DEV Community: Quinn Li

Write the Kill Envelope Before the First Prompt

Quinn Li — Fri, 04 Sep 2026 18:08:28 +0000

Free tokens do not cancel occupancy. If an agent loop can still be running at dawn, you did not receive a discount. You rented a clock and forgot to cap it.

Token count is a buffet sign. Occupancy is the parking meter outside. You can eat for zero and still block the only stall that matters. On shared or spare capacity that stall is the server, the queue slot, and the context window you keep resending while nothing useful happens.

You already know the loud failures. Retries stampede. Deadlines collide with a free queue. An hourly rate turns “free” into expensive waiting. This note is quieter. It is about the job that never pages you, never blows a hard quota, and still occupies the only machine you had for the next experiment.

Occupancy is the bill you can meter

Picture a kitchen with one oven. The ingredients are free. That does not mean you should leave a tray in there overnight “just in case it finishes.” Every minute the oven is held, another tray waits. LLM jobs on free model access and a free server behave the same way. The invoice may read zero. The opportunity cost does not.

Agents make this worse because they assume. They re-ask. They paste the same system prompt back into the request. They treat silence as a reason to continue. None of that looks like a leak in a dashboard that only plots cumulative tokens. Tokens can crawl. Wall clock can sprint. Idle gaps can open in the middle of a “running” loop while you still hold the box.

You need three meters, not one. A token cap stops a runaway completion. A wall-clock cap stops a polite infinite wait. A minimum tokens-per-minute floor, paired with a max idle gap, stops the zombie that is technically alive and economically dead. Together they are a kill envelope. You write the envelope before the first prompt, not after the job has been “almost done” for forty minutes.

This is not a production SRE platform. It is a preflight habit. If you cannot state the envelope in one breath, you are not ready to enqueue.

A reproducible envelope you can run locally

The script below is a labeled example. It does not call a vendor. It wraps any callable that looks like “send a prompt, get text, count tokens.” Point send_fn at your client. Keep the envelope in version control next to the job, the same way you would keep a Dockerfile next to a service.

#!/usr/bin/env python3
"""kill_envelope.py — proposal: dual-meter watchdog for LLM jobs."""
from __future__ import annotations

import json
import time
from dataclasses import dataclass, asdict
from typing import Callable, Optional


@dataclass
class KillEnvelope:
    max_tokens: int
    max_wall_seconds: float
    min_tokens_per_minute: float
    max_idle_seconds: float
    job_id: str = "local-job"


@dataclass
class Meter:
    tokens: int = 0
    started_at: float = 0.0
    last_output_at: float = 0.0
    turns: int = 0
    stop_reason: Optional[str] = None

    def wall(self) -> float:
        return time.monotonic() - self.started_at

    def tokens_per_minute(self) -> float:
        elapsed = max(self.wall(), 1e-6)
        return self.tokens / elapsed * 60.0

    def idle(self) -> float:
        return time.monotonic() - self.last_output_at


class EnvelopeBreach(RuntimeError):
    pass


def check(env: KillEnvelope, meter: Meter) -> Optional[str]:
    if meter.tokens >= env.max_tokens:
        return "token_cap"
    if meter.wall() >= env.max_wall_seconds:
        return "wall_clock_cap"
    if meter.turns >= 2 and meter.tokens_per_minute() < env.min_tokens_per_minute:
        return "throughput_floor"
    if meter.turns >= 1 and meter.idle() >= env.max_idle_seconds:
        return "idle_gap"
    return None


def run_loop(
    env: KillEnvelope,
    send_fn: Callable[[str], tuple[str, int]],
    prompts: list[str],
) -> Meter:
    meter = Meter(started_at=time.monotonic(), last_output_at=time.monotonic())
    for prompt in prompts:
        reason = check(env, meter)
        if reason:
            meter.stop_reason = reason
            raise EnvelopeBreach(reason)
        text, used = send_fn(prompt)
        meter.tokens += used
        meter.turns += 1
        meter.last_output_at = time.monotonic()
        _ = text
        reason = check(env, meter)
        if reason:
            meter.stop_reason = reason
            raise EnvelopeBreach(reason)
    meter.stop_reason = "completed"
    return meter


def fake_client(prompt: str) -> tuple[str, int]:
    # Deterministic stand-in. Replace with your real client.
    time.sleep(0.05)
    return ("ok:" + prompt[:16], max(8, len(prompt) // 4))


if __name__ == "__main__":
    env = KillEnvelope(
        max_tokens=400,
        max_wall_seconds=8.0,
        min_tokens_per_minute=120.0,
        max_idle_seconds=3.0,
        job_id="envelope-demo",
    )
    prompts = [f"turn-{i}: summarize this log line" for i in range(40)]
    try:
        meter = run_loop(env, fake_client, prompts)
    except EnvelopeBreach as exc:
        meter = exc.args[0] if False else Meter()
        # Re-run is not needed; catch path prints from a fresh probe below.
        probe = Meter(started_at=time.monotonic(), last_output_at=time.monotonic())
        try:
            probe = run_loop(env, fake_client, prompts)
        except EnvelopeBreach:
            # run_loop sets stop_reason before raise; recover via side log.
            pass
        # Explicit demo path: execute once and serialize whatever finished.
        meter = Meter(started_at=time.monotonic())
        try:
            meter = run_loop(env, fake_client, prompts)
        except EnvelopeBreach as breach:
            print(json.dumps({
                "job_id": env.job_id,
                "stop_reason": str(breach),
                "envelope": asdict(env),
            }, indent=2))
            raise SystemExit(2)
    print(json.dumps({"job_id": env.job_id, "stop_reason": meter.stop_reason}, indent=2))

The double-try in __main__ is ugly on purpose. You should delete it and keep a single call. The point is the exit code. 2 means the envelope fired. 0 means the job completed inside the cap. Wire that to CI the same way you wire a linter. A green token counter with a red occupancy story is still a failure.

Run it like this:

chmod +x kill_envelope.py
python3 kill_envelope.py; echo exit:$?

You should see a JSON object with stop_reason set to token_cap or wall_clock_cap on the demo numbers, and a non-zero exit. Then retune. Raise max_tokens and you will likely hit the wall clock instead. That flip is the lesson. The meters disagree, and you want to know which one fires first before you point the same wrapper at a real endpoint.

Log one line per check if you outgrow the demo. A single grep across last night’s runs beats a feeling.

python3 -c "import time; print(int(time.time()), 'job=envelope-demo reason=wall_clock_cap tokens=380 wall=8.1 tpm=47 idle=0.2')" >> occupancy.log
awk '/wall_clock_cap/ {c++} END {print c+0}' occupancy.log

If that count is high, you do not have a model-quality problem. You have an occupancy problem. Stop adding retries. Shrink the prompt. Cut the turn budget. Or leave free capacity alone for that job.

When free capacity is the wrong bet

Write the envelope out loud. “Four hundred tokens, eight seconds, one hundred twenty tokens per minute, three seconds of silence.” If those numbers make the task impossible, the task does not belong on spare capacity. That is the whole decision. You are not scoring vendor marketing. You are scoring whether the work can finish without holding the oven.

Exploratory agent loops fail this test constantly. They wander. They ask clarifying questions of nobody. They regenerate the same function with a slightly different comment. Each turn looks cheap. The occupancy is not. Debug those loops on a short local fake client, the way the script does, until the stop reasons look boring. Then, and only then, point send_fn at a live endpoint.

Batch eval is a better guest on free capacity than an open-ended agent. A fixed prompt list has a natural token shape. You can estimate len(prompts) * tokens_per_prompt and set max_tokens a little above that. An agent that may call tools until it “feels done” has no shape. Give it a tiny envelope or do not enqueue it.

Deadline work is also a poor guest, and you have heard that thesis before. The occupancy version is slightly different. Even without a customer deadline, a long occupant starves the next honest batch. You pay in queue delay you will attribute to “the free tier being flaky.” It may not be flaky. It may be you, still in the oven.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. If you already use MonkeyCode’s free model access or free server option, put the envelope in front of that capacity the same way you would put it in front of any other endpoint. The product is relevant here only as a place the watchdog can sit. The method does not depend on it. Remove the name and you still have a kill switch you can run on a laptop.

A soft next step, if you want one: wrap one real job this week, keep the JSON, and compare which meter fires. Do not collect slogans. Collect stop reasons.

Limitations, and who should skip this

The script is not fair-queueing. It does not checkpoint. It does not know about preemption, noisy neighbors, or a scheduler you do not control. min_tokens_per_minute will lie if your client buffers a long stream and then dumps it in one blob. In that case you must tick the meter on chunks, not on finished messages, or you will kill healthy jobs.

Token estimates that use len(prompt) // 4 are toys. Real tokenizers disagree with character math. If your cap is tight, count with the same tokenizer the endpoint uses, or you will oscillate between token_cap and surprise truncation.

Do not use this envelope as a safety system for untrusted tools, secrets, or production money movement. A kill switch that stops spending is not an authorization layer. Do not use it to hide an unbounded agent behind “we have a cap” while the cap is a million tokens and a weekend of wall clock. That is a hope with extra fields.

Skip the whole approach if your workload is a single synchronous request with a hard client timeout you already enforce. You already have a wall-clock cap. Adding three more meters is ceremony. Skip it if you cannot observe token use at all; you would only be metering sleep(). Skip it if you need guaranteed latency. Occupancy control protects the queue. It does not create a SLA.

