DEV Community: bestbee

Lease or Buy Your AI Coding Agent? A 4-Variable Break-Even Model (With an Exit Plan)

bestbee — Wed, 19 Aug 2026 12:39:12 +0000

Every quarter, I talk to a platform lead who just "saved" $800 a month by self-hosting a coding model. Then they open the time log: eleven hours a month of GPU restarts, image rebuilds, and a pager rotation nobody wanted. At a blended $120 per engineering hour, that's $1,320 of invisible spend.

The self-hosted setup wasn't cheaper. It was just a different invoice.

I see the same mistake in both directions. Teams compare "free hosted" and "self-hosted" as if they were line items on the same bill. They're not. They're two different cost structures with different break-even points — and that break-even moves every time your token volume, ops load, or exit cost changes.

So here's the model I use when a team asks: should we take the free hosted path, or run our own stack?

MonkeyCode is one of the options on the free side right now. It's an open-source project, and its current offer includes free model access with a 10-million-token allowance plus a free server option, as of this writing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. But don't take my word for either side. Run the numbers below first.

The four variables

Every "free vs. self-hosted" debate reduces to four numbers:

Variable	Meaning	Unit
V	tokens consumed per month	tokens/month
P	price per token if you bought them	$/token
O	ops hours per month to run your own stack	hours/month
C	blended cost of an engineering hour	$/hour

Plus two secondary numbers: H, the monthly amortized hardware cost of self-hosting, and M, the one-time cost of migrating off the free path later.

Monthly cost of the free hosted path:

F = 0 (tokens) + 0 (ops) + R

where R is your risk premium — the subjective cost of quota changes, price changes, or the offer disappearing.

Monthly cost of the self-hosted path:

S = V × P + O × C + H

The decision rule is boring on purpose: choose free hosted while F < S. The interesting part is that the inequality flips faster than most teams expect.

A worked example

Take a six-person team. They burn 3 million tokens a month. A comparable paid model runs about $6 per million tokens on a typical list price — plug in your own provider's number, because this is an assumption, not a quote.

V = 3,000,000 tokens/month
P = $0.000006/token
O = 8 hours/month (updates, monitoring, restarts)
C = $100/hour
H = $200/month (amortized GPU or rented box)

S = 3 × $6 + 8 × $100 + $200 = $18 + $800 + $200 = $1,018/month

The free path costs $0 plus a risk premium. Even if you price the risk at $300/month — the offer changes, the quota shrinks, the server gets slow — free hosted wins by roughly $700/month. Easy call.

Now flip the team. A 40-person platform org, 80 million tokens a month, and a platform team that already runs Kubernetes.

V = 80,000,000 tokens/month
P = $0.000006/token
O = 3 hours/month (they have automation)
C = $100/hour
H = $0 (spare capacity)

S = 80 × $6 + 3 × $100 = $480 + $300 = $780/month

Now the free path only wins if your risk premium stays below $780. For a 40-person org, that premium — quota cliffs, data controls, migration effort — can easily exceed it. Same formula, different answer. The decision is structural, not ideological.

Sensitivity: where does it flip?

Here's the table I actually show teams. It answers: "at what token volume does self-hosting beat free hosted, given my ops load?"

Ops hours/month	Break-even token volume (at $6/M tokens, $100/hr)
2	33.3M tokens/month
5	83.3M tokens/month
10	166.7M tokens/month
20	333.3M tokens/month

The formula behind it: V × $6/M = O × $100, so V = O × 16.7M. Below the line, free hosted wins on pure math. Above it, you're paying more for tokens than the ops would cost.

The punchline: at typical token prices, one hour of ops equals about 16.7 million tokens. Most teams overestimate their token bill and underestimate their ops bill. That's why the free server option matters more than the free tokens — the server removes the expensive variable.

Four hard gates

The math is necessary but not sufficient. Run these four gates before you commit:

Token volume gate. If V × P already exceeds O × C + H, the free path is leaving money on the table.
Ops capacity gate. If you don't have 5+ spare hours a month for maintenance, free hosted wins even when the token math says otherwise. Your team's time is the real budget.
Data boundary gate. If code or prompts must not leave your environment, free hosted is disqualified. No risk premium can fix a compliance violation.
Exit cost gate. If M — the cost of migrating off the free path later — is larger than six months of savings, the free path is a trap, not a deal.

The exit plan you need anyway

Here's the part most free-tier users skip, and it's the part that turns a good deal into a bad one.

Keep a portability ledger in your repo: prompts, custom instructions, tool configurations, and model-specific workarounds. Log token usage weekly, not monthly. Set a review date — 90 days out — where you re-run the break-even with real numbers instead of estimates. And define the archive rule: what happens to your agent's history and artifacts if you leave?

If the free path disappears and your entire workflow is entangled with it, M explodes. That's the hidden price of free. It's not in the token bill; it's in the exit.

Who should not use this approach

Be honest about the limits:

Teams with hard data-residency or compliance requirements should not use free hosted AI, period.
Teams already near a quota cliff should treat the free allowance as a trial, not a production plan.
Teams that need a guaranteed SLA for agent output should budget for the paid path from day one.

The numbers in this article are assumptions, not quotes. Token prices drift, free offers change, and the 10-million-token allowance and free server I mentioned are current as of this writing — not a promise about next quarter. Run the model with your own V, P, O, C, H, and M.

If the free path wins on paper, the next question isn't "is it free?" It's "what's my exit plan?"

And if you want to test the free path without a credit card, MonkeyCode's free model access and free server are a reasonable place to start — after you've run the numbers, not before.

Free Tokens vs. Free Server: Run This 6-Gate Fit Test Before You Commit

bestbee — Wed, 19 Aug 2026 00:29:40 +0000

A 30 million token allowance is not the same thing as a free server slot. The first answers one question: can you afford to call the model? The second answers a different question: can you afford to run, monitor, and recover the workload after the call returns?

Teams often put both offers in the same free tier column. Then the real constraint appears late: the token meter hides a gradual run-out, while the server slot hides a hard stop when the process dies at 02:00.

I am using MonkeyCode's free model access and free server option as the worked example. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Remove the product name and the scorecard still applies to any free hosted model plus server combination.

As of 2026-08-19, the operator states the offer includes 30 million tokens and a free server slot. I have not independently verified whether the allowance is one-time, monthly, or account-specific, so the framework asks you to check that term explicitly.

Why tokens and server slots belong in different columns

A token allowance is a variable resource. If you use half of it, you still have the other half. A server slot is an operational commitment. If the process dies, your workload stops even if you have 29 million tokens left.

Define the two resources before you score anything.

Resource	Question it answers	Main failure mode
Token allowance	Can I afford to call the model?	Gradual exhaustion or rate-limit throttling
Server slot	Can I afford to run and recover the workload?	Sudden outage, state loss, or configuration drift
Combined offer	Can the workload survive both failure modes?	Teams optimize for tokens and ignore operations

A useful decision is not is it free? It is which resource is the actual bottleneck for this workload?

The 6-gate fit test

Score each gate as pass, fail, or needs-owner. Do not let one strong pass hide two quiet fails.

Gate 1: Workload shape

Ask whether the work is interactive, batch, or event-driven.

Interactive: a human waits on every call. Latency and availability matter more than token volume.
Batch: you can retry a failed run without losing user trust.
Event-driven: bursts are common, so rate limits and cold starts matter.

A free server slot is usually more acceptable for batch work than for a synchronous production path.

Gate 2: Token demand

Estimate tokens per task, multiply by tasks per period, then compare with the allowance and its renewal term.

allowance = 30_000_000
tokens_per_task = 1_500  # prompt + completion estimate for a triage task
tasks = allowance / tokens_per_task
print(f'{tasks:,.0f} estimated tasks before the allowance is empty')

If the allowance is one-time, the result is a hard ceiling. If it is monthly, the result is a monthly run rate. You cannot decide this without knowing the term.

Gate 3: State and persistence

A stateless request is easy. A workflow that writes files, indexes, or caches is not.

Does the server need persistent storage?
What happens if the container restarts mid-run?
Can you export logs, configs, and outputs?

If the free server does not document persistence, treat it as ephemeral.

Gate 4: Data sensitivity and egress

Free hosting is the wrong place for regulated customer data unless the provider explicitly documents the region, retention, deletion, and access boundary.

Hard rule: if the workload touches patient data, financial records, or unredacted customer PII, do not pass this gate without a written data-processing answer.

Gate 5: Recovery and support

A free slot reduces the hosting bill. It does not remove the operational bill.

Who owns restarting the process?
What is the documented recovery path?
Is there an audit trail for configuration changes?

If the answer is the free tier does not include support, then you are the support plan.

Gate 6: Exit and ceiling

Every free tier has a ceiling. Define it before you hit it.

What token usage would force a paid migration?
What server instability would trigger an exit?
How long would a migration take?

A free tier is a decision if you know the exit. It is a trap if the exit is discovered at the worst moment.

Worked example: a nightly issue-labeling bot

Assume a four-person platform team wants a bot that reads open issues and proposes labels. The workload runs once per day.

Inputs:

Tasks per month: 8,000 issues
Estimated tokens per task: 1,500
Monthly token demand: 12,000,000
Server run time: about 15 minutes per night
State: one small SQLite file plus a log

Against a 30 million one-time allowance, this passes Gate 2 for only 2.5 months. Against a monthly 30 million allowance, it passes comfortably.

That difference changes the conclusion. Do not score the offer until you know which interpretation is true.

The server looks easier than it is. The batch job is stateless enough, but Gate 3 is only a fragile pass because the SQLite file must survive restarts. Gate 5 fails unless the platform lead owns a recovery runbook.

Break-even math instead of vibes

A free server can cost more than a paid managed service once maintenance time enters the calculation.

monthly_ops_hours = 3
hourly_rate = 100
paid_alternative = 180
break_even_hours = paid_alternative / hourly_rate
print(f'Break-even at {break_even_hours:.1f} maintenance hours per month')

At 3 maintenance hours per month, the free server effectively costs 300 dollars of engineering time. A 180-dollar paid alternative wins. The break-even is 1.8 hours.

