiktoken
    enc = tiktoken.get_encoding("cl100k_base")
    total = 0
    records = []
    for line in Path(path).read_text().splitlines():
        if not line.strip():
            continue
        try:
            obj = json.loads(line)
        except json.JSONDecodeError:
            continue
        n = len(enc.encode(json.dumps(obj, ensure_ascii=False)))
        records.append(n)
        total += n
    return total, records

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("path")
    args = parser.parse_args()
    total, records = counts(args.path)
    if not records:
        raise SystemExit("no JSONL rows found")
    avg = total / len(records)
    peak = max(records)
    print(f"{len(records)} requests, {total} tokens total")
    print(f"avg {avg:.0f} tokens/request, peak {peak}")

Run it with:

pip install tiktoken
python token_audit.py requests.jsonl

If the peak is more than 5x the average, a handful of large requests will define your quota use. That matters more than the average when you pick a free tier, because rate limits often trigger at the edge, not the median.

Step 3: Compare to the allowance

Take the advertised 30 million token allowance as a starting number, not a permanent SLA. If your sample covers three normal days, project:

daily_tokens = total * 30 / sampled_days
runway_days = allowance / daily_tokens

For example, 900 requests over three days with a 10,000-token average gives 3 million tokens per day, or ten days per 30 million token allowance. That is enough for a deliberate canary or a soft launch, but not for an unattended week of agent traffic. You should also account for tool schemas. A function-calling payload can easily add 500-1,000 tokens to each request even when the user prompt is short, and retries multiply that cost. If you don't have tool-call data yet, run the next step with an oversized tool list and mark the result as a planning upper bound.

Step 4: Put the evaluator on the free server

A laptop is an unreliable place to run a comparison because it sleeps, switches networks, and disappears. If your MonkeyCode account provides the advertised free server option, that gives you a stable host for a tiny evaluator. The following FastAPI server exposes one endpoint that returns token size, content length, and a failure flag for a payload. It is deliberately small enough to read in full and easy to abandon if the server option changes.

from fastapi import FastAPI, Request
from pydantic import BaseModel
import json

app = FastAPI()

class Payload(BaseModel):
    text: str
    tool_schema: dict = {}

@app.post("/audit")
async def audit(payload: Payload):
    import tiktoken
    enc = tiktoken.get_encoding("cl100k_base")
    full = json.dumps(payload.dict(), ensure_ascii=False)
    tokens = len(enc.encode(full))
    return {
        "token_estimate": tokens,
        "content_length": len(full),
        "over_8k": tokens > 8000,
    }

Deploy it with uvicorn audit_server:app --host 0.0.0.0 --port 8000, then call it from another machine. You now have a fixed, observable target for a request that your side project would actually send. This is not a benchmark; it's a consistency check that catches drift in payload size and hidden tool-call bloat.

Step 5: Run one failure fixture

After the token audit, send one malformed request and log the outcome. The minimum fixture is a tool call whose required argument is missing:

{"model": "your-model", "prompt": "run a search", "tools": [{"name": "search", "required": ["query"]}], "tool_calls": [{"name": "search", "arguments": {}}]}

What you want is a clear error inside a few seconds. What you don't want is a 40-second hang followed by an empty 200 response, because that will poison every retry loop you write later. Record the result in the same log file with a fixture field so the decision stays tied to evidence.

When you should not use this

Skip the switch if one request regularly exceeds 30,000 tokens, if your agent uses nested tool calls with long histories, or if you can't tolerate a few hours of queueing on shared hardware. Those constraints do not mean the free endpoint is bad; they mean your workload will spend more time fighting a quota than shipping. A small CLI canary or a same-day model announcement probe is a better fit. For that shape of work, the 30 million token allowance and free server option are worth a trial only after the audit says you have multiple days of runway.

The number to write down

Write down three values before you try any free endpoint: median request tokens, peak tokens, and tool-schema overhead. If you run this audit on your own logs, the useful next input for me is which field dominated the count in your case—the prompt, the tool schema, or the conversation history. That determines the next knob to keep the free tier from becoming a spike trap.