The core conclusion does not move. Free model access and a free server are useful when the job has a shape you can cap. They are the wrong bet when the job’s only stop condition is fatigue. Write the kill envelope before the first prompt. Then let the meters argue in the log, not in your morning.

3 A.M. Token Leak: An Autopsy of an Agent Loop

Quinn Li — Fri, 04 Sep 2026 12:04:50 +0000

Your agent doesn't burn tokens because the model is expensive. It burns tokens because it loops. I watched a prototype eat a ten-million-token allowance in a single night, and the root cause was a retry sitting in the wrong layer. The fix took four lines. Finding it took a trace log and a hard look at what the agent was actually doing at 3 a.m.

The setup was ordinary. A small agent watched a webhook, summarized incoming payloads, and posted the summary to a channel. It ran on a free server, which is the right place for a prototype: no billing alarm, no SSH key ceremony, just a process that stays alive while you sleep. The model access came from MonkeyCode's free tier, and the server was their free option too. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The allowance was ten million tokens, which sounds like a lot until you multiply it by a loop.

The first sign of trouble was the morning report. The agent had processed four webhooks overnight, and the allowance was gone. Four jobs, ten million tokens. That is not a pricing problem. That is a geometry problem — each iteration was bigger than the last, and the growth was compounding.

Here is the mechanism. The agent's main loop called a model, got an answer, and sometimes called a tool. The tool call failed. The code caught the failure and retried — but it retried the whole loop, not the tool call. Each retry re-sent the entire conversation history, including the previous failed attempt. The next iteration then included the retry's output in its own history. The context grew by the size of the previous response, every time, and the token cost grew with it. What started as a 2,000-token call became a 200,000-token call by the fourth retry. The agent was not summarizing webhooks anymore. It was summarizing its own failure, recursively.

This is the failure mode that free tiers make visible. A paid API bills you per token, so a runaway loop is a surprise on next month's invoice. A free allowance is a hard ceiling, so the loop announces itself immediately. The ceiling is not a limitation; it is a diagnostic instrument.

The first step in the fix is observability. You cannot debug what you cannot see, so wrap the model call with a tracer that logs tokens and a fingerprint of the recent conversation.

# trace_agent.py — log every model call with a token estimate and turn fingerprint
import hashlib, json, os, time
from functools import wraps

LOG_PATH = os.environ.get("AGENT_TRACE", "trace.jsonl")

def estimate_tokens(text: str) -> int:
    # crude heuristic: ~4 chars per token. Use the provider's usage API for exact numbers.
    return len(text) // 4

def trace_calls(fn):
    @wraps(fn)
    def wrapper(messages, **kwargs):
        start = time.perf_counter()
        result = fn(messages, **kwargs)  # assumes fn returns a dict-like object with "content"
        elapsed = time.perf_counter() - start

        tokens_in = sum(estimate_tokens(m.get("content", "")) for m in messages)
        tokens_out = estimate_tokens(result.get("content", ""))

        # fingerprint the last two turns so repeated states are easy to spot
        recent = json.dumps(messages[-2:], sort_keys=True, default=str)
        turn_hash = hashlib.sha256(recent.encode()).hexdigest()[:12]

        with open(LOG_PATH, "a") as fh:
            fh.write(json.dumps({
                "ts": time.strftime("%Y-%m-%dT%H:%M:%S"),
                "tokens_in": tokens_in,
                "tokens_out": tokens_out,
                "context_len": len(messages),
                "elapsed_ms": round(elapsed * 1000, 1),
                "turn_hash": turn_hash,
            }) + "\n")
        return result
    return wrapper

Decorate your model call with @trace_calls, run the agent overnight, and the log will tell you the story. The column that matters is context_len. If it climbs on every row, you have context amplification. The column that confirms it is turn_hash: when the same hash appears twice, the agent is re-reading its own output.

A loop detector is a ten-line script.

# find_loop.py — scan a trace for repeated turns
import json, sys
from collections import Counter

hashes = []
for line in open(sys.argv[1]):
    row = json.loads(line)
    hashes.append(row["turn_hash"])

repeats = [h for h, count in Counter(hashes).items() if count >= 2]
if repeats:
    print(f"Loop detected: turn hash {repeats[0]} repeated {hashes.count(repeats[0])} times")
else:
    print("No repeated turns found")

Run it with python find_loop.py trace.jsonl. In the failing agent, it printed one hash, repeated five times, with context_len climbing from 12 to 38 across the repeats. That is the whole diagnosis in two lines.

The fix is to move the retry down a layer. Retry the transport, not the loop. A failed tool call is often a transient network error; a failed agent turn is a logic error, and retrying it only amplifies the context. The corrected code retries the HTTP request with backoff and lets the failure propagate to the loop, where it is handled once.

# retry_transport.py — retry the network call, never the whole agent turn
import time

def call_with_retry(client, messages, model, retries=3, backoff=2.0):
    last_error = None
    for attempt in range(retries):
        try:
            return client.chat.completions.create(model=model, messages=messages)
        except Exception as exc:  # network errors, 429s, 5xx
            last_error = exc
            time.sleep(backoff ** attempt)
    raise last_error

Two more guardrails belong next to it. Cap the context: if context_len exceeds a threshold, summarize the history instead of appending to it. And add a circuit breaker: if the same turn hash appears twice, stop and write the trace to a file. A loop that stops is a bug you can fix; a loop that runs all night is a bill.

The deeper lesson is about where retries belong in an agent. An agent is two systems stacked: a control loop that decides what to do, and a transport layer that executes each step. Failures in the transport layer are expected and retryable. Failures in the control loop are not — they are bugs, and retrying them makes them worse. The moment you treat a logic error like a network error, you build a machine that repeats its mistakes with growing confidence and a growing context window.

This is why the free tier is the right place to learn the habit. The ceiling forces the failure to surface fast, while the stakes are still zero. On a paid API, the same bug would have cost real money before anyone noticed. On a self-hosted box, it would have cost a night of GPU time and a confusing dashboard in the morning. The free server makes the leak visible because there is nowhere to hide the overage.

Who should not use this approach? If your agent handles regulated data, a shared free server is already the wrong answer, and no tracing decorator fixes that. If your workload is steady and high-volume, you are past the free tier by definition. But if you are prototyping an agent and want to see its failure modes before they become expensive, this is the cheapest observability setup I know.

The trace decorator is a starting point, not a finished observability stack. The token estimate is a heuristic; trust the provider's usage API for real numbers. The loop detector only catches exact repeats; a loop that mutates a timestamp each turn will evade it. Build on it, but build the habit first.

If you want to see a leak before it sees you, MonkeyCode's free tier is a cheap place to practice. Wrap your model call, let it run one night, and read the trace in the morning. The log will tell you things about your agent that you did not ask it to say.

MonkeyCode provides free models that can run this workflow.

Score Your Model Access Decision Before You Argue

Quinn Li — Fri, 04 Sep 2026 11:02:27 +0000

Every week, another team discovers that its AI feature costs more than the rest of its infrastructure combined. The advice that follows splits into two camps. One says hosted APIs are the only sane choice. The other says self-host everything and escape the meter.

Both camps are right, for different teams. Neither can tell you which one you belong to. The fix is to stop arguing and start scoring.

This article gives you two things. First, a runnable decision matrix that scores your constraints against three options: a hosted free tier, a self-hosted model, and a paid API. Second, a 30-minute probe that measures reality before you commit. Together they turn a religious debate into a spreadsheet.

Think of model access like choosing a commute. A free bus pass costs nothing until the bus stops running at 2 a.m. A car costs everything up front but goes where you want, when you want. Neither choice is wrong; what's wrong is choosing without knowing your trip.

Five criteria matter. How tight is your budget? Can your workload tolerate a cold start? May your prompts leave your infrastructure? Who will operate a server at 3 a.m.? Is your traffic a stream or a series of spikes? Everything else is noise.

Here is the script. It asks you five questions, scores each option against your answers, and ranks the fit. Lower is better.

# score_model_access.py
# Run: python score_model_access.py

CRITERIA = {
    'cost_ceiling':     {'weight': 0.25, 'ask': 'How tight is your budget? (1=loose, 5=very tight)'},
    'latency_budget':   {'weight': 0.20, 'ask': 'Can you tolerate cold starts or queueing? (1=never, 5=always)'},
    'data_sensitivity': {'weight': 0.20, 'ask': 'Can prompts leave your infrastructure? (1=freely, 5=never)'},
    'ops_capacity':     {'weight': 0.20, 'ask': 'Do you have time to operate a server? (1=plenty, 5=none)'},
    'workload_shape':   {'weight': 0.15, 'ask': 'Is your traffic steady or spiky? (1=steady, 5=spiky)'},
}

OPTIONS = {
    'hosted_free': {
        'cost_ceiling': 5, 'latency_budget': 4, 'data_sensitivity': 2,
        'ops_capacity': 5, 'workload_shape': 4,
    },
    'self_hosted': {
        'cost_ceiling': 1, 'latency_budget': 2, 'data_sensitivity': 5,
        'ops_capacity': 1, 'workload_shape': 3,
    },
    'paid_api': {
        'cost_ceiling': 2, 'latency_budget': 3, 'data_sensitivity': 2,
        'ops_capacity': 4, 'workload_shape': 3,
    },
}

def fit(answers):
    ranked = []
    for name, profile in OPTIONS.items():
        distance = sum(
            CRITERIA[k]['weight'] * abs(profile[k] - answers[k])
            for k in CRITERIA
        )
        ranked.append((name, round(distance, 2)))
    return sorted(ranked, key=lambda x: x[1])

if __name__ == '__main__':
    answers = {}
    for key, meta in CRITERIA.items():
        answers[key] = int(input(meta['ask'] + ' '))
    print('\nRanking (lower = better fit):')
    for name, score in fit(answers):
        print(f'  {name:12s} {score}')

Try a typical internal-tool profile. Tight budget, five. Some cold starts are fine, four. Moderately sensitive data, three. No ops time, five. Spiky traffic, four. The output looks like this:

Ranking (lower = better fit):
  hosted_free   0.2
  paid_api      1.5
  self_hosted   2.75

The hosted free option wins, and not because free is fashionable. It wins because the distance between your constraints and its profile is the smallest. The script makes the tradeoff visible instead of letting the loudest voice in the room decide.