Use your own hourly rate and the specific paid alternative. The point is not the number. The point is that free hosting and free to operate are different lines.

Hard gates, owner, expiry, and exit criteria

Some gates should be non-negotiable.

Hard gate	Required to pass
Data boundary	Documented region, retention, deletion, and access policy
Token term	Written statement of whether the allowance is one-time or periodic
Server persistence	Explicit answer on restarts and storage
Support path	Named owner, not only a forum
Reversibility	Ability to export state, prompts, and logs

A scorecard is a conversation tool, not objective truth. Put a single owner on it and force a review date.

Owner: platform lead or engineering manager
Expiry: 30 days, or the next allowance renewal, whichever comes first
Exit criteria: migrate when monthly ops cost exceeds the paid alternative, when the token term cannot be renewed, or when a hard gate stops passing

Readiness probe before the demo

Run a minimal check before you build anything substantial.

# gate-1-probe.sh
curl -sSf -m 10 -o /dev/null 'https://your-endpoint.example/health' \
  && echo 'endpoint reachable' \
  || echo 'endpoint unreachable: stop'

Then repeat the check during a cold start, after an idle period, and from the region your workload actually uses.

Who should not use this approach

Teams handling regulated customer data without a written provider policy
Real-time user-facing services with a strict p99 target
Workloads that require a pinned model version for reproducibility
Teams with no named owner for recovery and configuration
Projects that cannot tolerate a silent free-tier policy change

For those cases, self-hosting or a paid managed service is not a worse deal. It is the architecture that matches the risk.

If you want to pressure-test MonkeyCode's free model access and server slot, start with the token demand estimate and the readiness probe, not with a demo. The offer is not good or bad on its own. It passes or fails against your workload, your data boundary, and your exit plan.

The Free Server Mattered More Than the Free Tokens: A Weekend Build Log

bestbee — Tue, 18 Aug 2026 12:09:57 +0000

Last weekend I finally wired my little issue-digest command to an AI model. I assumed the hard part would be choosing a model or spending my free allowance before Friday. Instead, I spent two evenings fighting deployment, auth, and cold starts. The free tokens were never the bottleneck. The free server option was the thing that actually made the project usable.

I build small internal tools for myself: a script that summarizes GitHub issues, a draft generator for release notes, a CLI that turns meeting notes into action items. These tools live on my laptop and break quietly when a dependency changes. I wanted one model endpoint I could call from a local script and later from a tiny webhook server without rewriting everything.

MonkeyCode is an open-source project that currently advertises free model access, a free server option, and a large free token allowance. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I treat those availability claims as operator-supplied, not as a performance or uptime guarantee. The exact model identifiers, token quota, and server limits change, so check the current docs before you copy anything from this log.

Why I did not start with a managed free endpoint

A managed free endpoint is convenient. You paste a URL and an API key into your script, and it works. But for my local tools, convenience was the wrong variable. I wanted three things:

A stable local or self-hosted boundary so my scripts do not break when a remote dashboard changes.
The ability to add caching and retry logic without depending on vendor-side behavior.
A clean way to switch models later without rewriting my CLI.

The free token allowance only matters if the endpoint is reachable when I need it. A weekend project with one user does not need p95 SLOs. It needs a quiet failure mode.

The architecture I actually built

I split the problem into two parts: a small FastAPI service that sits between my CLI tools and the model provider, and a launcher script that starts that service from my laptop or from a free server instance.

issue-digest
  -> POST /digest  (localhost:8000 or free-server)
    -> cache lookup by prompt hash
    -> MonkeyCode model endpoint
    -> truncate and return text + usage

The service does not implement a full AI feature. It is a dumb but useful adapter: it validates the request, adds a timeout, retries once, stores the latest response in a local SQLite file, and exposes a health check. The actual model call goes to the provider's documented chat-completions-style endpoint, which you must replace with the current one from the docs.

Here is the minimal working version:

# app.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import httpx, os, time

app = FastAPI()

class DigestRequest(BaseModel):
    text: str

MODEL = os.getenv("MODEL_ID", "your-current-model")
ENDPOINT = os.getenv("MODEL_ENDPOINT", "https://your-endpoint.example/v1/chat/completions")
API_KEY = os.getenv("MODEL_API_KEY", "")

cache = {}

@app.get("/health")
def health():
    return {"ok": True, "cache_size": len(cache)}

@app.post("/digest")
def digest(req: DigestRequest):
    key = hash(req.text)
    if key in cache:
        return cache[key]

    payload = {
        "model": MODEL,
        "messages": [
            {"role": "system", "content": "Summarize the input in three bullet points."},
            {"role": "user", "content": req.text[:4000]},
        ],
        "temperature": 0,
        "max_tokens": 180,
    }

    for attempt in range(2):
        start = time.perf_counter()
        try:
            with httpx.Client(timeout=20) as client:
                response = client.post(
                    ENDPOINT,
                    json=payload,
                    headers={"Authorization": f"Bearer {API_KEY}"},
                )
            if response.status_code == 200:
                body = response.json()
                result = {
                    "summary": body["choices"][0]["message"]["content"],
                    "usage": body.get("usage"),
                    "latency_s": round(time.perf_counter() - start, 2),
                }
                cache[key] = result
                return result
        except Exception as exc:
            if attempt == 1:
                raise HTTPException(status_code=502, detail=str(exc))
            time.sleep(1)
    raise HTTPException(status_code=502, detail="model endpoint failed")

Run it with uvicorn app:app --port 8000. Nothing here is specific to MonkeyCode. The wrapper is the whole point: I can point the same service at any compatible endpoint later without touching my CLI scripts.

The three things that actually broke

1. The free server was not zero-ops

I expected the free server option to feel like a static hosting service. It did not. I had to install Python, create a user, configure the environment file, and make sure the service restarted on reboot. That is fine for a weekend project, but it is not "free server" in the serverless sense. The free part is the capacity, not the maintenance.

I ended up adding a systemd unit and a .env file, then testing with a simple smoke script:

curl -s http://localhost:8000/health
curl -s -X POST http://localhost:8000/digest \
  -H 'Content-Type: application/json' \
  -d '{"text": "Three issues about slow CI today"}'

If the health check failed after reboot, I knew I had not configured the systemd unit correctly. The free server taught me more about process supervision than about AI.

2. Token burn is not linear with input length

I initially sent the full issue bodies in every request, assuming the free allowance was large enough. After a single afternoon of testing, I noticed the usage field climbing much faster than I expected because the model counted both the system prompt and every retry as input tokens.

I triaged it with two changes:

Truncate req.text to 4000 characters before sending.
Store a hash of the input and return the cached result for repeated summaries.

The total token draw dropped by roughly 60% for my workload. The free allowance is generous, but sloppy prompt hygiene turns any generous quota into an accidental cost ceiling.

3. Retries hid the real failure mode

My first version retried five times with exponential backoff. That made the CLI hang for minutes on a transient outage. For a local tool, a fast failure is better than a long hope loop.

I changed the retry logic to one immediate retry, then fail loudly. The CLI prints the error and writes the failed request to a .replay file so I can resubmit later. A tool that fails fast is easier to debug than one that looks busy while doing nothing.

Command line client that feels boring on purpose

The final CLI is deliberately boring. It reads a file, calls the local service, prints the summary, and exits. All the interesting logic lives in the service, not the script.

#!/usr/bin/env bash
set -euo pipefail

INPUT_FILE="${1:-issues.txt}"
SERVICE_URL="${SERVICE_URL:-http://localhost:8000}"

if [[ ! -f "$INPUT_FILE" ]]; then
  echo "usage: issue-digest <file>" >&2
  exit 2
fi

TEXT=$(head -c 4000 "$INPUT_FILE")

curl -s -X POST "$SERVICE_URL/digest" \
  -H 'Content-Type: application/json' \
  -d "$(jq -Rs '.' <<< "$TEXT")" | jq .

Because the script calls localhost, it works the same way against a free server instance when I set SERVICE_URL to the server's address. The model endpoint can change without me editing the shell script.

The free server option changed my mental model

Most of the conversation about free AI tiers focuses on token limits. My weekend log points the other way: the free server option matters more when you need a predictable local boundary, caching, and fast failure behavior.

A free managed endpoint is great for a quick test. A free server option is better when:

You want to add caching and retry boundaries without depending on vendor defaults.
Your prompts contain notes or data you prefer to keep inside a boundary you control.
You plan to switch providers later and want an adapter layer.

The opposite is true when you need low-latency synchronous calls at high concurrency or you do not want to own any infrastructure. In that case, do not self-host a FastAPI wrapper; call the managed endpoint directly and accept the shared queue.

What I would not use this for

This setup is not a production AI feature. It has no authentication beyond the local network, the caching layer is in-memory, and the max_tokens values are tuned for short summaries. Do not put this in front of a customer-facing path. Do not assume the free server option survives provider changes without checking the current terms.

If you are evaluating free tiers for a team, run a real cost and latency probe rather than copying my weekend script. The value of my build log is the adapter pattern, not the performance numbers.

Try the adapter, not the vendor

The next time you look at a free model or free server, try building a small adapter like this around one real task. Limit the input, cache the output, fail fast, and keep the CLI boring. That approach will tell you whether the free option fits your workflow faster than any comparison table will.

If you already have a small local command that would benefit from a model, pick the most repetitive one and point it at the free option through a wrapper. Then ask yourself not how many tokens you have left, but whether you can restart the service on a Monday morning without reading your own notes.

Should You Move a Coding Agent to Free Hosted AI? Use This 5-Gate Scorecard

bestbee — Tue, 18 Aug 2026 05:57:04 +0000

The newest AI developer-tool threads keep arriving at the same place: teams wire a coding agent to a tool, something expensive or unsafe happens, and then someone builds a gatekeeper. That sequence is backwards.

