The review passed. The deployment failed.
An engineer moved a code-review agent to a free server. The model answered correctly in every test. Then the server hit its quota at 2:47 PM on day three. Fourteen pull request verdicts vanished with the session. No state. No logs. No retry.
This is the reviewer's blind spot. Teams test models obsessively. They rarely test the runtime underneath.
This guide covers one decision: refusing a free server for an agent. It lists red flags, better alternatives, and exit criteria. It also names a concrete example: MonkeyCode's free model access and free server option.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
What "free" actually includes
MonkeyCode is an open-source agent platform. It offers free model access and a free server option. The free model access includes 10 million tokens per cycle, per the project's published claim. The free server runs the agent without a paid VM.
Those offers are real. They are also constraints. Free infrastructure is a budget, not a promise. Treat it like a trial environment, not a production contract.
Free tiers exist to convert users, not to run production. That is fine. The mistake is treating them as infrastructure.
Three failure modes
Free infrastructure fails in predictable ways. Know all three before committing.
Mode one: quota exhaustion. Token budgets reset on a schedule. Heavy days burn the whole cycle. The failure is silent. The agent stops mid-task.
Mode two: state loss. Free servers restart without warning. In-memory sessions disappear. Long-running agents lose context. Recovery is manual.
Mode three: contention. Shared resources mean cold starts. Neighbors consume CPU. Rate limits appear at peak hours. Latency becomes a random variable.
Red flags: check before committing
Run this checklist before any migration. One red flag means pause. Two mean stop.
- Hard deadlines. The agent gates CI or on-call responses. A quota reset cannot wait.
- Daily burn exceeds the budget. Tasks consume more tokens than the free cycle provides in one day.
- State lives in memory. No persistent store. A restart destroys the session.
- Audit requirements. Logs are needed for compliance. Free tiers rarely guarantee retention.
- Customer data in prompts. Free servers usually lack data-processing agreements. Sending PII is a policy risk.
- Unattended overnight runs. Nobody watches the retry loop. Failures compound silently.
Two red flags mean stop. Pick a paid tier or self-host.
A reproducible fit probe
Do not guess. Measure. The probe below records three signals: latency, token burn per task, and recovery time. It prints a verdict. The endpoint must return JSON with a tokens_used field.
#!/usr/bin/env python3
"""workload_fit_probe.py — measure an agent workload against free-tier limits."""
import json
import sys
import time
import urllib.request
FREE_TOKEN_BUDGET = 10_000_000 # operator-supplied figure for MonkeyCode free access
MAX_P95_LATENCY_S = 10.0
def run_task(endpoint: str, task_id: int) -> dict:
start = time.monotonic()
body = json.dumps({"task": task_id}).encode()
req = urllib.request.Request(
endpoint, data=body, headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req, timeout=30) as resp:
payload = json.loads(resp.read())
elapsed = time.monotonic() - start
return {
"task": task_id,
"latency_s": round(elapsed, 2),
"tokens": payload.get("tokens_used", 0),
}
def measure(endpoint: str, tasks: int = 20, interval_s: float = 5.0) -> list[dict]:
results = []
for i in range(tasks):
results.append(run_task(endpoint, i))
if i < tasks - 1:
time.sleep(interval_s)
return results
def p95(values: list[float]) -> float:
ordered = sorted(values)
index = min(len(ordered) - 1, int(len(ordered) * 0.95))
return ordered[index]
def verdict(results: list[dict]) -> str:
latencies = [r["latency_s"] for r in results]
tokens = sum(r["tokens"] for r in results)
avg_cycle_s = sum(latencies) / len(latencies) + 5.0
daily_tokens = tokens * (24 * 3600) / avg_cycle_s
flags = []
if p95(latencies) > MAX_P95_LATENCY_S:
flags.append("latency")
if daily_tokens > FREE_TOKEN_BUDGET:
flags.append("quota")
if not flags:
return "RUN — free tier fits the measured workload"
if len(flags) == 1:
return f"WATCH — red flag: {flags[0]}"
return f"STOP — red flags: {', '.join(flags)}"
if __name__ == "__main__":
endpoint = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:8080/task"
results = measure(endpoint)
print(json.dumps(results, indent=2))
print(verdict(results))
Run it against the agent endpoint:
python3 workload_fit_probe.py http://localhost:8080/task
Example output:
$ python3 workload_fit_probe.py http://localhost:8080/task
[
{"task": 0, "latency_s": 1.2, "tokens": 4200},
{"task": 1, "latency_s": 1.1, "tokens": 3800}
]
WATCH — red flag: quota
The verdict is a decision, not a suggestion. RUN means the measured workload fits. WATCH means one limit is close. STOP means migrate before the next sprint.
Measure recovery separately. Restart the server. Time how long the agent takes to resume a task. Anything above 60 seconds breaks interactive review workflows.
Better alternatives
Free infrastructure fits some workloads. Not all.
Use the free tier when: the workload is batch, interruptible, and stateless. Examples: overnight classification, weekly report generation, one-off migrations.
Pay when: the workload is interactive, stateful, or deadline-bound. A paid tier buys predictable latency and retention.
Self-host when: data policy forbids third-party processing. Local models plus a small VM beat any free API for privacy.
Hybrid when: both patterns apply. Run experiments on free infrastructure. Promote stable tasks to paid capacity.
Exit criteria
Define the exit before entering. Three thresholds trigger migration:
- Quota exhaustion twice in one cycle. The workload is too heavy for the budget.
- p95 latency above 10 seconds for three consecutive days. Contention is now the norm.
- State loss costs more than one hour of work. Recovery time exceeds the value of the free tier.
Write these into the runbook. Review them weekly. Planned migration is cheaper than emergency migration.
Set a calendar reminder for the review. Thresholds drift. Workloads grow.
Who should not use this approach
This guide assumes a small, interruptible workload. It does not fit everyone.
Small means fewer than a few hundred tasks per day. Interruptible means a failed run costs nothing. If both are true, the free tier is a good experiment. If either is false, it is a liability.
Do not use a free server for: production CI gates, customer-facing agents, regulated data pipelines, or anything with a hard SLA.
Do not use the probe as a compliance audit. It measures latency and tokens. It does not verify data handling, retention, or regional residency.
Free infrastructure is a tool. Use it where the failure cost is low. Pay where the failure cost is high. That distinction matters more than any token count.
If the fit probe looks useful, MonkeyCode's free access is a reasonable place to test it. Measure first. Decide after.
