Dear Past Me: Never Start a Free AI Stack Without a Token Audit

Dear past me,

You just found a free model tier and a free server. You feel unstoppable. Stop.

This week, developers have been arguing about AI memory and trust. The model remembers too much, or it trusts bad context. I think the deeper issue is auditability. If you cannot trace a run, you cannot trust it.

I want this letter to reach you before the first batch job. MonkeyCode is an open-source project. Its free tier advertises a 10M-token allowance and a free server option. That combination is useful. Without logs, it is a black box.

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

Here are the three mistakes I would unmake. Each one cost me a productive day.

Mistake 1: The first success made me stop counting

A single polished response looks like proof. It is a sample of size one. The next call can consume twice the tokens for half the quality. A retry can fail silently. You will not see any of this if you only watch the final message.

I used to trust the last printed answer. The trail between intent and output carried the real information.

Fix: wrap every API call in a usage logger before any loop starts. The wrapper below writes one CSV row per call.

# usage_log.py
import csv
import datetime
import os
import time
from openai import OpenAI

client = OpenAI(
    base_url=os.getenv('LLM_BASE_URL', 'https://provider.example/v1'),
    api_key=os.getenv('LLM_API_KEY', 'replace-me'),
)

def call_llm(system: str, user: str):
    started = time.perf_counter()
    response = None
    row = {'prompt_tokens': -1, 'completion_tokens': -1, 'error': ''}
    try:
        response = client.chat.completions.create(
            model=os.getenv('LLM_MODEL', 'your-free-model'),
            messages=[
                {'role': 'system', 'content': system},
                {'role': 'user', 'content': user},
            ],
        )
        usage = getattr(response, 'usage', None)
        if usage:
            row['prompt_tokens'] = usage.prompt_tokens
            row['completion_tokens'] = usage.completion_tokens
    except Exception as exc:
        row['error'] = str(exc)[:200]

    row['elapsed_ms'] = round((time.perf_counter() - started) * 1000, 2)
    row['ts'] = datetime.datetime.now(datetime.timezone.utc).isoformat()

    write_header = not os.path.exists('usage.csv') or os.path.getsize('usage.csv') == 0
    with open('usage.csv', 'a', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=list(row))
        if write_header:
            writer.writeheader()
        writer.writerow(row)

    return response

Now every call leaves an audit trace. That is the minimum requirement for a free AI stack.

Mistake 2: The free server became an infinite loop

A free server removes the billing wall. It does not remove the token budget. It does not remove rate limits. My old self treated each request like a local function call. A simple retry block became a 40-minute spin.

Fix: add a budget guard that reads the CSV before starting expensive work.

# budget_guard.py
import csv
import os
import sys

ALLOWANCE = 10_000_000
BUFFER = 0.8

def spent_tokens() -> int:
    if not os.path.exists('usage.csv'):
        return 0
    used = 0
    with open('usage.csv', newline='', encoding='utf-8') as f:
        for row in csv.DictReader(f):
            if row['error']:
                continue
            used += int(row['prompt_tokens']) + int(row['completion_tokens'])
    return used

used = spent_tokens()
if used > ALLOWANCE * BUFFER:
    sys.exit(f'Stop. Used {used} tokens, over {BUFFER:.0%} of allowance.')
print(f'OK: used {used} tokens.')

The guard stops the job at 80% of the allowance. This is not a benchmark. It is a seatbelt.

Mistake 3: I judged the agent by its last message

A final answer can hide seven wrong branches. The agent may have searched, guessed, failed, retried, and landed on a lucky output. The final answer will not show that. A step log will.

Fix: log each significant step before the model call.

# step_log.py
import json

def log_step(name: str, summary: str, ok: bool, detail: str = ''):
    with open('steps.jsonl', 'a', encoding='utf-8') as f:
        f.write(json.dumps({
            'name': name,
            'summary': summary[:200],
            'ok': ok,
            'detail': detail[:300],
        }, ensure_ascii=False) + '\n')

After the run, count step names by frequency:

jq -r .name steps.jsonl | sort | uniq -c | sort -rn

If one step appears more times than the task needs, prompt design or tool selection is leaking tokens.

The four numbers I track now

The real lesson is not one metric. It is the relationship between four numbers.

Number	What it tells you	Source
Tokens per task	Prompt cost and scope creep	usage.csv
Error rate	Free-tier reliability	error column in usage.csv
Retry ratio	Loop behavior	steps.jsonl
95th percentile latency	Perceived speed	elapsed_ms column

These numbers turn a vague feeling about cost into a reviewable report.

The workflow I would start with today

1. Pick one narrow task: validate a JSON file or summarize a closed issue.
2. Run three seed inputs while the usage logger is active.
3. Set a hard stop at 30 minutes or 500k tokens, whichever comes first.
4. Inspect usage.csv and steps.jsonl before editing any prompt.
5. Widen the scope only after the step count stays flat.

This workflow is deliberately boring. That is its value. It converts guesswork into four visible numbers.

Limitations

This is not a benchmark. It measures cost and flow, not answer quality. The examples assume an OpenAI-compatible usage response. If the provider omits usage data, every -1 in the CSV is a warning sign.

MonkeyCode's free tier is an advertised offer. Quotas, rate limits, and server availability can change. Read the current docs before planning real work around it.

Who should avoid this approach

Skip this workflow if you need an SLA, process healthcare data, or run paid customer traffic. Then you need a legal review and a support contract, not a CSV.

If you still want to test a free tier, run a small pilot with this guard. The logs will tell you within an hour whether the stack earns its place.