Eval on Free Infrastructure: Four Myths That Waste Your Quota

Your free model + free server combo looks like a gift. It's not. It's a moving target.

I ran model evals on shared infra. The surprises weren't in the model. They were in the platform.

MonkeyCode offers a free model tier and a free server option. That sounds perfect for hobby evals. But a free tier is a queue, not a guarantee. Let's break four myths with a workflow you can copy.

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

Myth 1: Your Free Server Is Persistent

You spin up a VM. You start a long eval. You go for coffee.

Then the server restarts. Your in-memory results vanish. The model API is fine. Your progress is gone.

The fix? Persist every result after each case. One JSONL file per run. Append, flush, move on.

Myth 2: Retrying a Failed Call Is Free

Free model endpoints throttle. They time out. They return 429 or 503.

Your first instinct is a while loop with retry(). Don't. Each attempt consumes your model budget. A naive retry turns one unclear case into ten billable calls.

Use a budget guard instead. Stop when you hit your limit. Log the failure, resume later.

Myth 3: One Run Is Enough

Free servers share CPU. Latency spikes come and go. A single eval run gives you a lucky p50, not a stable answer.

Run the same eval at least three times. Store the raw timings. Compare distribution, not the average.

Myth 4: Free Means Zero Cost Learning

You still pay with your time. Debugging a crashed eval at 2 AM is a cost. Writing a restart script is an investment that pays back.

The fix is a resumable runner. Here's a minimal one.

Artifact: A Resumable Eval Runner With Budget Guard

Copy this into a file named eval_runner.py.

import json
import os
import time
from pathlib import Path

BUDGET = int(os.getenv("EVAL_BUDGET_CALLS", "50"))
CHECKPOINT = Path(os.getenv("EVAL_CHECKPOINT", "./evals.jsonl"))


def load_done():
    """Return the set of case IDs already processed."""
    if not CHECKPOINT.exists():
        return set()
    return {json.loads(line)["id"] for line in CHECKPOINT.open()}


def run_case(case_id):
    """Replace this with your real model call."""
    time.sleep(0.1)
    return {"id": case_id, "score": 1}


def main(cases):
    done = load_done()
    used = 0

    with CHECKPOINT.open("a") as fh:
        for case in cases:
            if case["id"] in done:
                continue

            if used >= BUDGET:
                print("Budget exhausted. Resume later with --resume.")
                raise SystemExit(1)

            start = time.time()
            try:
                result = run_case(case["id"])
                result["elapsed"] = time.time() - start
            except Exception as exc:
                result = {"id": case["id"], "error": str(exc)}

            fh.write(json.dumps(result) + "\n")
            fh.flush()
            used += 1

    print("Done or budget reached.")


if __name__ == "__main__":
    test_cases = [{"id": i} for i in range(60)]
    main(test_cases)

Run it twice. The second run skips completed cases. Set EVAL_BUDGET_CALLS=20 to see the guard stop early.

EVAL_BUDGET_CALLS=20 python eval_runner.py
python eval_runner.py

Why does this work?

• Every result lands on disk immediately.
• Errors are recorded, not silently retried.
• The budget is explicit via an environment variable.
• Resuming is just running the same script again.

Test Your Guard, Not Just Your Model

Before touching a real endpoint, simulate failures. Make run_case raise a RuntimeError for even IDs. Run twice. Confirm the error lines exist and unfinished cases get a second chance.

That test costs you two minutes. It saves you from burning a real quota on a broken script.

Limitations

This pattern is for batch evals. Not for low-latency production inference. Not for strict A/B comparisons where you need deterministic hardware.

It also does not fix the model provider's rate limits. You still need exponential backoff when the API explicitly asks you to slow down.

Who Should NOT Use This

Skip this if you have three test cases. A JSONL file is overkill. Just run a function and print the result.

Skip this if you need guaranteed audits. A free server can lose data between backups. Use paid storage for compliance-grade records.

The Correct Mental Model

Free model access is a rate-limited queue. Free servers are ephemeral workers. Treat them as such.

Budget your calls. Checkpoint your results. Resume after a crash. That's the whole trick.

Want a deeper dive? Show me your eval failure logs and I'll share the next pattern.