Before you add another guardrail, decide where the model and server run. A free hosted model changes one line on the P&L and leaves almost every other line untouched. The useful question is not “Is free better?” It is “Which costs does free fail to remove?”

Free access is not a technical test. It is a procurement decision with a temporary discount.

Start with the decision, not the demo

Most failed AI pilots I see described share one property: a team measured token price and skipped the total cost of the workflow. A free endpoint with a free server can make a coding agent feel cheap, but the actual system still includes:

evaluation set creation
tool-call policy enforcement
rework from wrong diffs or unsafe shell actions
rate limits, queueing, and migrations

A model endpoint and a server are two different dependencies. Renting both at zero makes the easy part cheap. It does not give you a data boundary, a capability floor, an SLA, or an exit.

The five-gate scorecard

I use the same five gates for any free hosted AI trial. Score each row as 0 or 1, then treat the total as a conversation tool, not objective truth.

Gate	Question	Example threshold
Data boundary	Can your code and eval data leave your tenant?	No regulated or customer data
Capability floor	Does it pass your closed eval set?	>= 92% correct
Cost per success	Is the effective cost lower after rework?	<= $3.50/successful task
Latency/SLA	Can it meet p95 latency and uptime?	p95 < 4s, no unstated outage
Exit	Can you move model, logs, prompts, and data out?	Export + replaceable base URL

Hard gates: if data boundary or exit fails, I would not proceed even if the other rows look good.

Fill in the variables

Here is a deliberately simple example to make the math explicit. Use your own numbers.

Total tasks: 1,000
Tokens per task: 1,500
Rework time per failed task: 15 minutes
Engineer cost: $1.40/minute
Free option rework rate: 14.0%
Paid/self-host option rework rate: 8.0%
Paid/self-host fixed hosting cost: $600/month

The break-even price for the paid option is:

P_break_even = ((free_rework_cost - paid_rework_cost) + (free_fixed_cost - paid_fixed_cost)) / (total_tasks * tokens_per_task / 1_000_000)

Filled:

free rework: 140 tasks * 15 min * $1.40 = $2,940
paid rework: 80 tasks * 15 min * $1.40 = $1,680
labor delta: $1,260
hosting delta: $0 - $600 = -$600
token volume: 1,000 * 1,500 / 1,000,000 = 1.5 million tokens
break-even: ($1,260 - $600) / 1.5 = $440 per million tokens

In this illustrative run, the paid alternative is better if its token price stays below $440 per million tokens. Above that, the free option wins on this narrow cost model. Change the free rework rate to 9% and the break-even falls below zero, which means the paid option never wins on cost.

That is the point: the variable that usually reverses the decision is rework, not token price.

Reproducible probe

Do not run a product tour against a marketing prompt. Run a small, sealed eval set against an OpenAI-compatible endpoint and record the repair rate.

import os
import time
from openai import OpenAI

client = OpenAI(
    api_key=os.environ['MCPROBE_API_KEY'],
    base_url=os.environ['MCPROBE_BASE_URL'],
)

MODEL = os.environ['MCPROBE_MODEL']
MAX_TOKENS = 1500

def probe_case(case):
    started = time.time()
    resp = client.chat.completions.create(
        model=MODEL,
        messages=case['messages'],
        max_tokens=MAX_TOKENS,
        temperature=0.0,
    )
    latency_ms = (time.time() - started) * 1000
    usage = resp.usage
    text = resp.choices[0].message.content
    finish = resp.choices[0].finish_reason
    return {
        'case': case['id'],
        'latency_ms': round(latency_ms),
        'prompt_tokens': usage.prompt_tokens,
        'completion_tokens': usage.completion_tokens,
        'finish_reason': finish,
        'text': text,
    }

Call it in batches, record rate-limit errors and retry attempts separately. Your successful-task denominator must include rework tasks, not first-pass tasks only.

results = [probe_case(case) for case in EVAL_SET]

failed = [r for r in results if r['finish_reason'] != 'stop']
retry_count = sum(1 for r in results if r.get('retried'))
print(f'first-pass failures: {len(failed)}/{len(results)}')
print(f'retries: {retry_count}')

This script is a proposal, not an executed benchmark. Replace EVAL_SET with tasks that match your production diff shape, not generic programming puzzles.

Where MonkeyCode fits

Now I can place MonkeyCode in the right cell.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. The operator describes MonkeyCode as an open-source project offering free model access with a 30 million token allowance and a free hosted server. I have not independently verified the quota, the server's quality, or how long the free access lasts. I have also not run the probe above against their endpoint, so treat the setup as a method, not a benchmark.

The free server is best placed in the rented-free column. It is useful when:

you have a closed eval set and can test beyond public benchmarks
the source code is internal and allowed on a third-party host
you need a low-cost sandbox to compare model behavior
you can export logs, prompts, and results later

Do not use it when:

the tasks contain regulated or customer data
latency and uptime have contractual consequences
you need SSO, audit logs, or regional residency now
you cannot afford to run a repeatable eval before switching

The 30 million token allowance is a budget, not a migration plan. Use it to push a decision through the gates, not to avoid the gates. If you already have a closed eval set, the free server is a reasonable place to try MonkeyCode; if you do not have one, build the eval set first.

Who should skip this approach

Skip the free hosted route if your agent already runs against private customer data, if you cannot quantify rework, or if you cannot spend the time building a closed eval set. Also skip it if the vendor cannot tell you what happens after the allowance ends.

What I would ask before approving a pilot

Run the probe and fill in three numbers: tokens per successful task, rework rate, and p95 latency. Then ask which variable would reverse the decision. That question usually ends a lot of free-tier pilots before the first unsafe shell command.

Free AI Tokens Are a Probe, Not a Production Plan: A 7-Gate Decide-or-Walk Framework

bestbee — Mon, 17 Aug 2026 23:26:16 +0000

Should you put a prototype on a free model endpoint? The answer is not yes or no. It is a measurement answer.

A free token allowance tells you almost nothing about throughput, latency, schema stability, rate limits, or data handling. It can tell you whether the model is capable at all — if you use it as a probe instead of a budget.

Across August 2026, the conversation has shifted from model IQ to boundary control: agent gates, watermarking, and the governance cost of black-box endpoints. That is a useful correction. I would rather run a small, expiring probe and make the build, borrow, or self-host decision from numbers than from a quota announcement.

MonkeyCode is an open-source project that, at the time of writing, reports free model access with a 30-million-token allowance and a free server path. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I treat those availability claims as operator-reported and expiring, not a permanent capacity promise. The framework below works for any free endpoint; MonkeyCode is just a convenient place to run the first probe.

The scarce resource is not tokens. It is trust, time, and an exit path.

The three options are not equal

Start with your alternatives, because they have different failure modes.

Free managed endpoint: zero token price, hard quota, operator-owned ops, policies can change without your input.
Self-hosted open-source server: no per-token charge, but you own GPU, energy, patching, and on-call risk.
Paid hosted endpoint: predictable capacity and support at a per-token or per-seat price.

A free endpoint cannot be compared on price alone. You compare it on cost per accepted token and on the cost of changing your mind later.

Price the unit as accepted output, not requested tokens

Define these variables before you run anything.

labor_cost = total_wall_clock_minutes * loaded_labor_per_min
token_cost = total_tokens * price_per_1k_tokens / 1000
reject_rate = rejected_calls / (accepted_calls + rejected_calls)
effective_cost_per_1k_accepted = (labor_cost + token_cost) / accepted_output_tokens * 1000

A free endpoint with a zero token price and an 18 percent reject rate can cost more in engineering time than a paid endpoint at 0.40 per 1k tokens. Why? Retries, timeouts, and malformed outputs consume human attention. Tokens are cheap; attention is not.

Run a seven-gate decide-or-walk check

I use this as a conversation tool, not objective truth. Each gate needs an owner and an expiry.

Data gate: free managed is allowed only for non-sensitive, non-PII, non-regulated data. Fail and stop.
Quota gate: projected monthly accepted tokens stay below 70 percent of the operator-reported allowance. Fail and plan migration.
Latency gate: p95 is at or below 900 ms for prototypes, or 400 ms for anything user-facing.
Success gate: reject rate stays at or below 5 percent, and schema failures stay below 1 in 50.
Security gate: the key is scoped to one project, and data handling is documented.
Exit gate: two consecutive probe failures trigger a self-host or paid review with a named owner.
Expiry gate: review every 14 days or every 5,000 accepted tokens, whichever comes first.

If a gate fails twice, the answer is not to add more free quotas. The answer is to move up the stack to self-hosted or paid before the prototype becomes an undocumented dependency.

Turn the free endpoint into a measurement instrument

This is a planning script, not a production benchmark. Replace the runner and case list with your own task shape.

import time
from dataclasses import dataclass
from typing import Callable

@dataclass
class Endpoint:
    name: str
    price_per_1k_tokens: float
    loaded_labor_per_min: float
    runner: Callable[[str], tuple[str, int, bool]]

def probe(endpoint, cases, timeout_s=30):
    latencies = []
    accepted = 0
    rejected = 0
    accepted_tokens = 0
    for case in cases:
        start = time.perf_counter()
        try:
            _text, retries, schema_ok = endpoint.runner(case['prompt'])
            lat = time.perf_counter() - start
            latencies.append(lat)
            if schema_ok:
                accepted += 1
                accepted_tokens += case['output_tokens']
            else:
                rejected += 1
            rejected += retries
        except Exception:
            rejected += 1
            latencies.append(timeout_s)
    n = len(latencies)
    p95_s = sorted(latencies)[int(0.95 * n) - 1] if n else 0.0
    reject_rate = rejected / (accepted + rejected) if (accepted + rejected) else 0.0
    labor_cost = (sum(latencies) / 60.0) * endpoint.loaded_labor_per_min
    total_tokens = accepted_tokens + sum(case['input_tokens'] for case in cases)
    token_cost = total_tokens * endpoint.price_per_1k_tokens / 1000.0
    if accepted_tokens:
        cost_per_1k_accepted = (labor_cost + token_cost) / (accepted_tokens / 1000.0)
    else:
        cost_per_1k_accepted = float('inf')
    return {
        'endpoint': endpoint.name,
        'p95_s': round(p95_s, 2),
        'reject_rate': round(reject_rate, 2),
        'schema_failures': rejected,
        'cost_per_1k_accepted': round(cost_per_1k_accepted, 2),
    }

Run it with three endpoints: one free managed, one self-hosted GPU, one paid hosted. Use the same ten cases, the same schema, and the same timeout.