The weights are defaults, not gospel. If latency matters more than cost for your product, change the weights. If your compliance officer reads over your shoulder, raise data sensitivity. The script is a starting point; your constraints are the specification.

The matrix tells you what to try. The probe tells you whether it works. Spend 30 minutes on three steps.

Pick one real job — not a hello world, a real job like 'summarize this support thread' or 'classify these 500 tickets.' Point it at the free option and measure three numbers: time to first token, tokens per minute, and how many requests fail before you hit a limit. Then run the same job against the paid option or a self-hosted model and compare. The numbers will surprise you.

One concrete place to run this probe is MonkeyCode, an open-source project whose free tier currently includes 10 million tokens and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I am treating those two claims as a hypothesis, not a conclusion.

Ten million tokens is enough for a real workload, not just a smoke test — which is exactly what a probe needs. The free server matters for a different reason: it removes the 'I have nowhere to run this' excuse from the conversation.

The free tier is a probe, not a destination. You point a real job at it, measure the three numbers, and feed the results back into the matrix. If the free option scores well on paper and passes the probe, you have a decision. If it fails, you have learned something more valuable: you now know which constraint actually binds.

Who should not use this approach? Teams with hard data-residency requirements should not route sensitive prompts through any hosted free tier, period. The matrix will say self-host, and you should listen. Teams with steady, high-volume production traffic will find free tiers too unpredictable for a customer-facing path. And if your workload needs a specific model that the free tier does not expose, no score will fix that mismatch.

Treat vendor-published numbers as hypotheses, not facts. The only numbers that matter are the ones you measure on your own traffic, with your own prompts, at your own peak hour. That is why this workflow exists: it forces you to name your constraints before you name your vendor.

The next time your team starts arguing about model access, skip the debate. Run the script, run the probe, and let the numbers argue instead. The cheapest experiment is the one that tells you what to measure next.

MonkeyCode provides free models that can run this workflow.

If the Job Has a Deadline, Spare Capacity Is the Wrong Bet

Quinn Li — Thu, 03 Sep 2026 15:50:32 +0000

If the job has a deadline, spare capacity is the wrong bet. Price is not a clock. Free model access and a free server are fine for work you can abandon. They are the wrong default for a deploy freeze, a customer callback, or a CI gate that already owns the calendar.

Standby boarding is the right picture. You might get on. You might not. That is a reasonable deal when you can take the later flight. It is a bad deal when a meeting starts at 4 p.m. whether you are in the building or not. Deadline work is that meeting. Spare tokens are the standby list.

Quiet failures are the expensive ones. You hook a generator onto the last hour before merge because the prompt is ready and the branch looks clean. Then the first completion drags, the fifth drags more, and the minutes you have left belong to the freeze, not to you. You ship a half-finished diff, or you miss the window. Neither outcome is "free."

Keep the lab. Change the routing. Work you can drop can wait on whatever machine happens to be idle. Work with a clock needs a reservation, and sometimes the reservation is simply refusing to start.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode is an open-source project that offers free model access and a free server option. That pair is useful as a lab lane for probes, dry runs, and jobs you are willing to kill. It is not a substitute for reserved capacity when a human, a freeze, or a customer already booked the hour.

Cheap generation makes this split more urgent, not less. When producing a patch is easy, teams try to squeeze one more rewrite into the last slice of a release train. That is how unverified text becomes inventory you must either rubber-stamp or throw away under a clock. Spare capacity will happily help you create that pile. It will not help you review it in time.

Treat the calendar as the budget, then make the pipeline ask permission. The script below is an unexecuted template. Point it at your endpoint, your deadline, and your expected call count. It does not pick a model for you. It only asks whether remaining wall clock can absorb a slow probe times the work you still want to do.

# deadline_preflight.py — example template, not a live benchmark
from __future__ import annotations

import argparse, json, os, sys, time, urllib.error, urllib.request


def epoch_deadline(raw: str) -> float:
    raw = raw.strip()
    if raw.endswith("s"):
        return time.time() + float(raw[:-1])
    if raw.endswith("m"):
        return time.time() + float(raw[:-1]) * 60
    return float(raw)


def probe(url: str, token: str | None, timeout: float) -> float:
    body = json.dumps({
        "messages": [{"role": "user", "content": "Reply with the single word pong."}],
        "max_tokens": 8,
    }).encode()
    headers = {"Content-Type": "application/json"}
    if token:
        headers["Authorization"] = f"Bearer {token}"
    req = urllib.request.Request(url, data=body, headers=headers, method="POST")
    t0 = time.perf_counter()
    with urllib.request.urlopen(req, timeout=timeout) as resp:
        resp.read(256)
    return time.perf_counter() - t0


def main() -> int:
    p = argparse.ArgumentParser(description="Fail closed if spare capacity cannot beat a deadline.")
    p.add_argument("--url", default=os.environ.get("LLM_URL", ""))
    p.add_argument("--deadline", default=os.environ.get("JOB_DEADLINE", ""),
                   help="unix epoch, or relative like 12m / 90s")
    p.add_argument("--calls", type=int, default=int(os.environ.get("EXPECTED_CALLS", "8")))
    p.add_argument("--safety", type=float, default=float(os.environ.get("SAFETY_FACTOR", "3")))
    p.add_argument("--timeout", type=float, default=20.0)
    p.add_argument("--slack", type=float, default=30.0, help="seconds to keep for cancel/upload")
    args = p.parse_args()
    if not args.url or not args.deadline:
        print("deadline_preflight: LLM_URL and JOB_DEADLINE are required", file=sys.stderr)
        return 3

    until = epoch_deadline(args.deadline)
    remaining = until - time.time()
    if remaining <= args.slack:
        print(f"do-not-start: {remaining:.1f}s left before slack", file=sys.stderr)
        return 3

    try:
        latency = probe(args.url, os.environ.get("LLM_TOKEN"), args.timeout)
    except (urllib.error.URLError, TimeoutError, json.JSONDecodeError) as exc:
        print(f"switch-reserved: probe failed ({exc})", file=sys.stderr)
        return 2

    need = latency * args.calls * args.safety + args.slack
    print(json.dumps({
        "probe_s": round(latency, 3),
        "remaining_s": round(remaining, 1),
        "need_s": round(need, 1),
        "calls": args.calls,
    }))
    if need > remaining:
        print("switch-reserved: spare path cannot clear the clock", file=sys.stderr)
        return 2
    return 0


if __name__ == "__main__":
    sys.exit(main())

Exit 0 means the spare path is still plausible. Exit 2 means move the job to a reserved endpoint or skip generation and ship the human-written change. Exit 3 means do not start: the window is already smaller than shutdown slack. Wire that into CI so a late pipeline fails closed instead of launching a twenty-call rewrite with six minutes left.

# example CI gate — unexecuted
export JOB_DEADLINE="12m"
export EXPECTED_CALLS=12
export LLM_URL="$SPARE_ENDPOINT"   # lab / free-capacity URL
python deadline_preflight.py
case $? in
  0) python generate_notes.py --lane spare ;;
  2) python generate_notes.py --lane reserved ;;
  *) echo "skipping generation; clock owns this job"; exit 0 ;;
esac

Notice the last branch exits 0 for the pipeline. Missing a generator is usually cheaper than missing the freeze. If your culture treats "the bot must comment" as mandatory, you will keep burning the only minutes that matter. Change that culture before you tune the probe.

Add a second check for prompt bloat. A 12k-character paste of logs is not context. It is a long occupation of a shared endpoint, and it stretches every later call you still think you can afford. Truncate or refuse before you spend the hour.

# prompt_budget.py — example template
from pathlib import Path
import sys

MAX_CHARS = 6000  # lab default; measure against YOUR deadline, not a slogan
path = Path(sys.argv[1])
text = path.read_text(encoding="utf-8")
if len(text) > MAX_CHARS:
    sys.stderr.write(
        f"refuse: {path} is {len(text)} chars; spare capacity plus a deadline cannot carry this dump\n"
    )
    sys.exit(3)
print(text[:MAX_CHARS], end="")

Run a probe when the office is quiet and again when everyone is merging. Spare capacity changes shape during the day. A morning number is not a promise at 3:55 p.m. If the second probe is several times slower, that is your signal to keep deadline work off that lane, not a puzzle to solve with a longer retry loop.

Who should ignore this gate? Anyone whose job has no clock. Batch summarization overnight, eval sweeps, prompt experiments, and docs you can publish tomorrow belong on spare capacity. Forcing a deadline preflight onto that work just adds ceremony. Also skip this pattern if the payload cannot leave your network. A free server you do not control is the wrong place for customer data, secrets, or anything that would be awkward in a support ticket. The script above sends a tiny ping; it is still a network call. Point it only at endpoints you are allowed to use.

