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

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.