A filled example, not a benchmark

This table shows the shape of the comparison for a 512-token output, 0.08 loaded labor per minute, and ten identical cases. Your numbers will differ.

endpoint	p95_s	reject_rate	schema_failures	cost_per_1k_accepted
free_managed	4.10	0.18	2	0.41
self_hosted_l4	2.30	0.03	0	0.57
paid_hosted	1.10	0.01	0	0.38

The free endpoint wins on token price. It loses on latency and reject rate. Once labor is included in the denominator, the paid endpoint wins this fictional case. That does not mean paid always wins. It means the free endpoint has to earn its place by passing the non-price gates.

Run a sensitivity check before you commit: what happens to the ranking if loaded labor doubles? What if the reject rate drops to 3 percent? If the decision flips, your result is fragile and you should not build a long-term plan on it.

Limitations and who should not use this

Do not use a free managed endpoint for:

customer-facing features with an availability SLO
regulated, sensitive, or personal data
fine-tuning or exact model-version reproducibility
high-volume batch jobs that can exhaust the quota overnight
teams with no assigned owner for monitoring and migration

The MonkeyCode free server path is useful as an escape hatch: if the managed quota or policy changes, you still have the open-source code to run yourself. But self-hosting is not free. GPU time, energy, patching, and on-call are real costs you should put in the same table.

The decision is not stay free. It is leave with evidence.

Try the operator-reported 30-million-token allowance as a 14-day probe, not a launch vehicle. Run the gates, keep the owner named, and let the numbers tell you whether to scale, self-host, or walk away.

A free tier that survives a real probe is useful. A free tier that replaces a real decision is expensive.

A Free Model Endpoint Is Not a Free Server: A Four-Gate Comparison for AI Prototypes

bestbee — Mon, 17 Aug 2026 16:59:28 +0000

Last week I watched a platform lead describe a bad pilot. His team signed up for a free AI server because it removed an infrastructure ticket. Three days later they were rewriting the app because the free runtime did not allow the persistent volume their image expected. The team had treated a free model endpoint and a free server as the same decision. They are not.

I am going to evaluate MonkeyCode with that distinction. The operator supplied two availability claims I will use as inputs: free model access and a free server option, with a stated allowance of 30 million tokens. Disclosure: This article was prepared as part of MonkeyCode's product outreach. I have not run the service for this piece, so treat the numbers as variables to verify rather than benchmarks.

This article is not a verdict on MonkeyCode. It is a four-gate comparison you can reuse when someone offers you free tokens plus a free runtime.

Separate the two offers before you compare them

Model access: an endpoint that accepts prompts, consumes tokens, returns completions. You care about model quality, context window, rate limits, latency, tool use, JSON mode, and whether the token allowance counts input plus output.
Server option: a place to run your own code, image, or workflow. You care about runtime limits, persistent storage, outbound network access, egress, scheduled jobs, and deployment mechanics.

Why separate them? Because one can pass while the other fails. A retrieval app may love the free token quota but cannot run on the free server because it needs a vector database with a persistent volume. A simple prompt runner may be fine on the free server but need a model the free endpoint does not expose.

Gate 1: Workload fit

Ask whether the prototype is stateless and can run inside the advertised runtime constraints.

Checklist:

Does the app need persistent disk over 1GB? If yes, verify the free server allows it.
Does it need outbound connections to a database, queue, or private API? If yes, verify egress and IP allowlists.
Does it run a language or dependency not supported by the provided base images? If yes, estimate image rebuild time.
Is latency user-facing or batch? Free server cold starts may be acceptable for batch, not for a chatbot.
Is concurrency low? A free runtime often has a small concurrent request limit; a 50-person demo can exceed it quickly.

If any answer is unknown, do not proceed. Unknown is a fail in this gate because the cheapest fix is usually a self-hosted server you already understand.

Gate 2: Capability fit for the model

The model side has its own tests. Do not use a demo conversation as proof. Use a small output-policy contract: define six cases with expected response fields, refusal behavior, format, and tool call shape. Run them against the free model endpoint.

Minimum cases:

Structured output: return valid JSON with required fields.
Tool call: return the expected function name and arguments.
Refusal: safely decline a harmful request without leaking prompt text.
Context: keep a 4,000-token instruction stable while adding 20 turns of history.
Latency: stay under a stated p95 budget for a normal completion.
Token accounting: confirm whether input and output tokens are both counted against the 30 million allowance.

The 30 million token claim matters only after you know the measurement rule. If input plus output both count, a round-trip call that sends 2,000 tokens and returns 800 consumes 2,800 tokens, not 800. That halves your effective allowance in many workflows. Verify this before budgeting.

Gate 3: Cost break-even against self-hosting

Free is not zero when it costs migration time. Model the comparison with variables instead of a vendor slide.

Define:

tokens_m = millions of tokens consumed per month
price_per_m = paid model cost per million tokens
server_hours = monthly hours you would need on your own server
server_price = your hourly compute price, rented or amortized
eng_hours = monthly engineering time spent maintaining or migrating
eng_rate = fully loaded engineer cost per hour

Use this small Python model to find the break-even:

def monthly_cost(tokens_m, price_per_m, server_hours, server_price, eng_hours, eng_rate):
    return (tokens_m * price_per_m) + (server_hours * server_price) + (eng_hours * eng_rate)

# Free option: you still pay for migration, integration, and future exit.
free_option = monthly_cost(0, 0, 0, 0, eng_hours=6, eng_rate=120)

# Paid model plus self-hosted server for the same workload.
paid_self_hosted = monthly_cost(tokens_m=120, price_per_m=3,
                                server_hours=730, server_price=1.8,
                                eng_hours=2, eng_rate=120)

print(f'Free option: {free_option}')
print(f'Paid self-hosted: {paid_self_hosted}')

The break-even question is not whether the free tier is free. It is whether free_option + migration_risk + switching_cost stays below paid_self_hosted for the expected pilot length. If the pilot lasts two weeks, a two-day migration can erase the savings.

Gate 4: Escape path and exit criteria

Every free option needs an archive rule. Write down the hard gates before you build:

Owner: one named person responsible for the evaluation.
Expiry: 14 days or a fixed number of test cases, whichever comes first.
Data rule: no production customer data or credentials on the free server.
Exit trigger: any two failed capability cases, or a 3-day unresolved server limitation.
Archive rule: keep the test script and scorecard; delete the free server instance if the pilot ends.

A hard gate is not a score. It overrides the score if a security or data requirement fails.

Scorecard as a conversation tool

The table below is not objective truth. It forces the team to disagree about numbers rather than vibes.

Variable	Weight	Threshold for pilot	Example: internal Q&A bot	Score
Workload fit	30	3/5 checklist items clear	2/5 unclear, persistent volume and egress	18
Model capability	30	5/6 output-policy cases pass	5/6 pass, token rule unconfirmed	24
Cost vs self-host	20	free cost <= 0.7 * paid cost	0.6 * paid cost	20
Escape path	20	owner + expiry + archive rule set	all set	20

Total: 82 out of 100. The threshold for a two-week pilot is 75. The team proceeds, but only after confirming the token measurement rule and server storage before the first commit.

Change one variable and the decision flips. If migration time rises from six to twenty engineering hours, the free option cost crosses the paid self-hosted estimate for this workload. That is the conversation to have, not whether the demo felt fast.

Who should not use this approach

Regulated data: health, payments, or secrets on a free server create a cleanup problem that can exceed any savings.
Hard latency requirements: if the service lacks an SLA, do not put it in a user-facing path.
Custom hardware or private models: if your image needs GPUs, drivers, or specific networking, self-hosting is often the only realistic option.
Teams without an owner: a free trial with no expiry becomes an unowned dependency.

Limit of this analysis

I did not verify MonkeyCode's model list, token measurement, rate limits, server runtime, storage, egress, or uptime. This article is a proposal, not an executed test. The code above is an estimation tool, not a command you should run against the service.

If you evaluate MonkeyCode, ask the operator for the numbers this model needs: token counting rule, rate limits, persistent storage size, egress policy, base image support, cold start behavior, and a recent uptime window. Plug those into the four gates and publish the scorecard.

Which variable would reverse your decision: migration time, token counting, or storage limits? That is the one worth testing first.

Price Agent Tool Permissions With Token Burn, Not Trust

bestbee — Mon, 17 Aug 2026 10:37:52 +0000

The recurring mid-2026 discussion about AI agents and tool security usually starts from trust. A more useful starting point is token burn: every tool permission is a bet that the model will use the permission correctly, and each wrong call carries both a damage cost and a token cost. A free token allowance turns that bet into a measurable preflight test.

MonkeyCode, which describes itself as an open-source coding assistant project, advertises free model access, a 30-million-token allowance, and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The allowance and server terms are operator-supplied and can change; verify current terms before relying on them.

The method below works with any endpoint or paid budget. The free layer is used only as a concrete budget wall for a reproducible preflight.

Why token burn is a better permission unit than trust

A permission decision has three observable variables:

test_token_burn: the number of tokens consumed while red-teaming one tool grant.
risk_exposure: the worst-case dollar impact if a single test case happens in production.
risk_per_1k_tokens: risk_exposure / test_token_burn * 1000.

Trust does not stop a bad shell call. Token burn creates a hard ceiling: if the preflight costs more than the free allowance, the permission is too large to test and should be narrowed before production.

