Ship an AI Feature for $0: A Bootcamp Lab With Checkpoints and a Fair Rubric

You can learn more about AI in one afternoon of constrained deployment than in a week of copying tutorials. The trick is to put yourself inside a real budget, a real deadline, and a grading system that measures judgment, not just “it works.”

This lab does exactly that. You’ll build, deploy, and evaluate a small AI-powered feature on a free server, using MonkeyCode’s free model access and the free server option. By the end, you’ll have a reusable artifact, a token budget, and a rubric that actually tells you how well you engineered the thing.

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

Why this lab exists

Most bootcamp projects end with a git push and a sigh of relief. Nobody asks about the latency, the failure modes, or the cost of running the thing. Then you get to a real job and a senior engineer asks, “How much does this endpoint cost per request?” and you freeze.

The fix is to design a lab where those questions are not optional. You have a free tier, a hard token ceiling, and a server that will restart if you abuse it. Those constraints force you to think like a production engineer.

Still not sure? Let me ask you a question: would you rather explain a failed deployment to a senior dev, or to your future self at 2 AM? Exactly.

The mission

Build a tiny web app that uses an AI model to classify support tickets into urgent, normal, or low. Deploy it on the free server. Keep each request under a self-imposed token budget. Then document your choices.

Here’s what you get from MonkeyCode:

• Free model access with a generous token allowance (10 million tokens at the time of writing, per the operator)
• A free server that’s enough for a low-traffic demo
• No credit card required

That’s it. No invented benchmarks, no “blazing fast GPU” claims. Just enough to learn.

Setup (15 minutes)

1. Create an account on MonkeyCode and grab your API key.
2. Clone this starter repo (or create your own with a single main.py).
3. Install dependencies: pip install fastapi uvicorn requests.
4. Set your environment variables: export MONKEYCODE_ENDPOINT=... and export MONKEYCODE_KEY=....

Here’s a minimal FastAPI app. Note: this is example code; adjust the endpoint and payload shape to match your actual MonkeyCode API contract.

# main.py
from fastapi import FastAPI
from pydantic import BaseModel
import requests, os, json

app = FastAPI()

class Ticket(BaseModel):
    description: str

TOKEN_BUDGET = 50  # max tokens per request, we self-impose

def classify(description: str) -> str:
    payload = {
        "prompt": f"Classify support ticket as urgent, normal, or low. Ticket: {description}",
        "max_tokens": TOKEN_BUDGET
    }
    resp = requests.post(
        os.environ["MONKEYCODE_ENDPOINT"],
        headers={"Authorization": f"Bearer {os.environ['MONKEYCODE_KEY']}"},
        json=payload
    )
    return resp.json()["choices"][0]["text"].strip().lower()

@app.post("/classify")
def classify_ticket(ticket: Ticket):
    label = classify(ticket.description)
    return {"label": label, "cost": estimate_tokens(ticket.description) + TOKEN_BUDGET}

def estimate_tokens(text: str) -> int:
    # rough heuristic: 4 chars ≈ 1 token
    return len(text) // 4

Checkpoint 1: Call the model (30 minutes)

Your first milestone is simple: send a request, get a label, print it. Don’t worry about the web app yet. Just run a Python script that classifies three sample tickets.

• [ ] Script runs locally without errors
• [ ] All three labels match your manual expectation at least twice
• [ ] You can see the token count in the response

If you’re stuck, answer this: are you passing the right model name in the payload? Did you check the status code? Log everything.

Checkpoint 2: Deploy to the free server (30 minutes)

Now we go live. Push your code, set the same environment variables on the free server, and run with a production server, not the dev one.

uvicorn main:app --host 0.0.0.0 --port 8000

Then hit it from your local machine:

curl -X POST https://your-subdomain.example.com/classify \
  -H "Content-Type: application/json" \
  -d '{"description": "Login button returns 500 error"}'

Did you enable HTTPS? Add that to your checklist. Deploy once, break twice, fix three times — that’s the rhythm.

Checkpoint 3: Measure the damage (45 minutes)

Now the fun part. Calculate the cost per request using the token numbers in the response. Then find three ways to reduce it.

• Shorten the prompt without losing accuracy.
• Cache repeated ticket descriptions (bad idea if they’re unique, but good to try).
• Use a cheaper model if your plan exposes one.

Write a small script that sends the same ticket 10 times and logs the total tokens. That’s your “lab bill.”

Stretch goals

Finish the core lab? Try one of these:

1. Add retry logic with exponential backoff for transient 429s.
2. Add a /health endpoint that reports uptime and last error.
3. Build a simple queue that batches requests to stay under a per-minute limit.
4. Create a tiny dashboard that plots your daily token usage.

Each stretch goal adds a line to your resume and a risk to your deadline. That’s okay — you’re here to learn that tension.

The fair grading rubric

The whole point is to grade your engineering decisions, not the output’s sentiment. Use this rubric to score yourself 0–4 on each dimension.

Criterion	What “4” looks like	What “1” looks like
Correctness	Labels are reliable across 20 test cases	Works only on the exact samples you tried
Cost awareness	You can state token cost per request and justified the budget	You have no idea how many tokens you used
Deployment quality	HTTPS, graceful error handling, environment vars not hardcoded	Port 8000 exposed on a free server with no auth
Failure handling	Retries and timeouts are implemented and tested	Crashes on first 500 response
Documentation	README explains setup, cost, and limits	Only the code is in the repo

Total your score. 15–20 is ship-ready for an intern project. 8–14 is “keep refactoring.” Below 8? You’ve discovered real constraints — which is exactly the point.

Who should NOT use this lab

Be honest. If you’re building anything with strict latency requirements (under 300ms), high concurrency, or production SLAs, free-tier servers with shared GPUs will not cut it. This lab is for learning trade-offs, not for handling real customer traffic.

Also, don’t run a demo that generates thousands of requests just to see the meter spin. The free allowance exists to lower the barrier, not to be abused. Respect the limits or lose the privilege.

Limitations you should know

• The free server may restart on heavy load, so your app might occasionally go cold.
• Token costs vary with model choice; the 10M allowance will shrink if you pick a dense prompt.
• MonkeyCode’s free tier is a starting point, not a permanent production backend. If my request patterns change, the rules change too.

Final thought

You don’t need a big cloud budget to learn how to ship AI. You need a constraint, a measurable artifact, and a rubric that punishes hand-waving. This lab gives you all three — now go classify your first ticket, break your first deployment, and tell me what you’d grade differently.

Try it with your next side project. Then come back and argue with me about the rubric — that’s the best way to learn.