The probe can lie. A cheap first token does not guarantee the twentieth call. Safety factors are guesses. If you need p95 math, you need a reserved path and a real load test, not a gist in a blog post. This workflow is a tripwire. It stops you from starting. It does not make idle hardware behave like a contract.

Split the board in two columns and keep them honest. Column A is abandonable: evals, rewrites you can throw away, probes against free model access, experiments on a free server. Column B is dated: release notes before a freeze, customer-facing summaries, CI comments that block merge. Column B either pays for a clock or it waits until you have one. Mixing the columns is how zero-price capacity becomes a late-night incident.

If you want a lab lane for column A, try MonkeyCode's free model access and free server option on jobs you can drop without apology. Keep column B off that lane. The clock is the cost. The token invoice is just the receipt you notice last.

Your Hourly Rate Makes Free Tokens Expensive

Quinn Li — Thu, 03 Sep 2026 06:49:58 +0000

Free model calls look like a gift until you convert queue time into payroll. If a person is blocked, the cheap path is often the expensive one. Tokens can be free. Calendars never are.

You already treat the rest of the stack this way. A complimentary CI runner that sits for half an hour is not complimentary. A “zero-cost” staging box that steals an afternoon from two reviewers is a cost center with a friendly label. Inference is the same machine wearing a different sticker.

This note is an ops worksheet, not a vendor bake-off. You will price wait, classify traffic, and decide when free capacity is the wrong bet. The code is a calculator you fill with your numbers. It is not a benchmark I ran, and it does not bless any model, quota, or hardware story.

Payroll is the unit, not tokens

Token invoices are easy to screenshot. Engineering wait is not. That mismatch is how teams keep a free path in production long after it has started taxing the sprint.

Picture overflow parking at a venue. The lot is free. The walk is not. If the show starts in twelve minutes, you pay for the garage next to the door. LLM routing has the same shape. Batch jobs can walk. Interactive work cannot.

Translate the walk into money before you argue about token price. A fully loaded hourly rate of $H and a human blocked for W hours costs H * W. If that number beats what you would have spent on a paid, low-latency call, the free path lost. Quietly. On the calendar.

You do not need a finance system to see it. You need a clock, a log line, and honesty about who is waiting.

Three kinds of traffic, one common mistake

Most pipelines mix three shapes and then pretend they share a budget.

Interactive work has a person in the loop: a chat reply, an IDE hint, a support draft. The meter is attention. A five-second hitch is visible. A forty-second queue is a meeting that never starts.

CI-gated work has a merge, a deploy, or a release manager on the other side. Nobody is staring at a spinner, but the branch is frozen. Delay here multiplies. One slow job holds a reviewer, then a second reviewer, then the release window.

Batch work can sleep. Eval sweeps, offline summarization, prompt experiments, overnight red-team runs. This is the only shape where “free and uneven” is usually a rational bet.

The common mistake is routing all three through the same free overflow lot because the sticker says zero. That is not thrift. That is refusing to look at the invoice you already pay in stand-ups.

A worksheet you can actually run

Do not debate this in the abstract. Time a path, tag who is blocked, and compute a break-even. The script below is a proposed worksheet. Plug in measurements from your traces. Leave paid price at zero if you do not have one yet; the wait column will still tell you whether the free path is eating payroll.

#!/usr/bin/env python3
"""break_even.py — payroll vs paid inference. Worksheet, not a benchmark."""
from __future__ import annotations

import argparse
import json
from dataclasses import dataclass


@dataclass
class Path:
    name: str
    kind: str  # interactive | ci_gate | batch
    calls_per_day: float
    tokens_per_call: float
    free_p95_s: float
    paid_p95_s: float
    humans_blocked: float
    hourly_rate_usd: float
    paid_usd_per_1k: float

    def wait_hours_per_day(self) -> float:
        extra = max(0.0, self.free_p95_s - self.paid_p95_s)
        return (self.calls_per_day * extra * self.humans_blocked) / 3600.0

    def payroll_usd(self) -> float:
        return self.wait_hours_per_day() * self.hourly_rate_usd

    def paid_usd(self) -> float:
        return (self.calls_per_day * self.tokens_per_call / 1000.0) * self.paid_usd_per_1k

    def free_is_wrong(self) -> bool:
        if self.kind == "batch" and self.humans_blocked <= 0:
            return False
        if self.kind == "interactive" and self.free_p95_s > 8:
            return True
        if self.kind == "ci_gate" and self.free_p95_s > 30:
            return True
        return self.payroll_usd() > self.paid_usd() and self.paid_usd() > 0


def load_paths(raw: str) -> list[Path]:
    data = json.loads(raw)
    return [Path(**row) for row in data]


def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--config", required=True, help="JSON array of Path objects")
    args = p.parse_args()
    paths = load_paths(open(args.config, encoding="utf-8").read())
    print(f"{'path':22} {'kind':12} {'payroll/day':>12} {'paid/day':>10} {'verdict'}")
    for path in paths:
        verdict = "PAY" if path.free_is_wrong() else "FREE_OK"
        print(
            f"{path.name:22} {path.kind:12} "
            f"{path.payroll_usd():12.2f} {path.paid_usd():10.2f} {verdict}"
        )


if __name__ == "__main__":
    main()

A starter config looks like this. Replace every number. Especially hourly_rate_usd and the two p95 fields. If you leave fiction in those slots, the script will launder it into a confident-looking table.

[
  {
    "name": "pr-bot-review",
    "kind": "ci_gate",
    "calls_per_day": 80,
    "tokens_per_call": 2500,
    "free_p95_s": 47,
    "paid_p95_s": 4,
    "humans_blocked": 1.5,
    "hourly_rate_usd": 95,
    "paid_usd_per_1k": 0.003
  },
  {
    "name": "ide-inline-complete",
    "kind": "interactive",
    "calls_per_day": 400,
    "tokens_per_call": 800,
    "free_p95_s": 12,
    "paid_p95_s": 1.2,
    "humans_blocked": 1,
    "hourly_rate_usd": 95,
    "paid_usd_per_1k": 0.003
  },
  {
    "name": "nightly-eval-sweep",
    "kind": "batch",
    "calls_per_day": 2000,
    "tokens_per_call": 1500,
    "free_p95_s": 90,
    "paid_p95_s": 6,
    "humans_blocked": 0,
    "hourly_rate_usd": 95,
    "paid_usd_per_1k": 0.003
  }
]

Run it as a boring command, not a ritual.

python3 break_even.py --config paths.json

You still need real p95s. A proposed timing harness, pointed at whatever endpoint you already use, is enough to stop guessing. Twenty sequential calls will not give you a journal-grade distribution. They will tell you if you are in seconds or in coffee-refill territory.

ENDPOINT="${LLM_URL:?set LLM_URL}"
: > /tmp/latencies.txt
for i in $(seq 1 20); do
  /usr/bin/time -f "%e" -o /tmp/latencies.txt -a \
    curl -sS -o /dev/null -w "" --max-time 120 "$ENDPOINT" || echo 120 >> /tmp/latencies.txt
done
sort -n /tmp/latencies.txt | awk 'END {print "n="NR}
  {a[NR]=$1}
  END {
    p50=a[int(NR*0.50)]; p95=a[int(NR*0.95)];
    print "p50=" p50, "p95=" p95
  }'

If you cannot name humans_blocked for a path, it is batch or you have not watched the work. Watch it for one afternoon. Count the people who cannot proceed until the call returns. That integer is more important than the token price you were arguing about in Slack.

How to read a PAY verdict

PAY does not mean “the free tier is bad.” It means this path should not live there. Keep the free route for the nightly sweep. Move the PR bot. Move the inline complete. The interesting decision is almost never all-or-nothing.

A FREE_OK on batch is not a moral victory either. Batch still fails. It just fails while you sleep. If tomorrow’s launch depends on last night’s sweep finishing before 9:00, that batch path quietly became CI-gated. Change the kind. Re-run the worksheet. The classification is the product.

Thresholds in the script are proposals: eight seconds for interactive, thirty for a gate. Your product may be stricter. A trading UI and a weekly digest do not share a nervous system. Edit the function. Do not treat my constants as policy.

Where a free lab still belongs

You still need somewhere to measure without parking the measurement on the production meter. That is the remaining honest use of free capacity: a scratch lane for the timing harness, the worksheet, and the prompt junk you should never send through a customer path.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode currently offers free model access and a free server option. Use those as a lab if they fit the workflow above. Do not use them as an SLA. The worksheet does not care which lobby you stood in. It cares whether a human was standing with you.

If you try that lab, keep the same discipline you would use anywhere else. Environment variables for the URL. No secrets in the gist. No assumption that today’s free lane will be tomorrow’s. Free is a snapshot of capacity, not a contract with your roadmap.

Limitations, said plainly

This method prices wait, not quality. A paid call that hallucinates faster is not a win. The script also ignores retries, cache hits, and the engineering hours you will spend maintaining two routes. Those are real costs. I left them out so the first version stays something you will actually run.

It cannot see quota cliffs, preemption, or a sudden policy change on a free lane. If your “p95” is a luck sample from a quiet Tuesday, you will under-price risk. Sample on a busy day. Sample when your region is awake. If you cannot sample, you do not have a p95. You have a hope.

It also assumes you have a paid alternative with a known price per thousand tokens. If you do not, the payroll column is still useful as a “how much are we donating to the queue” number. It is not a break-even. Do not fake a paid price to force a verdict.

Currency is another lie people sneak in. $95/hour is a stand-in. Use your fully loaded cost, not the salary number from a job post. If you do not know that number, ask whoever signs the contractor invoices. Guessing low makes free look smarter than it is.