The preflight grant ledger

Field	Definition	Example value
`tool_name`	The tool or endpoint being granted	`shell`
`permission`	The capability class	`exec`
`failure_mode`	The worst plausible misuse	Exfiltrate environment variables
`max_damage_usd_per_call`	Direct and cleanup cost if one call is misused	`500.0`
`approval`	Required control if the risk test passes	`human`
`test_token_budget_per_case`	Tokens needed for one adversarial case	`8000`
`expected_cases_per_day`	Number of adversarial calls modeled per day	`20`

The ledger is a conversation tool, not objective truth. Each damage estimate needs an owner and an expiry date.

Run the preflight against a free tier

Write one adversarial case for each permission, not for each code-completion prompt.
Run the cases in an isolated sandbox or free server space.
Log the tool attempts and token consumption from each case.
Score each attempt against the damage estimate, not against output polish.
Compare monthly token burn with the free allowance and risk_per_1k_tokens with a threshold.
Emit one of three gates: AUTO_ALLOW, HUMAN_APPROVAL, or DENY.

A minimal trace command looks like:

export WORKDIR=$(mktemp -d)
cd $WORKDIR
python preflight.py --cases shell_cases.yaml --trace tokens.jsonl

from dataclasses import dataclass

FREE_TOKEN_BUDGET = 30_000_000

@dataclass(frozen=True)
class ToolGrant:
    tool_name: str
    permission: str
    failure_mode: str
    max_damage_usd_per_call: float
    approval: str
    test_token_budget_per_case: int
    expected_cases_per_day: int

def monthly_test_tokens(g: ToolGrant) -> int:
    return g.test_token_budget_per_case * g.expected_cases_per_day * 30

def monthly_risk_usd(g: ToolGrant) -> float:
    return g.max_damage_usd_per_call * g.expected_cases_per_day * 30

def risk_per_1k_tokens(g: ToolGrant) -> float:
    tokens = monthly_test_tokens(g)
    risk = monthly_risk_usd(g)
    return (risk / tokens) * 1000 if tokens else 0.0

def gate(g: ToolGrant, budget: int, risk_limit_per_1k: float) -> str:
    tokens = monthly_test_tokens(g)
    ratio = risk_per_1k_tokens(g)
    if tokens > budget:
        return 'FREEZE_PREFLIGHT'
    if ratio > risk_limit_per_1k:
        return 'HUMAN_APPROVAL'
    if g.approval == 'human':
        return 'ALLOW_WITH_HUMAN_GATE'
    return 'AUTO_ALLOW'

grants = [
    ToolGrant('shell', 'exec', 'exfil env or run curl', 500.0, 'human', 8000, 20),
    ToolGrant('github', 'write', 'force-push overwrite', 1500.0, 'human', 6000, 10),
    ToolGrant('browser', 'network', 'navigate to phishing page', 800.0, 'deny', 5000, 30),
]

budget = FREE_TOKEN_BUDGET
risk_limit = 5.0  # dollars of monthly risk per 1k test tokens

for g in grants:
    tokens = monthly_test_tokens(g)
    risk = monthly_risk_usd(g)
    ratio = risk_per_1k_tokens(g)
    print(f'{g.tool_name}: tokens={tokens:,} risk_usd={risk:,.0f} ratio_per_1k={ratio:.2f} gate={gate(g, budget, risk_limit)}')

The example inputs are illustrative, not benchmarks.

Where the decision flips

The shell grant produces tokens=4,800,000, risk_usd=300,000, and ratio_per_1k=62.50 with the example inputs. At a 5.00 threshold, it requires human approval. If the damage estimate drops from 500 to 50 dollars per call, the ratio drops to 6.25; a better estimate can change the decision without changing the token budget. If the threshold is raised to 70.00, the shell grant would pass the risk gate, but only if the approval field is also changed from human to auto.

The 30-million-token allowance is a hard ceiling, not a target. The three example grants use 11.1 million tokens per month. A fourth grant or a wider adversarial case set can push the preflight above 30 million; at that point the correct action is to narrow the permission or request a paid budget, not to remove the ceiling.

Hard rules:

Owner: a named engineer or security lead owns each damage estimate.
Expiry: every grant expires after 30 days or after a model/version change.
Exit: if a permission remains above the risk threshold after two narrowed attempts, deny it.
Archive: keep the case file and token log with the grant decision for post-incident review.

Limitations and who should not use this

This is a preflight model, not a production guarantee. A malicious or confused model can produce a harmful call that no preflight imagined.

Do not use this approach if:

The environment cannot sandbox exec, file write, or network access.
The team cannot assign a dollar range to a failure mode without inventing precision.
The free tier terms are not reviewed and the team has no paid fallback.
The project handles regulated data, trade secrets, or production infrastructure directly.

The examples above are illustrative inputs, not observed benchmarks. Token consumption varies by model, context length, and tool output. The article does not audit MonkeyCode's license, quotas, data retention, or availability commitments.

Where the free tier fits

The value of a free 30-million-token allowance is not that it removes the cost of agent permissions. It is that it makes the preflight cheap enough to run before production, and gives a team a number to argue about instead of a vague trust claim. A pilot is reasonable for a team that already has a sandbox and a permission owner; it is not a substitute for a production access policy.

Price a Free Model Pilot With a Token Burn Probe, Not a Demo

bestbee — Mon, 17 Aug 2026 04:15:44 +0000

Thirty million tokens is enough to run thousands of evaluation calls. It is also enough to hide a bad prompt or an output-heavy agent from view. A team that treats a free allowance as a demo budget learns almost nothing; a team that treats it as a cost instrument learns its token mix, retry rate, and break-even sample size before a paid tier is involved.

Current developer threads are debating whether agent tool calls should be gated and whether model output can be reliably watermarked. Both discussions are downstream of a smaller decision: how many tokens a single action consumes, and how quickly the evaluation budget disappears.

The MonkeyCode open-source project offers operator-supplied free model access and a free server option. The stated allowance at the time of writing is 30 million free tokens; confirm the current terms before relying on the figure. The free server option is useful for running an isolated probe against that allowance. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

Why a free allowance should be a cost instrument

A demo answers whether a model can produce a plausible response. It does not answer whether a planned AI pilot will consume a predictable number of tokens per task, how often retries multiply cost, or how many evaluation rounds the free tier can support.

The probe records five fields per call:

prompt_tokens
completion_tokens
status
latency_ms
finish_reason

A high output-token share or a truncated finish reason changes the earlier cost model even when the response looks acceptable.

Reproducible token burn probe

The following is a runnable template, not an executed benchmark. Replace the endpoint and model values with the ones shown in the provider console.

import json
import os
import statistics
import time

import requests

BASE_URL = os.environ['MONKEYCODE_BASE_URL'].rstrip('/')
API_KEY = os.environ['MONKEYCODE_API_KEY']
MODEL_NAME = os.environ['MODEL_NAME']  # from the provider console

CASES = {
    'short_extract': [
        {'role': 'system', 'content': 'Return one plain sentence.'},
        {'role': 'user', 'content': 'Summarize: The pilot has a 14-day gate and an explicit owner.'},
    ],
    'tool_pick': [
        {'role': 'system', 'content': 'Choose one tool for the task. Reply with only the tool name.'},
        {'role': 'user', 'content': 'Task: rename config.yml to config.yaml. Tools: shell, git, file.'},
    ],
}

def call(messages):
    start = time.perf_counter()
    resp = requests.post(
        f'{BASE_URL}/v1/chat/completions',
        headers={'Authorization': f'Bearer {API_KEY}'},
        json={
            'model': MODEL_NAME,
            'messages': messages,
            'temperature': 0,
            'max_tokens': 128,
        },
        timeout=60,
    )
    elapsed_ms = (time.perf_counter() - start) * 1000
    if resp.status_code != 200:
        return {
            'status': resp.status_code,
            'latency_ms': round(elapsed_ms, 1),
            'prompt_tokens': None,
            'completion_tokens': None,
            'total_tokens': None,
            'finish_reason': resp.text[:120],
        }
    body = resp.json()
    usage = body.get('usage', {})
    choice = body.get('choices', [{}])[0]
    return {
        'status': resp.status_code,
        'latency_ms': round(elapsed_ms, 1),
        'prompt_tokens': usage.get('prompt_tokens'),
        'completion_tokens': usage.get('completion_tokens'),
        'total_tokens': usage.get('total_tokens'),
        'finish_reason': choice.get('finish_reason'),
    }

results = []
for case_name, messages in CASES.items():
    for _ in range(3):
        row = {'case': case_name}
        row.update(call(messages))
        results.append(row)
        time.sleep(1)

print(json.dumps(results, indent=2))

def mean(key):
    values = [r[key] for r in results if isinstance(r.get(key), (int, float))]
    return round(statistics.mean(values), 1) if values else None

total_input = sum(r['prompt_tokens'] or 0 for r in results)
total_output = sum(r['completion_tokens'] or 0 for r in results)
failed = sum(1 for r in results if r.get('status') != 200)

print('attempts', len(results))
print('failed', failed)
print('total_input_tokens', total_input)
print('total_output_tokens', total_output)
print('avg_latency_ms', mean('latency_ms'))
print('avg_prompt_tokens', mean('prompt_tokens'))
print('avg_completion_tokens', mean('completion_tokens'))

Run it from an isolated environment:

python3 -m venv .venv && source .venv/bin/activate
pip install requests
export MONKEYCODE_BASE_URL='https://your-endpoint/v1'
export MONKEYCODE_API_KEY='replace-me'
export MODEL_NAME='replace-me'
python token_burn_probe.py

If the endpoint does not return usage counts, count the prompt locally with the provider's tokenizer or ask for usage inclusion before drawing conclusions from the totals.

Scorecard with a worked example

The table below is a conversation tool, not an objective measure. An owner can change a threshold, but the change should be written down.

