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Emery Yang
Emery Yang

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A 90-Minute Ship-or-Kill Spike for Free AI Hosting

#ai #productivity #testing #devops

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

Promises are cheap. Free AI tiers promise tokens, servers, and uptime. Evidence is rare. This post runs a 90-minute spike. The goal: decide if MonkeyCode's free tier and free server deserve your time. The method transfers to any AI vendor.

The Problem

You see a free tier. It offers 10 million tokens and a free server. Your engineering discipline says "measure it." Here is a repeatable test.

Why 90 Minutes?

Ninety minutes forces focus. It limits waste. It fits a lunch break. You can generate, deploy, and smoke-test in that window. If the stack is any slower, the tool is not production-ready.

Hypotheses

  • H1: A CRUD API can be generated, deployed, and smoke-tested in 90 minutes.
  • H2: One iteration cycle fits inside the free token allowance.
  • H3: The free server returns 2xx at least 95% of the time.

Define kill criteria before you start. Do not move the goalposts.

Spike Design

Time-box: 90 minutes. No extensions.
Artifact: a FastAPI app with one endpoint and one health check.
Track: tokens used, deploy time, error rate, latency.

Step 1: Generate the App

Write a focused prompt. Include exact dependencies and behavior.

Generate a FastAPI app with a GET /items endpoint. It returns a JSON list of three strings. Use pydantic for the response model. Keep main.py self-contained.

Send this to MonkeyCode's free model. Save the output as main.py.

I won't name the underlying model. My protocol says don't invent specs.

Prompt Design Rules

  • Be specific about the stack.
  • List constraints line by line.
  • Request a health check endpoint.
  • Keep the prompt under 200 tokens.

A good prompt is a spec. A bad prompt is a wish.

Step 2: Measure Token Burn

Count the bytes in your generated file. Then divide by four. That is a rough token count.

expr $(wc -c < main.py) / 4
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This is crude. Use the provider's real tokenizer when possible. For a 90-minute spike, crude is fine.

Step 3: Deploy to the Free Server

MonkeyCode is open source. Its free server is a sandbox for experiments. The exact CLI depends on your account. I won't invent commands.

Generic checklist:

  1. Authenticate with the CLI.
  2. Point it at the project folder.
  3. Deploy.
  4. Capture the returned URL.

Time this step. If deployment takes more than 15 minutes, kill the spike.

Deployment Pitfalls

  • Wrong environment variables.
  • Missing dependency manifest.
  • Port binding mismatches.
  • Stale build cache.

Document each error. Iterate quickly. Do not debug for more than 20 minutes.

Step 4: Smoke Test

Run a loop for 10 minutes. Hit the health endpoint every 5 seconds. Record status and latency.

URL="https://your-app.mock.server/health"
for i in $(seq 1 120); do
  code=$(curl -s -o /dev/null -w "%{http_code}" -w "$code %{time_total}\n" "$URL")
  sleep 5
done
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Then compute the success rate.

awk '$1 ~ /^2[0-9][0-9]$/ {ok++} END {print ok/NR*100}' log.txt
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Compare against your threshold.

Decision Table

Gate Pass Fail
Generate app < 15 min > 30 min
Token usage < 2M > 5M
Deploy time first attempt > 3 attempts
Health success >= 95% < 90%

One "fail" means ship-or-kill. Stop the spike.

Reading the Results

  • Four passes: the free tier works for prototypes.
  • Three passes: usable with caveats.
  • Fewer: move to a paid tier or another tool.

The table is a guide, not a religion. Adjust thresholds to your own risk tolerance.

Limitations

One test is not a benchmark. Token usage depends on prompt quality and model drift. Free servers rarely guarantee SLAs. Latency varies by region and load.

The 10M token figure is operator-reported. Verify it on your account. It may change.

Who Should Not Use a Free Server

  • Teams with production uptime requirements.
  • Apps handling personal health or financial data.
  • Projects needing predictable sub-100ms latency.

Use the free tier for prototypes and demos. That's its honest home.

The Verdict

Run your own spike. Ninety minutes is a small price for real evidence. A dashboard number is not uptime. A token counter is not a deployment.

Your verdict depends on your prompts, your model, and your load. Do not trust my conclusions. Run the test. Then share your gate results.

The open-source repo invites your own spikes. Break something small before you trust it with something big.

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