Your Free Token Allowance Is a Random Variable

Every token budget you have ever set is a guess dressed as a number. The free 10-million-token allowance on a project like MonkeyCode sounds precise, but the requests you send are not, and the gap between the two is where production surprises come from. Treating token consumption as a random variable instead of a constant changes how you size budgets, set alarms, and decide when to switch providers.

MonkeyCode, an open-source project, offers a free server option and free model access, with a token allowance of 10 million as of this writing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The allowance is generous enough for weeks of light traffic, but only if your consumption estimate has any relationship to reality.

Last week I watched a proxy service burn through its daily allowance in four hours instead of the projected twelve. The average request consumed 812 tokens, which was exactly what the eval predicted. The problem was the distribution: most requests used 600 to 700 tokens, but a small number of long-context requests used 2,000 or more. The mean looked fine, and the tail ate the budget.

The fix is to stop planning around the mean and start planning around percentiles. Measure the 90th percentile of token consumption per request, then multiply by your expected request volume. If P90 is 1,100 tokens and you expect 8,000 requests per day, you need 8.8 million tokens, not the 6.5 million the average would suggest.

import numpy as np

# Replace this with real measurements from your logs
token_counts = np.array([812, 645, 902, 2104, 738, 1150, 689, 1870])

p90 = np.percentile(token_counts, 90)
p95 = np.percentile(token_counts, 95)
mean = token_counts.mean()

print(f"mean: {mean:.0f}, p90: {p90:.0f}, p95: {p95:.0f}")

Static percentiles age quickly because prompts drift as you iterate. An exponential moving average with a short window, say 100 requests, tracks the current distribution without keeping the whole history in memory.

class TokenTracker:
    def __init__(self, alpha=0.05):
        self.alpha = alpha
        self.ema = None
        self.samples = []

    def update(self, token_count):
        self.samples.append(token_count)
        if len(self.samples) > 100:
            self.samples.pop(0)
        if self.ema is None:
            self.ema = token_count
        else:
            self.ema = (1 - self.alpha) * self.ema + self.alpha * token_count

    def p90(self):
        if len(self.samples) < 10:
            return None
        return float(np.percentile(self.samples, 90))

The tracker gives you two signals: the EMA tells you where the center is moving, and the rolling P90 tells you how much headroom remains before the tail hits your ceiling. When P90 starts climbing faster than EMA, your prompts are getting longer or your context windows are filling up, and that is the moment to trim system prompts, not the moment after the 429s arrive.

MonkeyCode's free tier is a good place to practice this because the allowance is large enough to generate meaningful statistics without a credit card. Run a load test, record the distribution, and you will know within a day whether the free allowance fits your traffic pattern. The free server option makes the experiment cheap, and the open-source nature of the project means you can read the token-counting code yourself instead of trusting a dashboard.

Who should not use this approach? Anyone with fewer than a few hundred requests per day, because the sample size is too small for percentiles to mean anything. Also, if your token consumption is dominated by a single request type, the distribution is bimodal and the mean is meaningless in a different way. In that case, split your tracking by request type before you compute any statistics.

There is one more trap worth naming: token counts from different providers are not comparable. A model that uses byte-pair encoding may report fewer tokens than a character-based tokenizer for the same string, so your P90 from one endpoint is not a safe budget for another. Always measure against the actual model you plan to run in production.

Next time you set a token budget, ask for the distribution, not the average. The free allowance will thank you, and so will your on-call rotation.