Free quotas feel infinite until they are not. A 10-million-token allocation sounds generous. In practice, it disappears faster than most developers expect. The cause is rarely heavy usage alone. It is invisible consumption.
This article provides a measurement framework for AI coding budgets. The working example uses MonkeyCode's free model access and free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Why token accounting matters
Most developers cannot name their daily token consumption. They see a quota number and assume it is enough. Then the quota runs out mid-sprint. The result is an unplanned stop.
Token accounting changes that. It turns a vague quota into a predictable budget. You can see which tasks consume the most. You can forecast when the quota will end. You can optimize before the stop happens.
The framework below is tool-agnostic. It works with any AI coding assistant that reports token usage. MonkeyCode is used here because its free tier makes measurement practical.
The measurement setup
The first step is a token ledger. This script records every AI call to a JSONL file. It captures task type, token counts, and success status.
#!/usr/bin/env python3
"""Track token usage for AI coding sessions."""
import json
import time
from pathlib import Path
class TokenLedger:
def __init__(self, log_path="token_usage.jsonl"):
self.log_path = Path(log_path)
self.session_id = str(int(time.time()))
def record(self, task_type, prompt_tokens, completion_tokens, model, ok=True):
entry = {
"session_id": self.session_id,
"timestamp": time.time(),
"task_type": task_type,
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
"model": model,
"ok": ok,
}
with open(self.log_path, "a") as f:
f.write(json.dumps(entry) + "\n")
def summary(self):
if not self.log_path.exists():
return {"total_tokens": 0, "by_type": {}}
total = 0
by_type = {}
with open(self.log_path) as f:
for line in f:
entry = json.loads(line)
total += entry["total_tokens"]
by_type[entry["task_type"]] = by_type.get(entry["task_type"], 0) + entry["total_tokens"]
return {"total_tokens": total, "by_type": by_type}
Integrate this into your workflow. After each AI response, extract token counts from the response metadata. Call record() with the task type. Run summary() at the end of the day.
The ledger gives you three numbers: total consumption, consumption by task type, and success rate. These three numbers drive every optimization decision.
Breaking down consumption by task
Not all AI tasks consume tokens equally. A code generation call is expensive. A small edit is cheap. A debugging conversation is the most expensive of all, because it involves multiple round trips.
| Task type | Typical prompt tokens | Typical completion tokens | Round trips |
|---|---|---|---|
| Code generation | 800-2,000 | 300-1,000 | 1-2 |
| Edit/refactor | 400-1,200 | 100-500 | 1-3 |
| Debugging | 1,000-3,000 | 200-800 | 3-10 |
| Code review | 500-1,500 | 300-900 | 1-2 |
| Q&A/explanation | 200-800 | 100-500 | 1-3 |
These are order-of-magnitude estimates. Your actual numbers depend on your codebase and prompt style. The ledger replaces these estimates with your real data.
The pattern is clear: debugging dominates. A single debugging session can consume more tokens than five code generations. The reason is context accumulation. Every round trip resends the conversation history.
Hidden token costs
The visible tokens are only part of the story. Three hidden costs inflate your consumption.
Context window fill. Every message in a conversation is resent with each new turn. A 10-turn debugging session with 2,000 tokens of history costs 20,000 tokens of context alone. The completion tokens are the smallest part of the bill.
Retries and failures. A failed call still consumes tokens. The request was sent, processed, and returned an error. If your workflow retries three times, you pay three times. Failed calls are pure waste.
Tool output and errors. When the AI reads a file, runs a command, or receives an error message, that output enters the context window. A long stack trace can add thousands of tokens. Truncate tool output before sending it to the model.
A budget planner for your quota
Once you have a ledger, you can build a forecast. The math is simple:
daily_consumption = sum(task_count * tokens_per_task for each task type)
days_until_exhaustion = remaining_quota / daily_consumption
Here is a concrete example:
def forecast(remaining_tokens, daily_tasks, tokens_per_task):
daily_total = sum(daily_tasks.get(t, 0) * tokens_per_task.get(t, 0)
for t in set(daily_tasks) | set(tokens_per_task))
if daily_total == 0:
return float("inf")
return remaining_tokens / daily_total
# Example: 10M quota, 20 generations/day at 2K, 30 edits at 600, 5 debug sessions at 3K
remaining = 10_000_000
tasks = {"generation": 20, "edit": 30, "debug": 5}
per_task = {"generation": 2000, "edit": 600, "debug": 3000}
days = forecast(remaining, tasks, per_task)
print(f"Quota lasts approximately {days:.1f} days")
Run this forecast weekly. The number will surprise you. Most developers discover their quota lasts far fewer days than expected.
Optimization strategies
The ledger reveals where to cut. Three strategies reduce consumption without reducing output quality.
Prune conversation history. Start a new session for each task. Do not carry a long conversation into a new problem. The context window is the biggest hidden cost.
Cache repeated requests. If the same file is reviewed twice, the second review should not re-read the whole file. Cache the file content and send only the diff.
Batch small tasks. Instead of 10 separate edit calls, combine them into one prompt. One round trip with 10 changes costs less than 10 round trips with one change each.
Truncate tool output. Limit file reads to relevant sections. Cap stack traces at 50 lines. The model does not need the full output to understand the problem.
When the free quota is not enough
The measurement framework has a hard limit. If your daily consumption exceeds what the free quota can sustain, no optimization will save you. The decision is then about priorities.
| Situation | Recommended action |
|---|---|
| Prototyping and learning | Free tier is sufficient |
| Regular side project | Optimize first, then consider paid |
| Production CI/CD | Paid or self-hosted |
| Large refactoring campaigns | Budget explicitly or split across weeks |
The free tier is a starting point, not a destination. Measurement tells you when to move.
Limitations of this approach
The token ledger requires discipline. You must call record() after every AI interaction. Missed entries create blind spots in the data.
Token estimates vary by model and provider. The table above is a starting point, not a guarantee. Your real numbers depend on your workload.
Free quotas change. The 10-million-token allocation and free server option may be adjusted. Verify current terms in the official MonkeyCode documentation before planning a large project.
Closing
A free quota is a budget, not a gift. The difference matters. A gift is spent without thought. A budget is measured, forecast, and optimized.
Start a token ledger today. Measure for one week. The data will change how you use AI coding tools.
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