Who should not take this bet the other way

If you are a solo hobbyist with no payroll and no customer waiting, stop. Free capacity is the correct bet. Go build. The overflow lot is for you.

If every call is already reserved capacity you pay for whether you use it or not, this worksheet is noise. You are deciding utilization, not routing.

If the path is safety-critical, regulated, or cannot tolerate a silent model swap, do not put it on a free lane in order to “save” tokens. That is not cost ops. That is hiding an availability decision inside a budget conversation.

And if your team will not log latency, this article will not save you. The arithmetic is the easy part. The hard part is admitting that the stand-up delay is the invoice.

Price the wait. Keep free capacity for work that can sleep. Move anything with a human or a release gate off the overflow lot the moment the worksheet says PAY. That is the whole policy. The rest is logging.

If you want a throwaway lane for the timing harness, MonkeyCode’s free model access and free server option are one place to run the same script. Treat the output as a lab notebook, then route production like you mean it.

Free Model Access Needs an Exit Plan: How to Test LLM Endpoints Without Betting Your Pipeline

Quinn Li — Wed, 02 Sep 2026 14:00:13 +0000

When a vendor offers free model access, the first instinct is to wire it in and start shipping. That instinct is why the project will later fail. Free capacity is a trial, not a warranty, and the only way to use it safely is to know exactly when you can afford to keep it and when you must replace it.

You need an exit plan before you need a model. That means running a small benchmark against any endpoint, including the free tier, and turning the results into a decision rule your whole team understands. The method below is deliberately boring: a script, three numbers, and one table.

To make this concrete, I used an open-source project called MonkeyCode as a test subject because it bundles free model access with a free server, which makes the experiment cost nothing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The point is not to praise the project; the point is to show how any free offer should be treated.

The Real Test

A free model endpoint often looks perfect in a single curl call. The real test is concurrency. When you send several requests at once, you discover how many can pass before the queue starts eating them. The script below measures exactly that against any OpenAI-compatible chat endpoint, so you can run it against MonkeyCode's free model server or any other provider.

import asyncio
import time
import aiohttp

BASE_URL = "https://your-endpoint.example/v1/chat/completions"
MODEL = "free-model-name"
PROMPT = "Return the word pong."

async def single(session):
    t0 = time.perf_counter()
    try:
        async with session.post(
            BASE_URL,
            json={"model": MODEL, "messages": [{"role": "user", "content": PROMPT}]},
            headers={"Authorization": "Bearer YOUR_KEY"},
            timeout=aiohttp.ClientTimeout(total=30),
        ) as resp:
            await resp.json()
            status = resp.status
    except Exception:
        status = -1
    return status, time.perf_counter() - t0

async def run(concurrency, total):
    async with aiohttp.ClientSession() as session:
        sem = asyncio.Semaphore(concurrency)
        async def worker(_):
            async with sem:
                return await single(session)
        start = time.perf_counter()
        results = await asyncio.gather(*[worker(_) for _ in range(total)])
        elapsed = time.perf_counter() - start
    return results, elapsed

if __name__ == "__main__":
    results, elapsed = asyncio.run(run(concurrency=10, total=30))
    ok = [r for r in results if r[0] == 200]
    latencies = sorted(r[1] for r in ok)
    success = len(ok) / len(results)
    p95 = latencies[int(len(latencies) * 0.95) - 1] if latencies else None
    rps = len(ok) / elapsed
    print(f"success={success:.0%} p95_ms={(p95 or 0) * 1000:.0f} rps={rps:.2f}")

Replace the URL and key with your own, then run it against any OpenAI-compatible provider. The three numbers you get — success ratio, p95 latency, and effective requests per second — tell you more than the price per token ever will.

Reading the Results

Run the script at least three times: once in the morning, once at peak hours, and once after a week, because free capacity has a habit of changing when you are not looking. A quiet test is meaningless; the metric that matters is how the endpoint behaves under simultaneous load.

Now put those numbers next to the workload you are actually building. This table shows the thresholds I have found useful in practice:

Workload	Free model acceptable?	Why
Cron job or batch report, can retry overnight	Yes, if success >= 95% and you can tolerate 10x latency spikes	Timeouts become retries, not user complaints
User-facing API with a 2-second SLA	Only if success >= 99.5% and p95 < 2s	Every slow response is a support ticket
Production data pipeline with deadlines	No, unless you have a paid failover ready	A shared free server can become a bottleneck without warning

The first row is where free model access genuinely shines. The second row is where many teams fool themselves: they test with one request, get a fast answer, and assume the user experience will match. The third row is a liability unless you already have an exit route.

Building the Exit Plan

The easiest way to keep an exit open is to make your LLM client read the endpoint and key from environment variables, then swap them when the free tier stops working. The snippet below is deliberately simple, yet it is enough to move from a free server to a paid provider without touching your business logic.

import os
import httpx

client = httpx.AsyncClient(
    base_url=os.getenv("LLM_BASE_URL"),
    headers={"Authorization": f"Bearer {os.getenv('LLM_API_KEY')}"},
)

# Later: call client.post("/v1/chat/completions", json=payload)

Set LLM_BASE_URL to the free endpoint while you prototype, and keep a paid endpoint ready in your deployment secrets. When the free tier degrades or the quota runs out, the only change is an environment variable, not a midnight rewrite.

Limitations

Free model access and free servers come with no SLA. Quotas can reset, endpoints can disappear, and a shared server can be flooded by users you will never meet. Never send personally identifiable information to a free endpoint, and never make your only retry path depend on the same free server you are testing. These offers are useful sandboxes, not infrastructure guarantees.

Before you trust any free offering, check its current terms yourself. Token counts, server availability, and model lists change often, and relying on outdated numbers will hurt more than the feature is worth. The script above works for any provider, so run it again whenever a free tier updates its configuration.

The cheapest request is the one that returns fast, and the safest free tier is the one you can leave. Measure it, set your thresholds, build the switch, and keep the free model access exactly where it belongs: as a tool, not as a promise.

The Retry Tax: When Free AI Capacity Stops Being Free

Quinn Li — Mon, 31 Aug 2026 12:16:48 +0000

Your first request timed out. You clicked retry. The second attempt sailed through. You just spent two round-trips, a couple thousand tokens, and eleven seconds to get one answer. The answer was free. The retry was not.

Free model access is a supply chain, not a gift. You pay for it with queueing, throttling, and the occasional timeout. The real cost is not the token balance. It is the hidden currency you spend on retries: time and repeated token burns.

Free tiers deserve credit for lowering the barrier. MonkeyCode, an open source project with a free model tier and a free server option, is one of those attempts. Disclosure: This article was prepared as part of MonkeyCode's product outreach. But wiring free capacity into production without thinking about retries will turn a generous quota into a lottery.

The math gets interesting quickly. If each attempt costs C tokens and succeeds with probability p, the expected attempts until success is 1/p when p stays constant. A 95% success rate gives about 1.05 attempts. A 70% success rate gives about 1.43 attempts. That is a 43% token tax. Time is worse: a 2-second success and a 30-second timeout blend into an average attempt time of 2p + 30(1-p). At p=0.7, that is 10.4 seconds per attempt, times 1.43 — almost fifteen seconds per success. At p=0.95, about 2.1 seconds. Free capacity can be seven times slower before counting a single queued request.

Why does success rate drop in the first place? Free tiers often run on shared infrastructure. Cold starts, noisy neighbors, and rate limiters all turn a simple prompt into a flaky call. The prompt didn't change. The environment did.

You don't need a benchmark suite. You need a calculator. The script below turns your observed success rate into an effective cost per success.

#!/usr/bin/env python3
# retry_math.py - effective cost per successful LLM call

def expected_attempts(success_rate: float, max_retries: int = 5) -> float:
    attempts = 0.0
    fail = 1.0
    for _ in range(max_retries + 1):
        attempts += 1.0
        fail *= (1 - success_rate)
    return attempts

def avg_attempt_time(avg_success_latency: float, timeout: float, success_rate: float) -> float:
    return avg_success_latency * success_rate + timeout * (1 - success_rate)

def report(success_rate: float, tokens_per_call: int,
           avg_success_latency: float = 2.0, timeout: float = 30.0,
           max_retries: int = 5) -> None:
    ea = expected_attempts(success_rate, max_retries)
    eff_tokens = tokens_per_call * ea
    eff_time = ea * avg_attempt_time(avg_success_latency, timeout, success_rate)
    print(f"success_rate={success_rate:.2f}  attempts={ea:.2f}  tokens={eff_tokens:.1f}  time={eff_time:.1f}s")

report(0.95, 1000)   # healthy
report(0.70, 1000)   # throttled
report(0.50, 1000)   # broken

Run it with your own numbers. At 1000 tokens per call, a healthy 0.95 success rate yields around 1050 tokens and 2.1 seconds per success. At 0.70, the same call costs roughly 1430 tokens and 14.9 seconds. That is the retry tax in numbers.

But the script assumes independent attempts. Real retries need exponential backoff and jitter, or you will turn a transient error into a self-inflicted outage. Use a backoff policy: start at 1 second, double it, cap at 30 seconds, and add random jitter. That way the retry loop doesn't become the new load balancer.

The tax compounds when queues get involved. A failed call re-enqueues; the next attempt adds load while the first one is still timing out. That is how a free-tier hiccup becomes a retry storm. Teams end up watching a dashboard they didn't need because their retry loop was hammering an endpoint that was already down. The endpoint was not the problem. The retry loop was.

