Morgan Xu

Posted on Aug 31

Postmortem: The Free Tier Wasn't the Problem — Our Guardrails Were

#postmortem #ai #devops

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

The free tier failed. The servers didn't. The tokens didn't. Our guardrails did.

This postmortem covers a real incident from our CI pipeline. We relied on MonkeyCode's free model access and free server option. We skipped the operational discipline. That mistake cost us a release night.

The lesson is not "free tiers are unreliable." The lesson is: free access needs stricter guardrails than paid access. Here is what broke, why it broke, and how to fix it.

The Incident Timeline

All times are UTC.

21:47

The nightly batch started. The pipeline sent 1,200 code review requests to MonkeyCode's free endpoint. No throttling was configured.

22:13

The first rate limit responses arrived. HTTP 429 mixed with HTTP 200 in the logs.

The client treated 429 as a retryable error. No backoff. No cap on attempts.

22:18

Retry storm started. Each 429 spawned 15 new requests. The queue grew exponentially.

22:36

The free server hit its memory ceiling. The process started swapping.

22:41

OOM killer terminated the server. All in-flight requests failed.

22:47

The retry loop restarted the server. Then killed it again. This loop lasted 18 minutes.

23:05

Someone disabled the retry flag manually. The pipeline drained. 30% of the batch had failed permanently.

The release went out without those reviews. We found a broken import at 02:14.

Contributing Factors

We identified five root causes:

No endpoint separation. Free and paid endpoints shared the same client. We could not apply different policies.
Unbounded retries. The default retry was infinite. It amplified every transient error.
No concurrency cap. The client fired 200 parallel requests. That is fine for a paid SLA. It is deadly for a free server.
No budget alarm. We had no token or request budget. No alert, no dashboard.
No fallback path. When free failed, the pipeline did not degrade to a smaller batch or a paid endpoint.

We treated a free resource like an infinite one. That is the real mistake.

The Durable Fix

We built a small Python client. It wraps any OpenAI-compatible endpoint with three guardrails:

A fixed-size token bucket for concurrency.
Exponential backoff with a hard retry cap.
A daily request budget.

Here is the core implementation:

import time
import threading
from datetime import date

class GuardedClient:
    def __init__(self, max_concurrency=8, max_retries=3, daily_budget=500):
        self.semaphore = threading.Semaphore(max_concurrency)
        self.max_retries = max_retries
        self.daily_budget = daily_budget
        self.used_today = 0
        self.lock = threading.Lock()
        self.today = date.today()

    def _check_budget(self):
        with self.lock:
            if date.today() != self.today:
                self.today = date.today()
                self.used_today = 0
            if self.used_today >= self.daily_budget:
                raise RuntimeError("Daily budget exhausted")
            self.used_today += 1

    def call(self, fn, *args, **kwargs):
        self._check_budget()
        with self.semaphore:
            for attempt in range(self.max_retries + 1):
                try:
                    return fn(*args, **kwargs)
                except Exception as e:
                    if attempt == self.max_retries:
                        raise
                    time.sleep(2 ** attempt)

Use max_concurrency=4 for free servers. Use max_retries=2 for free endpoints. Keep the budget low enough to survive a full day.

Decision Matrix

We now route requests by task weight. This table shows what goes to the free tier and what must go elsewhere.

Task	Use free endpoint?	Reason
Single file review	Yes	Low volume, low blast radius
Repo-wide scan	No	Too many calls, likely to throttle
One-off code explanation	Yes	Latency tolerant
CI blocking check	No	Must not depend on free tier
Batch summarization	Yes, chunked	Use small batches and long delays

If the task is on the critical path, pay for it. If it is async and idempotent, free is fine.

Validation

We benchmarked this client against MonkeyCode's free server for one week.

The test used 50 synthetic review tasks. We measured success rate, p95 latency, and budget usage.

Run the test yourself with this script:

for i in $(seq 1 50); do
  curl -s -o /dev/null -w "%{http_code}\n" \
    -X POST "$MONKEY_FREE_ENDPOINT" \
    -H "Authorization: Bearer $MONKEY_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{"prompt":"Review this function","max_tokens":200}' \
    --max-time 30
  sleep 2
done

Our results after this change:

Success rate: 94% (up from 61%)
p95 latency: 4.2s (down from 18s)
No OOM or retry storm

Your numbers will differ. The point is to measure before you trust.

Limitations

Free tiers change. Quotas shift. Servers get recycled. Our fix does not guarantee uptime.

The 10M token promotion and free server are real today. We verified that this week. But we do not assume they will exist tomorrow.

Design your pipeline so that a broken free tier degrades gracefully. Use a queue. Log failures. Alert on budget exhaustion.

Who Should Not Use This Approach

This approach is wrong for you if:

Your traffic spikes without warning
You need a strict SLA
You cannot tolerate occasional data loss

In those cases, use a paid endpoint with a guarantee. The guardrail client still helps, but it is not a replacement for a contract.

The Final Survivor

The free tier was never the enemy. Our assumptions were.

MonkeyCode's free access is a useful sandbox. It fits experimentation, low-risk tasks, and side projects. It does not fit release gates without proper constraints.

Add guardrails first. Then enjoy the free tokens.

DEV Community