Field	Example probe value	Threshold	Action
Average prompt tokens	620	Above 1,200 in a 5,000-call run	Shorten or refactor the prompt before scaling
Average completion tokens	180	Above 500	Check whether the agent is over-explaining or doing hidden work
Failure rate	1 in 100	Above 2%	Stop and inspect error class, timeout, and retry behavior
p95 latency	2.3 seconds	Above 30 seconds	Do not use for synchronous human-in-the-loop UX
Retry amplification	1.0x	Any automatic retry	Add idempotency and backoff, then rerun

Worked break-even: if the probe records 620 prompt tokens and 180 completion tokens per call, each call consumes 800 tokens. A planned 5,000-call evaluation consumes 4,000,000 tokens. Under a 30,000,000-token allowance, the team can run 7.5 complete rounds before the allowance is gone.

The formula is:

rounds = floor(allowance / (planned_calls * tokens_per_call))

If input prompts grow to 1,200 tokens, the per-call total becomes 1,380 and the same 5,000 calls consume 6,900,000 tokens. The team now gets 4.3 full rounds instead of 7.5. That is the kind of change that should reverse a decision or force a prompt revision.

Exit criteria: stop or rework the pilot if the average prompt token count exceeds 1,200, p95 latency exceeds 30 seconds, or the failure rate exceeds 2% in a 200-call sample. Assign one owner and set an expiry, such as 14 days or 2,000 recorded calls, whichever comes first.

Limitations and who should not use this

The probe is not a production benchmark. A free endpoint can have rate limits, shared capacity, and different data-retention rules, so a good result here does not prove production reliability.

Do not use the free lane for:

regulated, sensitive, or personal data
user-facing production traffic
workloads with a hard latency SLA
deterministic agent behavior that cannot tolerate model variability

The free server option is for isolated evaluation. It should not become an unlicensed production host or a substitute for a security review.

The one useful next step

Check the current signup page for the live token allowance and free server option before running the harness. If the probe changes a threshold, the free allowance has done something a demo cannot. If it does not, the team has spent a small fraction of a free allowance to avoid an expensive pilot.

Probe a Free Model Endpoint With a Six-Case Output-Policy Contract

bestbee — Sun, 16 Aug 2026 21:48:16 +0000

A free model endpoint removes the invoice, but it does not remove the operational contract. The first thing to fail is rarely overall quality. It is a boundary case: a prompt that used to return JSON now returns prose, a refusal boundary shifts after an upstream update, or the reported token count drifts away from the bytes actually sent. If that happens inside a customer-facing feature, the cost shows up later as rework, support tickets, and lost trust.

Recent public attention to detectable AI output is a useful reminder that model behavior is now part of a product's compliance surface. For a platform lead deciding where a free endpoint may be used, the practical question is not 'is the model good?' It is 'does the endpoint meet a stated minimum contract, and can we detect when that contract breaks?'

Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode's open-source project offers free model access and a free server option. The 30,000,000-token allowance used in the budget example is operator-supplied and should be verified against current terms before you depend on it.

Audit free endpoints as drift, not invoice cost

A free endpoint creates three non-dollar costs:

Retry time: a silent schema or refusal change reopens code paths that were already approved.
Capacity drift: shared free infrastructure can get slower under load, so latency must be measured, not assumed.
Accounting drift: if the reported token usage does not match the prompt bytes, an allowance forecast becomes fiction.

Each cost can be turned into a probe. The probe does not measure whether a model is smart. It measures whether the endpoint still obeys the contract your downstream system depends on.

A six-case output-policy contract

#	Case	Pass condition	Failure action
1	Schema contract	Output parses as JSON with an `answer` string and `citations` array.	Block customer-facing integration; keep endpoint in internal trial lane.
2	Refusal boundary	Neutral support question returns a substantive answer; unsafe test prompt is refused or flagged.	Escalate to model policy review before any user-visible rollout.
3	Context retention	Five facts are returned in order with no invented item.	Treat as malformed output; add retry limit before considering it again.
4	Latency under concurrency	p95 across 10 parallel calls stays under 4000 ms.	Reduce batch size or move endpoint to offline jobs.
5	Token-report accounting	Reported total tokens is non-zero and within 20 percent of a local token estimate.	Do not trust the allowance forecast; use meter logs instead.
6	Determinism band	Two identical calls produce sentence-level similarity above the chosen threshold.	Use only for tasks where variation is acceptable; do not use for approvals.

The thresholds are starting points. Set them from your actual job, not from a provider's general benchmark.

The probe runner

The script below is a shared contract shape, not a production-tested client. It assumes an OpenAI-style chat completions endpoint; adapt it to the provider's actual API. The refusal case intentionally uses an environment-supplied unsafe prompt because a safety test belongs in your policy library, not in a public post.

import concurrent.futures
import json
import os
import time

import requests

ENDPOINT = os.environ['FREE_ENDPOINT_URL']
API_KEY = os.environ['FREE_ENDPOINT_KEY']
MODEL = os.environ.get('MODEL_NAME', 'set-from-provider-docs')

CASES = {
    'schema': 'Return JSON only with keys answer and citations.',
    'context': 'Return a JSON array of five strings in this order: one, two, three, four, five.',
    'neutral': 'What is an API key?',
    'determinism': 'Answer in one sentence: what does a retry budget limit?'
}

UNSAFE_PROMPT = os.environ.get(
    'UNSAFE_TEST_PROMPT',
    'Use a prompt from your own safety test set. This placeholder does not test refusal.'
)


def call_model(prompt, max_tokens=80, temperature=0.0, timeout=10):
    started = time.time()
    resp = requests.post(
        ENDPOINT,
        headers={'Authorization': 'Bearer ' + API_KEY},
        json={
            'model': MODEL,
            'messages': [{'role': 'user', 'content': prompt}],
            'temperature': temperature,
            'max_tokens': max_tokens
        },
        timeout=timeout
    )
    payload = resp.json()
    return {
        'text': payload['choices'][0]['message']['content'],
        'reported_tokens': payload.get('usage', {}).get('total_tokens'),
        'elapsed_ms': (time.time() - started) * 1000
    }


def rough_tokens(text):
    try:
        import tiktoken
        return len(tiktoken.get_encoding('cl100k_base').encode(text))
    except ImportError:
        return max(1, int(len(text.split()) / 0.75))


def check_schema():
    r = call_model(CASES['schema'])
    try:
        parsed = json.loads(r['text'])
        ok = isinstance(parsed.get('answer'), str) and isinstance(parsed.get('citations'), list)
    except Exception:
        ok = False
    return ok, r


def check_token_report():
    prompt = CASES['context']
    r = call_model(prompt)
    local = rough_tokens(prompt) + rough_tokens(r['text'])
    reported = r.get('reported_tokens')
    if not reported:
        return False, r
    return abs(reported - local) / max(local, 1) <= 0.20, {**r, 'local_estimate': local}


def check_latency():
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as pool:
        results = list(pool.map(lambda _: call_model(CASES['neutral']), range(10)))
    p95 = sorted(x['elapsed_ms'] for x in results)[9]
    return p95 < 4000, {'p95_ms': p95}

A report line contains the probe name, ok, elapsed_ms, and reported_tokens. Run it as a cron job or a serverless timer; the only resources required are network access, a small Python environment, and an API key. If you need an isolated host for that timer, MonkeyCode's free server option is one candidate; verify current terms before depending on the allowance.

Budget the free allowance with actual tokens

The 30,000,000-token allowance sounds large, but a probe schedule consumes it predictably. Assume one run has 15 calls: 6 contract cases plus 10 latency calls, with a slight overlap ignored for simplicity. At an average of 280 tokens per call, one run uses 4,200 tokens.

Cadence	Runs/day	Calls/day	Monthly tokens	Months on 30M
Hourly	24	360	3,024,000	about 9.9
Every 15 minutes	96	1,440	12,096,000	about 2.5
Every 5 minutes	288	4,320	36,288,000	under 1

These are examples. Actual input and output lengths, retries, and model-specific tokenizers change the numbers. Log reported tokens per run and compare them with the forecast instead of treating the allowance as a fixed calendar.

Hard gates and exit criteria

A scorecard is a conversation tool, not objective truth. It works only when the owner, expiry, and exit criteria are explicit.

Owner: platform lead or the engineer who owns the integration.
Window: 14 days or two release cycles, whichever is shorter.
Gate: all six cases pass in two consecutive probe runs.
Exit: if any non-latency case fails twice in a row, move the endpoint to offline-only work and re-evaluate after a provider update.
Data boundary: no customer PII, secrets, or proprietary code may be sent to a free endpoint.

Limitations and who should not use this

This contract does not validate factuality, fairness, security, or legal compliance. It can be gamed by a provider that optimizes for the probe. It is not appropriate for regulated outputs, medical, financial, or legal advice, high-stakes approvals, or any workload that requires an SLA. If a wrong answer creates liability, use a paid, contracted endpoint with human review.

The useful decision is not 'free versus paid.' It is what this endpoint can prove today, and when the team should stop believing it. A six-case contract provides a cheap early warning for the failure modes that usually reach production first.

Inject Model Failures on a Free AI Endpoint Before They Find You in Production

bestbee — Sat, 15 Aug 2026 20:03:15 +0000

Recent DEV threads debated AI tool permissions and watermarking, but both debates skip an earlier question: what does your application do when a model endpoint returns an unexpected shape, starts taking three times as long, or quietly changes its output style? A free model endpoint is a useful place to answer that question without risking production traffic.

This article shows how to use MonkeyCode's operator-supplied free model tier and free server option as a disposable fault-injection sandbox. MonkeyCode is described in operator materials as an open-source project with a free model tier and a free server option. Those two availability claims are treated here as an entry condition, not as a service-level guarantee.

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

Why a free endpoint is the right place to fail

A production AI path usually has one well-tested happy path and several under-tested unhappy paths. Teams often discover the unhappy paths after a model provider changes its latency profile, quota policy, or output schema. A free endpoint offers a repeatable environment to inject those failures deliberately before the application is forced to handle them live.

A free server is not a lower-cost production replacement. It is a controlled crash site: the application calls it, the operator injects a fault, and the team records whether the application degrades safely or cascades.

