Your Free AI Server Has a Load Ceiling. Probe It.

Free tokens get the headlines. The free server is where the promise actually breaks. Load-test it before you trust it with real work.

Every week a new model drops. Every week another free tier appears. The hot take is that AI lets you write less code. The cold truth is that your free server decides whether that code ever runs. Teams onboard fast, then hit a wall they never measured. The wall is rarely the token count. It is the server behind the tokens.

One project worth probing is MonkeyCode. It is an open source AI coding assistant. It offers free model access with a 10M token budget. It also offers a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I treat those offers as claims, not facts. My job is to design a test that shows what they mean in practice.

The question is not whether the server works. The question is under what load it stops working. I built a probe that answers that. It measures four things: cold start, latency percentiles, failure rate, and token burn per task.

Think of a free server like a shared office kitchen. Free tokens are the menu. The free server is the kitchen. You can read the menu all day. The kitchen decides whether you actually eat. At 9 AM it is empty. At 3 PM everyone wants coffee at once. The same kitchen behaves differently at different hours. You need numbers, not vibes.

Here is the probe. It is one Python file. It sends streamed requests to any OpenAI-compatible endpoint. It records time-to-first-token, total latency, and failures under controlled concurrency.

#!/usr/bin/env python3
"""probe_free_server.py - find the load ceiling of a free AI server."""
import argparse
import asyncio
import statistics
import time

import httpx


async def one_call(client, url, payload, semaphore):
    async with semaphore:
        start = time.perf_counter()
        first_token = None
        chunks = 0
        try:
            async with client.stream("POST", url, json=payload, timeout=60) as response:
                async for line in response.aiter_lines():
                    if line.startswith("data:") and first_token is None:
                        first_token = time.perf_counter() - start
                    chunks += 1
            return {"ok": True, "ttft": first_token,
                    "total": time.perf_counter() - start, "chunks": chunks}
        except Exception as exc:
            return {"ok": False, "error": type(exc).__name__,
                    "total": time.perf_counter() - start}


async def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--url", required=True)
    parser.add_argument("--prompt", default="Write a Python function that parses CSV without the csv module.")
    parser.add_argument("--concurrency", type=int, default=2)
    parser.add_argument("--requests", type=int, default=10)
    args = parser.parse_args()

    semaphore = asyncio.Semaphore(args.concurrency)
    payload = {"messages": [{"role": "user", "content": args.prompt}], "stream": True}

    async with httpx.AsyncClient() as client:
        results = await asyncio.gather(
            *[one_call(client, args.url, payload, semaphore)
              for _ in range(args.requests)]
        )

    ok = [r for r in results if r["ok"]]
    failed = [r for r in results if not r["ok"]]
    totals = sorted(r["total"] for r in ok)
    ttfts = sorted(r["ttft"] for r in ok if r["ttft"] is not None)

    print(f"ok={len(ok)} failed={len(failed)}")
    if totals:
        p95 = totals[min(len(totals) - 1, int(len(totals) * 0.95))]
        print(f"total latency  p50={statistics.median(totals):.2f}s  p95={p95:.2f}s")
    if ttfts:
        p95 = ttfts[min(len(ttfts) - 1, int(len(ttfts) * 0.95))]
        print(f"first token    p50={statistics.median(ttfts):.2f}s  p95={p95:.2f}s")
    if failed:
        modes = sorted({r["error"] for r in failed})
        print("failure modes:", ", ".join(modes))


if __name__ == "__main__":
    asyncio.run(main())

Run it like this:

pip install httpx
python probe_free_server.py \
  --url https://your-endpoint.example/v1/chat/completions \
  --concurrency 2 \
  --requests 10

Start at concurrency one. Record the baseline. Double the concurrency until failures appear. That doubling is the whole experiment. The shape of the curve tells you more than any single number.

Interpret with plain thresholds. A p95 time-to-first-token under two seconds is fine for interactive coding. Between two and ten seconds, the server is queueing your requests. Above ten seconds, the free tier has become a waiting room. A failure rate above five percent at your working concurrency means the ceiling is below your needs.

Cold start deserves its own measurement. Send one request. Wait five minutes. Send another. The gap between call and first byte is your cold start tax. A single curl gives you that number:

time curl -N -X POST "$URL" \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"ping"}],"stream":true}' \
  -o /dev/null

Then measure token burn. Most responses include usage metadata. Record it per task type. A one-function fix burns far fewer tokens than a multi-file refactor. A tiny ledger turns the budget into a plan:

usage = response.json().get("usage", {})
ledger[task_type] += usage.get("total_tokens", 0)

The failure pattern is predictable. Every shared free server degrades the same way. Low concurrency is smooth. Then latency climbs. Then requests time out. Then connections drop. The knee of that curve is your real limit. Measure it for your workload, from your network. My numbers would be noise for your setup. Your numbers are the signal.

This probe has limits. It tests one endpoint from one network. Your geography changes every result. It does not test correctness. A fast wrong answer is still wrong. It does not test long sessions or context retention. Those need a separate harness.

Who should skip this approach? Teams with production traffic. Products with a latency SLA. Anything user-facing where a five-second pause is a bug. Free servers are for experiments, prototypes, and bursty personal work. Treat them as such.

The MonkeyCode free server is there for you to probe. Run this harness against it. The numbers will tell you whether it earns a place in your stack. Measure first. Decide second. That is the whole trick.