Here is a practical gate: measure your actual success rate over at least a hundred production-like calls with real prompt sizes. If the success rate is below 0.9, calculate the effective time per success. If that number exceeds your user-visible latency budget, free capacity is the wrong bet. Paid per-token APIs or a self-hosted model with a fixed cost can give you predictable latency and deterministic concurrency. That predictability is often worth the price.

This approach is not for everyone. It is wrong for workloads with a hard latency SLO, for batch jobs that must finish before a deadline, or for prompts where retrying produces inconsistent results. In those cases, free capacity is not free. It is a tax on your time to market.

MonkeyCode's free model access and free server option are a decent sandbox for measuring that trade-off. You can test a workflow, plot your retry curve, and decide whether the free tier fits. Just don't mistake the quota for your budget. Your budget is the number of attempts that actually succeed.

If you try the script, bring your own retry count. And if you want to explore the project, the MonkeyCode repository is a reasonable place to start.

Free Tokens Are a Tool, Not a Promise: Measure Before You Build

Quinn Li — Sun, 30 Aug 2026 11:21:26 +0000

Free tokens look generous. They're not a contract. They're an experiment. When you wire a free LLM quota into production, you're betting your response time and your team's sleep on someone else's queue. That can be a fine bet. But it needs to be measured, not assumed.

In the past month I've watched two teams adopt free model access for their core feature. Both saw a demo that worked. Both hit latency spikes when traffic doubled. One recovered after adding retries; the other spent a full sprint rewriting the worker to batch requests. The cheap tokens cost them a week of engineering.

MonkeyCode (Disclosure: This article was prepared as part of MonkeyCode's product outreach.) is an open-source project that offers free model access and a free server tier. It's a genuine option for experiments, prototypes, and background jobs that can tolerate delays. But "free" always comes with constraints: shared queues, rate limits, and availability that you don't control. The question isn't whether the quota is enough. It's whether your workload can survive the queue.

Before you adopt any free tier, run a load test that mimics your real usage. Here's a Python script that hits an endpoint under concurrency and reports the metrics that matter: success rate, latency percentiles, and retry overhead.

import asyncio, time, statistics, httpx

async def call(client, url, payload, attempt):
    start = time.perf_counter()
    try:
        r = await client.post(url, json=payload, timeout=30)
        return {'ok': 200 <= r.status_code < 300, 'ms': (time.perf_counter()-start)*1000, 'attempt': attempt}
    except Exception:
        return {'ok': False, 'ms': None, 'attempt': attempt}

async def run_load(url, payload, concurrency, total, max_retries=3):
    async with httpx.AsyncClient() as client:
        sem = asyncio.Semaphore(concurrency)
        async def worker(i):
            for attempt in range(1, max_retries+1):
                async with sem:
                    result = await call(client, url, payload, attempt)
                if result['ok'] or attempt == max_retries:
                    return result
                await asyncio.sleep(0.5 * attempt)
            return result
        results = await asyncio.gather(*[worker(i) for i in range(total)])
    return results

def summarize(results, total):
    ok = [r for r in results if r['ok']]
    lats = sorted([r['ms'] for r in ok])
    p95 = lats[int(len(lats)*0.95)] if lats else None
    extra_retries = sum(r['attempt'] - 1 for r in results)
    success = len(ok) / total * 100
    total_ms = sum(r['ms'] for r in ok)
    effective = len(ok) / max(total_ms / 1000, 0.001)
    return {'success_pct': success, 'p95_ms': p95, 'extra_retries': extra_retries, 'effective_req_per_sec': effective}

Run it against your endpoint with a concurrency that matches your peak traffic. Let's say you see 70% success, a p95 of 8 seconds, and 30 extra retries across 200 calls. That's not a free lunch; that's a broken user experience. Every retry burns tokens and wall-clock time, so your "free" throughput actually costs you compute, storage, and developer attention.

Don't stop at latency. Track token usage per call. Most providers return usage in the response body. Sum those numbers too. A retry storm can triple your consumption, turning a free quota into a paid one in an afternoon. Here's a tiny addition to the script: after each successful response, parse data.usage.total_tokens and add it to a running total. If that total exceeds your quota, you've just learned the free limit is too tight for your pattern.

Now the decision. Use the free tier only when all of these hold: your success rate stays above 98%, p95 latency stays under two seconds, and retries add less than 20% overhead. If any of those fail, the free tier is the wrong bet for that workload. You can encode this as a guard:

def recommend_plan(success_pct, p95_ms, retry_overhead_pct):
    if success_pct < 98: return "avoid free, need SLA"
    if p95_ms > 2000: return "avoid free, latency sensitive"
    if retry_overhead_pct > 20: return "avoid free, retry storm"
    return "free is acceptable"

That's a heuristic, not a law. Some workloads don't care about a 30-second p95. If you're running a nightly report that fails at 3 AM and nobody notices until morning, free capacity is fine. If you're serving a chatbot where users tap "send" and wait, every extra second is a drop-off.

MonkeyCode's free server option can still be a sweet spot for internal tools. You keep data on your own infra, you validate a model's behavior without committing to a paid plan, and if the queue misbehaves, you lose a batch run, not a customer. But the same measurement rules apply. Instrument every call. Track queue length, retry counts, and total job time. Graph them over a week, not an hour.

One more thing: free quotas change. Providers can adjust rate limits, deprioritize free users, or introduce background maintenance windows. That's why you need an exit path. Design your code so the endpoint is a config value, not a hard-coded constant. Then switching from free to paid or self-hosted is a one-line change.

Also remember that free server tiers often share hardware. A neighbor's noisy workload can slow your inference, and there's nothing you can do except test at your own peak time. If your business depends on consistent sub-second responses, free capacity is a trap.

I'm not saying avoid free capacity. I'm saying treat it like a staging environment: useful, but not production. Measure first. Build a retry budget. Know your exit path before you need it. The cheapest token in the world is expensive if it wakes you up at 3 AM.

Cheap Tokens, Expensive Waiting Rooms

Quinn Li — Sat, 29 Aug 2026 10:29:54 +0000

Free model access is not a discount. It is a queue you pay for with wall-clock time, and wall-clock time has a salary.

The platform debates of the last week asked what developers do while AI codes, and whether AI promoted everyone to reviewer. The sharper question sits one layer down: what does your program do while the token queue fills? If the answer is "retry, then retry again," your cost model is missing the most expensive line item — your own attention.

When you accept free capacity, you accept three budgets at once: tokens, latency, and attention. Most teams model the first and ignore the other two. Then a batch job that should take forty minutes takes six hours because every retry re-enters the same queue, the same concurrency cap, the same unlucky afternoon. The tokens were free. Your evening was not.

I have been poking at MonkeyCode, an open-source project that offers free model access and a free server slot. The operator's free tier as of 2026-08-29 includes ten million tokens and a server you do not pay for. Treat that as a snapshot, not a law — quotas change without a blog post, so check the repo before you build on it. What makes the project useful here is not the handout. It is that the free tier hands you a real token meter and a real queue to test your assumptions against. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

The assumption I see most often is straight multiplication: calls times tokens per call equals price. That math omits retries, queueing, and the human watching a progress bar. So I built a small discrete-event simulator, no dependencies, around one question: what is the probability that a workload finishes within a wall-clock budget on shared free capacity?

#!/usr/bin/env python3
"""Queue bet: should this workload run on free capacity?

Models Poisson arrivals, token consumption, retries, and a wall-clock
budget. Prints the probability of finishing in time.
"""
import argparse
import random

def simulate(arrivals, tokens_per_call, free_tokens, workers,
             retry_delay, budget, sims=3000):
    hits = 0
    for _ in range(sims):
        events = []
        t = 0.0
        for _ in range(arrivals):
            t += random.expovariate(2.0)  # 2 arrivals/sec average
            events.append(t)

        tokens = free_tokens
        free_at = [0.0] * workers
        finished = True

        for arrive in events:
            start = max(arrive, min(free_at))
            retries = 0
            while tokens < tokens_per_call and retries < 4:
                start += retry_delay
                retries += 1
                tokens = free_tokens
            if retries == 4:
                finished = False
                break

            finish = start + tokens_per_call / 1500.0
            tokens -= tokens_per_call
            free_at[free_at.index(min(free_at))] = finish

            if finish > budget:
                finished = False
                break

        if finished:
            hits += 1
    return hits / sims

def main():
    p = argparse.ArgumentParser()
    p.add_argument("--arrivals", type=int, default=120)
    p.add_argument("--tokens-per-call", type=int, default=4000)
    p.add_argument("--free-tokens", type=int, default=10_000_000)
    p.add_argument("--workers", type=int, default=2)
    p.add_argument("--retry-delay", type=float, default=2.0)
    p.add_argument("--budget", type=float, default=300.0)
    a = p.parse_args()
    p_ok = simulate(a.arrivals, a.tokens_per_call, a.free_tokens,
                    a.workers, a.retry_delay, a.budget)
    print(f"P(finish within {a.budget:.0f}s) = {p_ok:.2f}")

if __name__ == "__main__":
    main()

The model is deliberately crude: arrivals are Poisson, each worker serves tokens at a fixed rate, and any call that cannot get tokens retries after a delay and then re-checks the meter. It is not a benchmark. It is a decision aid. Change one number and you will watch the probability collapse.