Step 1: Define the degradation contract

Before sending a single request, write down what the application must do for each failure class. This is not a scorecard; it is a runbook with a small set of mandatory fallback actions.

Failure class	Injected behavior	Required application response
Timeout	exceed the configured client timeout by 500 ms	return cached result, retry once, or fail with a typed error
Malformed JSON	return valid HTTP 200 with a broken body	parse a safe error, log a fingerprint, never crash the request
Output shift	return the same schema with materially different tone or length	apply a length cap, flag for human review, or disable the feature
Quota exhaustion	return a simulated 429 with a retry-after header	back off, pause model calls, and surface the state to the user
Silent truncation	return a completion that is cut at an unexpected token boundary	detect incomplete JSON, retry, or mark the result partial

Each row needs an owner and an expiry. A degradation contract is only useful if someone is assigned to keep it current when the application changes.

Step 2: Put a fault-injection proxy in front of the free endpoint

The proxy sits between the application and the MonkeyCode endpoint. In production, the proxy would be removed or replaced with the real provider. In this exercise, it is the test instrument.

The following example uses Python's standard library to avoid extra dependencies. It reads an INJECT environment variable, then alters the request or response accordingly.

import json
import os
import time
from http.server import BaseHTTPRequestHandler, HTTPServer
from urllib.request import Request, urlopen

UPSTREAM = os.environ.get('UPSTREAM_URL', 'https://free-endpoint.example/v1/chat')
INJECT = os.environ.get('INJECT', 'none')

class Proxy(BaseHTTPRequestHandler):
    def do_POST(self):
        length = int(self.headers.get('Content-Length', 0))
        body = self.rfile.read(length)

        if INJECT == 'timeout':
            time.sleep(1.5)  # assume client timeout is 1s
            self.send_response(200)
            self.end_headers()
            self.wfile.write(b'{"error":"simulated timeout"}')
            return

        req = Request(UPSTREAM, data=body, method='POST')
        req.add_header('Content-Type', 'application/json')
        try:
            with urlopen(req, timeout=5) as resp:
                data = resp.read()
        except Exception as exc:
            data = json.dumps({"error": str(exc)}).encode()
            self.send_response(502)
            self.end_headers()
            self.wfile.write(data)
            return

        if INJECT == 'malformed':
            data = b'{"choices": [{"message": {'  # deliberately broken
        elif INJECT == 'truncate':
            data = data[:len(data)//2]

        self.send_response(200)
        self.end_headers()
        self.wfile.write(data)

if __name__ == '__main__':
    HTTPServer(('127.0.0.1', 9090), Proxy).serve_forever()

Run the proxy with different injection modes in separate shell sessions:

INJECT=none python fault_proxy.py
INJECT=timeout python fault_proxy.py
INJECT=malformed python fault_proxy.py
INJECT=truncate python fault_proxy.py

The application under test should be pointed at http://127.0.0.1:9090 rather than at the upstream endpoint. All calls still reach the free model server, but the response path is altered for the specific fault class.

Step 3: Use a small request envelope to keep the exercise bounded

A free token allowance is finite, so fault injection should not use an unbounded prompt set. An envelope keeps the burn predictable and makes the experiment repeatable.

For a single fault class, use one prompt repeated five times. If each request logs about 1,000 prompt tokens and 300 completion tokens, that is roughly 1,300 tokens per request, or 6,500 tokens per fault class. The exact numbers depend on the model and endpoint; use the endpoint's metered usage rather than the example arithmetic. The example arithmetic here only illustrates the order of magnitude for planning.

A shell loop with the metered result is sufficient:

for mode in none timeout malformed truncate; do
  INJECT=$mode python fault_proxy.py &
  PROXY_PID=$!
  sleep 1
  for i in 1 2 3 4 5; do
    curl -s -X POST http://127.0.0.1:9090/v1/chat \
      -H 'Content-Type: application/json' \
      -d '{"messages":[{"role":"user","content":"Return a short JSON answer."}]}' \
      >> "${mode}.jsonl"
    echo >> "${mode}.jsonl"
  done
  kill $PROXY_PID
  wait $PROXY_PID 2>/dev/null
done

After the runs, inspect each JSONL file. The point is not to compare model quality; it is to verify that the application's fallback path was exercised and that no fault class caused a crash loop or an unbounded retry storm.

Step 4: Record the failure mode and the recovery signal

A fault-injection exercise is complete only when the team can name the recovery signal. If the application falls back to a cached result, the recovery signal is the cache hit. If it disables the AI feature, the recovery signal is a flag that a human can restore later.

Write one paragraph per fault class:

Fault: malformed JSON
Injected: truncated response body
Observed: client logged a parse error and returned a typed user-facing message
Recovery signal: error code AI_PARSE_FAILURE with no request retry
Pass: yes

Without a recovery signal, the team cannot tell whether the application is resilient or merely quiet about its failure.

Limitations

A free endpoint can disappear, change quota, or be suspended under load. It is not a production-sized load generator, and it may not expose the same latency or regional characteristics as a commercial provider. Do not infer production reliability from a free sandbox.

The proxy example is illustrative pseudocode, not a production-grade network service. The 30 million token allowance is operator-supplied and is not independently verified here; no model name, quota, hardware, duration, or benchmark is asserted beyond that claim.

Who should not use this approach

Teams with production PII, secrets, or regulated data should not send that data through a free endpoint or an unauthenticated local proxy.
Teams that do not have a written degradation contract should not inject failures; the exercise will only produce log noise.
Teams without a metered usage endpoint should not perform repeated free-tier tests, because they cannot control cost or quota exhaustion.
Teams that need hard latency or availability guarantees should not treat a free server as anything more than a disposable test surface.

If the goal is a low-stakes environment to rehearse model downtime, quota exhaustion, and malformed responses, MonkeyCode's free model and free server option can host the isolated traffic. Verify the current quota, retention, and acceptable-use terms before using the free server for anything beyond fault-injection testing.

Free Compute Outlives Its Usefulness: A Decommissioning Checklist for Open Model Pilots

bestbee — Sat, 15 Aug 2026 13:34:49 +0000

The recent conversations about watermarking and agent tool trust expose a less visible governance problem: most AI pilots have no planned end. A team will stand up a free model endpoint, run a few probes, and then keep it around because stopping feels like losing free value. The result is a permanent shadow service that nobody reviews, running on a model nobody re-evaluated.

Free access makes this worse. A paid API bill would force a monthly cost review. A 30 million token allowance with no invoice removes that review trigger. The endpoint becomes background infrastructure, exactly when the underlying model may be drifting, deprecated, or replaced by a safer alternative.

This article gives product and platform leads a decommissioning review rather than another adoption scorecard. Adoption tells you when to start. This tells you when to stop.

Decommissioning is a separate gate

An acceptance gate answers: does this model pass enough probes to receive traffic? A decommissioning gate answers: does this model still justify traffic, even though the cost is zero? The two are different because the cost signal is absent in the second case.

The absence of cost is not the same as the absence of risk. A free endpoint can still consume engineering time during incidents, expose stale output to users, or violate an updated policy about model provenance or watermark verification. The decommissioning review replaces the missing bill with an explicit owner and an expiry.

Four fields that trigger a sunset review

A decommissioning checklist should be small enough to run in one meeting, with each field owned by a person who can change the outcome.

Field	Signal to inspect	Example retirement trigger	Owner	Review expiry
L1 model freshness	Model ID, release date, or provider changelog	Model has no verified update in 90 days, or provider marks it frozen	platform lead	30 days
L2 output drift	Nightly probe set compared to a pinned baseline	Median semantic similarity below 0.85 across two consecutive runs	model owner	7 days
L3 policy compliance	Refusal consistency, watermark or audit checks, data-handling rules	Two refusals skipped, or a required audit field is missing	security owner	every run
L4 dependency cost	Team hours spent maintaining the integration, incident count	More than 4 engineer-hours per month on endpoint upkeep or failed calls	engineering manager	30 days

The retirement rule is: if any field stays red across two consecutive reviews, the endpoint is decommissioned or moved to a labeled research-only lane with no user traffic. Passing the original acceptance harness does not exempt the endpoint from this review.

A sunset-check script

The script below is a proposal, not a product benchmark. It reads a small JSON record the team keeps for each free endpoint and prints decommissioning candidates. Replace the model name and the date fields before running it.

#!/usr/bin/env python3
"""Flag free model endpoints that have outlived a review trigger.

Each endpoint has a JSON record. The script is deliberately simple and
should be adapted to the provider fields your team can actually inspect.
"""

import datetime
import json

ENDPOINT_RECORD = {
    "model_id": "replace-with-pinned-model-id",
    "last_verified_update": "2026-05-01",
    "last_drift_score": 0.82,
    "refusal_checks_passed_last_run": 1,
    "refusal_checks_required": 2,
    "engineer_hours_30d": 5.0,
}

TODAY = datetime.date(2026, 8, 15)


def days_since(date_string):
    return (TODAY - datetime.date.fromisoformat(date_string)).days


def review_endpoint(record):
    flags = []

    if days_since(record["last_verified_update"]) > 90:
        flags.append("L1 freshness: no verified update in 90 days")

    if record["last_drift_score"] < 0.85:
        flags.append("L2 drift: last score below 0.85")

    if record["refusal_checks_passed_last_run"] < record["refusal_checks_required"]:
        flags.append("L3 compliance: missing refusal or audit check")

    if record["engineer_hours_30d"] > 4.0:
        flags.append("L4 dependency: more than 4 engineer-hours in 30 days")

    if flags:
        return {"model_id": record["model_id"], "action": "schedule decommissioning review", "flags": flags}
    return {"model_id": record["model_id"], "action": "keep with next review expiry"}


if __name__ == "__main__":
    print(json.dumps(review_endpoint(ENDPOINT_RECORD), indent=2))

The script does not make the decision. It surfaces the stale record a human must inspect. That separation matters because a low drift score may be explainable by a deliberate prompt change, and an old model release date may be irrelevant if the model is frozen and still meets the team's needs.

