AI writes code faster than you can review it. The bottleneck shifted from authoring to verification. Verifying means running real prompts. Real prompts burn tokens.
Free quotas make experimentation cheap. But they are a hypothesis, not a guarantee. You must measure the burn rate. A 90-minute spike tells you if the free tier is a foundation or a teaser.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
Why token economics matter now
Developers are suddenly reviewers. They queue AI requests, inspect diffs, and stress edge cases. Every review attempt consumes model tokens. Most teams don't know the per-task cost until the invoice arrives.
Free tiers hide that signal. You get a big number like 10 million tokens. Is that enough for one feature, a week of reviews, or a single batch job? Nobody knows until they measure.
MonkeyCode offers free model access and a free server option. I'll use both to show you a spike. The goal is not to praise the product. It's to give you a reusable method for any AI endpoint.
The spike: one hypothesis, one measurement
Hypothesis: The free token quota covers my team's typical code-review workload for at least 30 days.
Ship-or-kill evidence comes from three steps:
- Collect 10–20 representative prompts.
- Send them to the endpoint, recording latency and token usage.
- Extrapolate to your expected monthly volume.
Time-box this to 90 minutes. You are not building a production service here. You are collecting data.
Step 1: Collect real prompts
Do not use toy examples. Look at your last week of code reviews. Pull 15 real requests: "explain this diff", "find a race condition", "suggest a refactor for this function".
Save them in a file, one per line:
Why does this deadlock? https://short.link/example1
Write a retry wrapper for this API call.
Is this query N+1? Show an alternative.
...
Step 2: Hit the endpoint
The following Python script works with any OpenAI-compatible API. Point it at MonkeyCode's free server or your local Ollama instance. It logs latency and token usage.
import time, json, requests
PROMPTS = [
"Why does this deadlock?",
"Write a retry wrapper for an HTTP API.",
"Find off-by-one errors in this snippet.",
# add 12-17 more realistic prompts
]
ENDPOINT = "http://127.0.0.1:11434/v1/chat/completions"
API_KEY = "" # MonkeyCode free endpoint may not need one
def run():
results = []
for i, p in enumerate(PROMPTS, 1):
payload = {
"model": "your-model",
"messages": [{"role": "user", "content": p}],
"max_tokens": 500,
}
start = time.time()
try:
r = requests.post(ENDPOINT, json=payload, timeout=120)
data = r.json()
usage = data.get("usage", {})
except Exception as e:
data, usage = {}, {}
print(f"Prompt {i} failed: {e}")
results.append({
"prompt": i,
"latency_ms": round((time.time() - start) * 1000, 1),
"prompt_tokens": usage.get("prompt_tokens", 0),
"completion_tokens": usage.get("completion_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
})
return results
if __name__ == "__main__":
out = run()
print(json.dumps(out, indent=2))
total = sum(x["total_tokens"] for x in out)
print(f"\nTotal tokens for {len(out)} prompts: {total}")
print(f"Avg tokens per prompt: {total // len(out)}")
if total:
print(f"10M free tokens would cover ~{10_000_000 // total} comparable runs")
Run it against one prompt first. Verify the response format. Then let it run through all prompts. That is the core of your spike.
Step 3: Interpret the numbers
Look at the output. Calculate your weekly token budget.
Example from a real test run (not ours):
| Metric | Value |
|---|---|
| Prompts | 15 |
| Total tokens | 3,412 |
| Tokens per prompt | 227 |
| Latency (p50) | 1.8s |
If your team does 200 review prompts per day, that is 45,400 tokens daily. A 10M quota lasts about 220 working days. The free tier is ample for experiments and small teams.
But if each prompt averages 5,000 tokens, the math flips. 10M tokens supports only 2,000 prompts. At 200 per day, that's 10 days. Suddenly you need a paid plan or aggressive caching.
Decision matrix
Use this table to guide your ship-or-kill call.
| Quota duration | Verdict | Action |
|---|---|---|
| > 90 days | Ship on free tier | Keep monitoring weekly |
| 30–90 days | Conditional | Cache common prompts, cap max_tokens |
| < 30 days | Kill for prod | Design a cheaper prompt or pay per use |
The spike also exposes latency outliers. A request that times out at 120s will break your review flow. Track the worst-case, not just the average.
Limitations
Free tiers change. Quotas, models, and rate limits are not contractual. The 10M number is current for MonkeyCode's offering, but it can change without notice.
This script measures one endpoint configuration. Different models or max_tokens settings produce very different burn rates. Always re-run the spike after any prompt or model change.
Also, the free server may have slower startup or cold starts. My test is synthetic. Real traffic patterns can expose limits that 15 prompts won't.
Who should not use this free tier
- Teams with strict zero-data-retention policies.
- Production services that need a guaranteed SLA.
- Workloads with burst token consumption beyond the free rate limit.
For those cases, pay for a dedicated tier. The free server is excellent for spikes, prototypes, and internal tools.
Final word
A 90-minute spike converts a vague promise into a number. You learn your token burn rate, latency profile, and whether free is enough. The method works for any AI provider, not just MonkeyCode.
If you're curious about the project, MonkeyCode's open-source repo and its free server are a cheap way to run this exact test. But the real value is the habit: measure before you trust the hype.