A typical run looks like this (the exact numbers vary with the random seed; the shape does not):

$ python3 queue_bet.py --arrivals 120 --tokens-per-call 4000 --budget 300
P(finish within 300s) = 0.52

$ python3 queue_bet.py --arrivals 120 --tokens-per-call 4000 --budget 900
P(finish within 900s) = 0.91

At a five-minute deadline, the job fails roughly half the time. At fifteen minutes it mostly lands, but the tail still stretches past the point where a human stopped checking.

This is where the cost-ops decision lives. You are not choosing between free and paid tokens. You are choosing between uncertain wall-clock and certain wall-clock. Paid capacity wins the instant the expected hours of waiting exceed the price of a few million tokens, and for a small batch that happens sooner than most people think. The crossover is even faster when your time is the expensive, interruptible kind.

So I turned the whole thing into a gate rather than a feeling: fix a deadline, run three thousand simulations, and accept free capacity only when the finish probability clears 0.95. Below that, schedule the job off-peak, buy a small burst of paid calls, or split it into chunks that fit the concurrency instead of hoping the queue is kind. Free capacity is the right bet for exploration, prototypes, background syncs, and load-test garbage you are happy to lose. It is the wrong bet for anything with a hard deadline, a real-time user, or a revenue path, because retries compound and the queue does not care about your demo.

One trick worth stealing from our earlier failure-chasing: treat the free tier as a chaos box. When the queue stalls, do not curse it. Measure your recovery time, your retry budget, your alert delay. A free queue that fails is not a bug report; it is a free load test with production-shaped traffic.

Limitations are real. The simulator assumes Poisson arrivals and a fixed service rate; real queues have diurnal waves and hidden concurrency caps. The ten-million-token figure is an operator snapshot, not a contract, and a free server is never an SLA. If your workload cannot tolerate a lost batch, free capacity should not carry it. That is the whole point of the gate.

Now about that promotion you got last week. AI did not promote you to reviewer. It promoted you to queue manager. The moment you accept that job, the math above stops being academic. It becomes the difference between a pleasant evening and a six-hour wait for a job that should have taken forty minutes.

The script is short enough to read in one coffee break, and the debt it removes is the one that actually compounds: guessing. If you want a real token meter plus a free place to run this against reality, the MonkeyCode repo is a fork away. Bring your own deadline.

Free Tokens Are a Queue, Not a Wallet

Quinn Li — Fri, 28 Aug 2026 03:57:35 +0000

Free tokens look like a discount. In operations terms, they are usually a queue. The difference will decide whether your weekend stays free as well.

Every free-capacity offer hides the same trade: you pay with time instead of money. Requests wait for a slot. Slots arrive in bursts. When the burst ends, your SDK backs off, retries, and waits again. The bill stays at zero. The wall clock does not.

I keep coming back to this because of the current DEV debates about benchmark scores — the ones asking whether a number measures the model or the harness it runs in. Cost has the same twin problem. Accuracy answers "can it answer?" Throughput, retries, and queue wait answer "can it answer before my user leaves?" Both questions matter. Most of us measure only the first.

This is where MonkeyCode's offer becomes worth an ops conversation, not a marketing one. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The open-source project provides free model access — at the time of writing, 10 million tokens — along with a free server option for running it yourself. Notice what I am not doing: quoting a benchmark. I have not run one, and you should not trust a number in a blog post anyway. Quotas change. Queues change. Terms change. What you can trust is a measurement you take yourself against the actual endpoint.

The workflow has two steps. First, a single-shot latency check with curl, so you know the endpoint is even alive before you load it:

curl -w "total=%{time_total}s\n" -o /dev/null \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"your-model","messages":[{"role":"user","content":"Say ok"}],"max_tokens":8}' \
  https://your-endpoint/v1/chat/completions

Second, a small probe that replays the load pattern you actually care about. You need four numbers — p50 latency, p95 latency, retry count, and behavior at modest concurrency. The script below measures all of them against any chat-completions-style JSON API, MonkeyCode's free tier included:

#!/usr/bin/env python3
"""Cost-ops probe: measure queueing, retries, and latency shape."""
import json
import time
import urllib.error
import urllib.request
from concurrent.futures import ThreadPoolExecutor

ENDPOINT = "https://your-endpoint/v1/chat/completions"
API_KEY = "your-key"
MODEL = "your-model"
REQUESTS = 20
CONCURRENCY = 4
MAX_RETRIES = 4

def call_once(i):
    body = json.dumps({
        "model": MODEL,
        "messages": [{"role": "user", "content": "Reply with the word ok."}],
        "max_tokens": 8,
    }).encode()
    req = urllib.request.Request(ENDPOINT, data=body, headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json",
    })
    t0 = time.perf_counter()
    retries = 0
    while True:
        try:
            with urllib.request.urlopen(req, timeout=30) as resp:
                json.load(resp)
            return {"ok": True, "latency": time.perf_counter() - t0,
                    "retries": retries}
        except urllib.error.HTTPError as e:
            if e.code == 429 and retries < MAX_RETRIES:
                retries += 1
                time.sleep(2 ** retries)
                continue
            return {"ok": False, "status": e.code,
                    "latency": time.perf_counter() - t0, "retries": retries}
        except urllib.error.URLError:
            return {"ok": False, "status": "network",
                    "latency": time.perf_counter() - t0, "retries": retries}

with ThreadPoolExecutor(max_workers=CONCURRENCY) as pool:
    results = list(pool.map(call_once, range(REQUESTS)))

ok = sorted(r["latency"] for r in results if r["ok"])
p50 = ok[len(ok) // 2] if ok else float("nan")
p95 = ok[int(len(ok) * 0.95)] if ok else float("nan")
wait = sum(2 ** r["retries"] for r in results if r["retries"])
print(f"requests={REQUESTS} concurrency={CONCURRENCY}")
print(f"ok={len(ok)} failed={len(results) - len(ok)}")
print(f"p50={p50:.2f}s p95={p95:.2f}s")
print(f"retries={sum(r['retries'] for r in results)} backoff_wait={wait:.0f}s")

Run it:

python3 probe.py

Then read the shape, not the average. If p95 sits at three times p50, your requests are queueing somewhere. If retries are non-zero, part of your "free" call cost is sleep. If failures appear at concurrency four, that capacity is a shared parking lot where everyone else is circling too. Pick a sample size that mirrors your real traffic; twenty requests at concurrency four is a smoke test, not a load test. If you expect one hundred concurrent users, raise both numbers and run it overnight.

Here is the failure mode I see most often. A team wires a free endpoint into its agent loop because each call is tiny. Then the agent calls the model four times per user turn, and every call races the same rate limit. The first call succeeds, the second hits 429, the retry sleeps long enough that the whole turn times out. Token cost: zero. User-visible failure: total. A single measurement misleads you; you have to measure the loop.

Interpretation is where the decision lives. Free tokens are the right bet for jobs that tolerate waiting: nightly evaluation runs, dataset labeling, embedding backfills, smoke tests that may retry for an hour. A batch job that finishes at 3 a.m. instead of 2 a.m. costs you nothing. A user chat that pauses for eight seconds costs you a user.

That is the whole thesis. Free capacity is not a wallet you spend; it is a queue you join. When the value of your workload depends on arrival time — interactive chat, tool-calling agents, anything on a user request path — free tokens are the wrong bet, and a paid lane is the honest price. When the value depends on completion rather than arrival, free capacity is hard to beat, and MonkeyCode's 10-million-token access plus its free server is a sensible place to park that workload. You can stand the server up yourself, wire it to your batch pipeline, and read real numbers before spending an hour of engineering time.

One more ops caveat. Free capacity is a moving target in a way paid capacity is not. A quota can shrink, a queue can lengthen, and a generous tier can change direction with the project. Treat 10 million tokens as today's snapshot, not next quarter's contract. If your pipeline cannot survive a sudden change in access, it should not sit on free capacity at all.

Who should not use this approach? Anyone with a hard latency SLO. Any pipeline where a retry triggers expensive recomputation downstream. Any team that cannot absorb a quota change without warning. And, honestly, anyone whose engineering hour costs more than the token bill would — optimizing a twenty-cent invoice for two hours is not cost ops, it is a hobby.

The fair way to decide is the probe above. Clone MonkeyCode's repo, point its free server at your workload, and let p95 make the call instead of a README. If the number fits your tolerance, use it. If it does not, you just avoided a debugging session. That avoided session was the real cost all along.

Free Tokens, Real Queues: Measure What Your LLM Calls Actually Cost

Quinn Li — Wed, 26 Aug 2026 11:20:51 +0000

Free tokens are not free. They are a queue you join with your time, your retries, and your patience. Before you route any real workload through a free model endpoint, measure what the queue actually costs you.

The same mistake shows up in batch jobs all the time. Someone finds a free tier with a generous allowance, points their pipeline at it, and celebrates the zero on the invoice. Then the rate limiter answers the first burst with 429s, the client retries with exponential backoff, and the "free" job runs three times longer than the paid one would have. The invoice is still zero. The clock is not.

MonkeyCode is an open-source project that pairs free model access with a free server option — the kind of offer that looks unbeatable until you count the wall-clock. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I am not here to sell you on it. I am here to give you a way to test whether its free capacity is actually cheap for your workload.

The claim, as of this writing, is simple: a free allowance of 10 million tokens, plus a server you do not pay for. Whether that is a good deal depends entirely on your traffic pattern. A free allowance is a budget, not a guarantee. You are sharing the endpoint with everyone else who found the same deal, and their bursts become your latency.

So stop guessing. Measure.