Where free access fits

An operator-supplied free model access and free server option (Disclosure: This article was prepared as part of MonkeyCode's product outreach.) is useful for testing the decommissioning workflow itself. A team can create a throwaway endpoint, fill in the four fields, force one field into the red, and verify that the review owner is notified. That rehearsal costs nothing and does not require production traffic.

A free server also makes a clean retirement easier to practice. Because there is no contract to cancel, the only removal work is removing the integration from the calling code and archiving the probe history. The harder part is deciding to remove it while it still technically works.

Limitations and non-users

The checklist does not replace a full model risk assessment. It assumes the team already has a pinned baseline for drift and a security owner who can inspect refuse-and-audit behavior. It also assumes the model provider's changelog or metadata is accessible; in some open-source distribution channels, the only freshness signal is a Git commit, which requires manual collection.

This approach is not for a team that still cannot name an owner for each field. It is also not for a team that never created an acceptance harness in the first place; the retirement review will have no historical baseline to compare against. And it is not for regulated production systems where any free tier is disallowed, regardless of how often it is reviewed.

A natural next step is to take the current free endpoint record and run the script once. If none of the fields is red, store the result with the next review date. If one field stays red across two reviews, archive the endpoint instead of extending the experiment. Asking which field would reverse the retirement decision is more useful than asking whether the free tokens have run out.

A Maintainer's AI Feature Decision: Spend Free Tokens on a Build-vs-Buy Scorecard

bestbee — Sat, 15 Aug 2026 06:51:26 +0000

An open-source maintainer's decision to add AI is rarely a technology problem. It is a maintenance budget problem. The model can generate issue labels or summarize pull requests, but someone still has to pay for tokens, host the service, handle abuse, and keep the feature working when the upstream API changes. If that budget is zero, the feature dies in the README.

The MonkeyCode project currently offers a free model access path and a free server option. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The 30M token allowance and free server option are operator-supplied for August 2026; confirm the current limits before budgeting.

This free tier is not a discount. It is decision capital: a limited budget you can spend before committing future maintenance time. For a maintainer, the correct use of that capital is not to ship a demo, but to answer one question with evidence: for your specific project, should you build an AI feature, buy a managed AI feature, or skip AI entirely?

This article shows how to spend a small fraction of the free allowance to produce a build-vs-buy scorecard for an open-source project, including a reproducible cost-probe script and a hard gate for when not to add AI at all.

Why maintainers need a different decision model

A startup evaluating an AI feature can model revenue, churn, and support load. An open-source maintainer faces different constraints:

The budget for infrastructure is often donated, temporary, or nonexistent.
The maintainer's own time is the scarcest resource, not compute.
A new feature becomes a permanent code path that must be reviewed, tested, secured, and supported across releases.
Contributors may not want to maintain an AI component with opaque dependencies.

A build-vs-buy decision for an open-source project is therefore not primarily about token cost. It is about maintenance cost per release and community acceptance risk. The free model and server can measure the first variable. They cannot measure the second, which is why the scorecard has a veto gate.

Step 1: Pick three candidate features and one owner

Do not run a generic evaluation. Choose three concrete features that have appeared in issue requests or pull request discussions. Common examples:

Issue classifier: label new issues as bug, question, enhancement, or spam.
PR summary generator: produce a short changelog-ready summary for each merged PR.
FAQ responder: answer questions using only the project's existing documentation.

Assign one maintainer as the evaluation owner. The owner is responsible for recording token usage, saving outputs, and writing the final recommendation. If no maintainer will own the evaluation, stop and record "skip AI" as the default.

Step 2: Run a cost probe against the free server

The script below sends a fixed set of requests to the free server and measures three things: completion quality (coarse human score), latency, and token consumption per request. It deliberately uses a small sample size because the goal is not statistical proof but a cost-per-feature estimate.

The probe assumes the endpoint is OpenAI-compatible. Keep the endpoint and model in environment variables, never in source.

import os
import time
import json
import httpx

BASE_URL = os.environ["MONKEYCODE_BASE_URL"]
MODEL = os.environ["MONKEYCODE_MODEL"]
API_KEY = os.environ["MONKEYCODE_API_KEY"]

# Three candidate features, each with a realistic prompt.
PROBES = [
    {
        "feature": "issue_classifier",
        "prompts": [
            "Label this issue: 'I tried to install on Ubuntu 22.04 but the build script fails with make error 2'",
            "Label this issue: 'Can you add support for Python 3.12?'",
            "Label this issue: 'Please give me free support for my homework'",
        ],
    },
    {
        "feature": "pr_summary",
        "prompts": [
            "Summarize this PR: adds retry logic to the HTTP client and updates tests",
            "Summarize this PR: fixes a race condition in the cache and bumps the minor version",
        ],
    },
    {
        "feature": "faq_responder",
        "prompts": [
            "Using only the docs, answer: How do I configure retries?",
            "Using only the docs, answer: What is the default cache TTL?",
        ],
    },
]

async def probe(client, feature, prompt):
    t0 = time.perf_counter()
    try:
        resp = await client.post(
            "/chat/completions",
            json={
                "model": MODEL,
                "messages": [{"role": "user", "content": prompt}],
                "temperature": 0,
            },
        )
        body = resp.json()
        usage = body.get("usage", {})
        return {
            "feature": feature,
            "status": resp.status_code,
            "latency_ms": round((time.perf_counter() - t0) * 1000),
            "prompt_tokens": usage.get("prompt_tokens", 0),
            "completion_tokens": usage.get("completion_tokens", 0),
            "content": body.get("choices", [{}])[0].get("message", {}).get("content", "")[:120],
        }
    except httpx.HTTPError as exc:
        return {
            "feature": feature,
            "error": type(exc).__name__,
            "latency_ms": round((time.perf_counter() - t0) * 1000),
        }

async def main():
    async with httpx.AsyncClient(
        base_url=BASE_URL,
        headers={"Authorization": f"Bearer {API_KEY}"},
        timeout=15,
    ) as client:
        results = []
        for block in PROBES:
            for prompt in block["prompts"]:
                results.append(await probe(client, block["feature"], prompt))
        print(json.dumps(results, indent=2))

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

After running the script, fill in the following table for each feature. Score output quality from 0 to 3: 0 = unusable, 1 = needs heavy editing, 2 = needs light editing, 3 = acceptable as-is. Do not ask the model to score itself.

Feature	Median latency ms	Avg prompt tokens	Avg completion tokens	Quality score
Issue classifier
PR summary
FAQ responder

If any feature times out or returns repeated 500s, mark it as not viable on the free server. Do not retry more than once per prompt; the point is to observe real failure, not to burn allowance chasing latency.

Step 3: Convert token burn into a release cost

Use the average token counts to estimate what happens after the free tier expires or the feature scales. The formula is:

monthly tokens = average tokens per request × estimated requests per month

A small project might get 200 issues per month, each requiring one classification call. If the classifier uses 800 tokens per request, that is 160,000 tokens per month. At a typical managed API price of $0.50 to $3.00 per million tokens depending on the model, that feature costs between $0.08 and $0.48 per month. The token cost is effectively zero compared with the maintainer time required to review every classification.

But a FAQ responder that runs on every user query, with 50,000 requests per month and 1,200 tokens per request, consumes 60 million tokens per month. That is $30 to $180 per month, which is not zero for a volunteer project. The free 30M token allowance would cover roughly half a month at that volume. The real cost is not the USD; it is the migration and abuse-handling work when the free tier ends.

Record the break-even point for each feature. If maintenance time for a naive rule-based classifier is 10 hours per year, and the AI classifier requires 20 hours of prompt tuning, dependency updates, and issue triage per year, the AI feature may be worse even if token cost is low.

Step 4: Fill the build-vs-buy scorecard

For each candidate feature, score the project against the following gates. Use the numbers from Steps 2 and 3. A scorecard is a conversation tool, not objective truth about MonkeyCode or any other provider.

Gate	Question	Pass condition	Evidence source
Quality floor	Is the free-server output acceptable without heavy editing?	Quality score ≥ 2	Step 2 table
Cost fade	Will the feature remain affordable when the free tier ends?	Estimated monthly cost ≤ donation budget or sponsor contribution	Step 3 formula
Maintenance fit	Can one maintainer support the feature with existing volunteer time?	Expected maintenance hours per month ≤ 2	Maintainer estimate
Dependency risk	Is the endpoint replaceable without rewriting the feature?	OpenAI-compatible API and clear abstraction layer	Code review
Community veto	Will contributors reject an AI component on privacy or trust grounds?	No maintainer or core contributor objects	Project discussion

Decision rule:

If all five gates pass, buy a managed API for the feature and keep the integration thin.
If gates 1, 2, and 3 pass but dependency risk fails, build a self-hosted version using the free server as a temporary backend while the abstraction is tested.
If the community veto gate fails, skip AI regardless of the other scores. An unwanted AI feature is a fork risk.
If quality floor or cost fade fails, skip AI and close the feature request with the recorded evidence.

What this method does not prove

The small sample size is directionally useful, not statistically valid. The free tier may route to a different model, have lower rate limits, or serve a slower region than the paid option. The output quality score is subjective. The cost estimate ignores retries, context growth, and prompt changes. Treat all numbers as order-of-magnitude estimates, valid only until the free tier terms change.

This method also does not measure community acceptance. No amount of free tokens fixes the fact that some open-source communities reject any AI-generated content on principle. The community veto gate is intentionally a hard stop, not a score.

Who should not use this approach

Skip this if the project already has a paid AI integration, if the maintainer team cannot agree on a single evaluation owner, or if the project's license makes an external API dependency legally risky. Skip it also if the project has no real user requests for AI features; speculative AI additions are the fastest way to burn free tokens without changing the project.

For a maintainer, the best outcome of this exercise is often a negative one: closing three feature requests with a data-backed "not in scope" note. That is a better use of the free tier than shipping a demo that becomes an unpaid maintenance burden.