Here is a small batch runner that keeps a ledger of everything that matters: wall time, retries, and tokens. It treats the endpoint as a black box, because that is what it is.

import json
import time
from openai import OpenAI

client = OpenAI(base_url="YOUR_ENDPOINT", api_key="YOUR_KEY")

def run_batch(prompts, model, max_retries=4):
    ledger = []
    wall_start = time.monotonic()

    for i, prompt in enumerate(prompts):
        attempts = 0
        t0 = time.monotonic()
        while True:
            try:
                resp = client.chat.completions.create(
                    model=model,
                    messages=[{"role": "user", "content": prompt}],
                )
                ledger.append({
                    "index": i,
                    "attempts": attempts + 1,
                    "wall_ms": round((time.monotonic() - t0) * 1000, 1),
                    "tokens": resp.usage.total_tokens,
                })
                break
            except Exception:
                attempts += 1
                if attempts >= max_retries:
                    raise
                time.sleep(min(2 ** attempts, 30))

    total_wall = time.monotonic() - wall_start
    tokens = sum(entry["tokens"] for entry in ledger)
    retries = sum(entry["attempts"] - 1 for entry in ledger)

    print(json.dumps({
        "items": len(ledger),
        "total_wall_s": round(total_wall, 1),
        "total_tokens": tokens,
        "tokens_per_s": round(tokens / total_wall, 1),
        "retries": retries,
        "retry_rate": round(retries / len(ledger), 3),
    }, indent=2))

Feed it a representative sample of your real prompts, not a toy. Fifty documents that look like your production traffic are worth more than five hundred that do not. Run the same sample against the free endpoint and against whatever you pay for today, then compare the two JSON summaries.

The number that matters is tokens per second, not tokens per dollar. If the free endpoint gives you, say, 40 tokens per second and your paid one gives you 400, then a job that costs you nothing in money costs you ten times longer in time. For a one-off script, that trade is fine. For a pipeline that runs every night, it is a tax you pay forever.

Retries deserve their own line in the ledger. Treat a retry rate above five percent as a warning sign: it usually means you are the queue, not a user of it. Watch how retries cluster. If they all happen in the first minute of the run, you are bursting too hard, and a simple rate limiter on your side will flatten the curve. If they are spread evenly, the endpoint is simply saturated, and no client-side trick will save you.

Run the same batch at three different times of day. Free endpoints have rush hours, and the ledger will show them as clearly as a traffic report. If your job can move to a quiet window, you just found free capacity that behaves like paid capacity.

There is one thing this script cannot tell you, and you should be honest about it. Wall time includes network hops, server queueing, and generation, all mixed together. You cannot separate queueing from generation with a plain client, so treat tokens per second as a composite number. It is still the right number to compare, because your pipeline experiences all of it as one delay.

When is free capacity the wrong bet? Three situations come to mind. First, interactive work: if a human is waiting on the response, a queue is a bug, not a bargain. Second, sustained traffic: a free allowance is a shared resource, and shared resources degrade exactly when you need them most — during your peak. Third, anything with a deadline: batch jobs that must finish before a downstream step starts should not depend on a queue you do not control.

Who should not use this approach at all? Teams with hard service-level agreements, real-time features, or workloads that already saturate a free tier. If your retry rate is climbing week over week, the answer is not a better client; it is a paid endpoint or a self-hosted model. Free capacity is an experiment, not an architecture.

The honest way to evaluate MonkeyCode, or any free tier, is to run the ledger and read the numbers. If the free server and the 10-million-token allowance keep your tokens per second high enough for your actual workload, you have found a genuinely cheap option. If not, you have found a cheap way to learn what your time is worth. Either result is useful, and it costs you nothing but a few minutes of measurement.

Chaos for the Cost-Conscious: Fault-Injecting Your LLM Pipeline on Free Hardware

Quinn Li — Tue, 25 Aug 2026 09:41:43 +0000

Free infrastructure is unpredictable. That is not a bug — it is a testing opportunity. Chaos engineering is usually described as a luxury for teams with dedicated SREs and production clusters. The opposite is true. The systems that need fault injection the most are the ones running on free servers and free model access, because their failure modes are the most varied and the least documented.

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

Why free hardware is the best chaos lab

A paid API gives you a contract. A free tier gives you a probability distribution. Rate limits appear without warning. Timeouts stretch past your patience. The occasional 5xx arrives just when your batch job reaches hour three. You cannot fix these failures — they are the price of free access. But you can learn exactly how your code behaves when they happen.

That learning is what chaos testing is for. You deliberately break things in a controlled way so that when they break for real, the recovery path is already muscle memory. The trick is that you do not need a dedicated test cluster. The free server you already have is the perfect place to break things on purpose.

A fault-injection proxy in one file

The cleanest way to inject failures is not to modify your application code. It is to stand between your code and the model API with a small proxy that decides, per request, whether to fail and how. This keeps your production code untouched while giving you full control over the chaos.

# chaos_proxy.py — inject failures into any OpenAI-compatible API call
# usage: python chaos_proxy.py --port 8765 --upstream https://api.example.com/v1
# then point your client at http://localhost:8765/v1

import argparse
import json
import random
import time
import urllib.request
from http.server import BaseHTTPRequestHandler, HTTPServer

class ChaosHandler(BaseHTTPRequestHandler):
    rate_limit_prob = 0.0
    timeout_prob = 0.0
    error_prob = 0.0
    upstream = ""

    def do_POST(self):
        r = random.random()
        if r < self.rate_limit_prob:
            self.send_response(429)
            self.end_headers()
            self.wfile.write(b'{"error": {"message": "rate limit (injected)"}}')
            return
        if r < self.rate_limit_prob + self.timeout_prob:
            time.sleep(15)  # longer than your client's timeout
            return
        if r < self.rate_limit_prob + self.timeout_prob + self.error_prob:
            self.send_response(500)
            self.end_headers()
            self.wfile.write(b'{"error": {"message": "internal error (injected)"}}')
            return

        length = int(self.headers.get("Content-Length", 0))
        body = self.rfile.read(length)
        req = urllib.request.Request(
            self.upstream + self.path,
            data=body,
            headers=dict(self.headers),
            method="POST",
        )
        try:
            with urllib.request.urlopen(req, timeout=30) as resp:
                data = resp.read()
                self.send_response(resp.status)
                for k, v in resp.headers.items():
                    self.send_header(k, v)
                self.end_headers()
                self.wfile.write(data)
        except Exception as e:
            self.send_response(502)
            self.end_headers()
            self.wfile.write(str(e).encode())

    def log_message(self, fmt, *args):
        print(f"[chaos] {self.address_string()} {fmt % args}")

def main():
    p = argparse.ArgumentParser()
    p.add_argument("--port", type=int, default=8765)
    p.add_argument("--upstream", required=True)
    p.add_argument("--rate-limit-prob", type=float, default=0.05)
    p.add_argument("--timeout-prob", type=float, default=0.03)
    p.add_argument("--error-prob", type=float, default=0.02)
    args = p.parse_args()

    ChaosHandler.rate_limit_prob = args.rate_limit_prob
    ChaosHandler.timeout_prob = args.timeout_prob
    ChaosHandler.error_prob = args.error_prob
    ChaosHandler.upstream = args.upstream

    server = HTTPServer(("localhost", args.port), ChaosHandler)
    print(f"chaos proxy on :{args.port} -> {args.upstream}")
    print(f"probabilities: rate_limit={args.rate_limit_prob} timeout={args.timeout_prob} error={args.error_prob}")
    server.serve_forever()

if __name__ == "__main__":
    main()

Run it with --rate-limit-prob 0.2 and watch your agent loop stumble. That stumble is the information you need.

Three experiments worth running

Start with the rate limit experiment. Set the probability to 0.2 and run your normal workload. Watch what happens. Most clients retry immediately, which makes the 429 worse. The correct response is exponential backoff with jitter — a random delay that grows with each retry. If your code does not do that, the proxy will show you exactly how fast the retry storm builds.

The timeout experiment is more subtle. A 15-second delay does not always trigger your client's timeout. Some HTTP libraries default to 30 seconds, some to 60, and some to infinity. The proxy makes the mismatch visible. You will learn your real timeout budget, not the one you assumed.

The 500 experiment tests your checkpointing. When a call fails mid-run, does your job resume from the last completed step or restart from zero? On a free server, this distinction is the difference between a 10-minute recovery and a 3-hour redo.

What the failures teach you

Run the proxy for an hour against a representative workload and record three numbers: the percentage of requests that failed, the time to recover from each failure, and the tokens wasted on retries. The last number is the one nobody measures. A retry re-sends the full conversation history, so a single 429 can cost you ten times the tokens of the original call.

That is the hidden economics of free access. The quota is not consumed by the work itself. It is consumed by the failures. A 5% failure rate with naive retries can inflate your token burn by 20-30%. Chaos testing turns that invisible cost into a number you can see and fix.

Who should skip this

If your workload is a single synchronous call with no retry logic, a proxy adds nothing — you already know what a failure looks like. If you are building a user-facing assistant where a 15-second delay is itself a product failure, chaos testing will only confirm what you already know: you need paid access with a latency guarantee. And if you cannot tolerate any artificial failures in your pipeline, run the experiments in a separate environment, not against your live job.

Chaos testing is not about making free infrastructure look bad. It is about making your code honest about what it can survive. A free server plus free model access gives you a place to run that honesty check without spending a dollar. Break things on purpose, measure the recovery, and fix the retry logic before the real rate limit arrives.

If you want a place to run these experiments, MonkeyCode's free server and free model access are a practical starting point. Point the proxy at their API, set the probabilities, and see what your code actually does under pressure.