Free Hosted AI or Self-Hosted? Run a 20-Minute Comparison Harness Before You Choose

Two teams. Same stack. Opposite choices.

Team A grabbed free hosted tokens and shipped a demo in an afternoon. Team B self-hosted an open model and spent three weeks fighting GPU drivers. Six months later, Team A is re-architecting because a rate limit broke their CI at 4 p.m. on a Friday. Team B is... fine.

The mistake wasn't the choice. It was that neither team measured before choosing.

Free hosted AI looks like a no-brainer. No GPU. No ops. No invoice. But "free" is a pricing model, not a fit assessment. Fit depends on variables you can measure in an afternoon: latency, token burn, error rate, and the cost of switching.

Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode's current offer — free model access (10M tokens) and a free server option — is exactly the kind of thing that looks like a no-brainer until you measure it. So let's measure.

The 5-Branch Decision Tree

Before you compare endpoints, walk the tree. Each branch kills a candidate fast.

1. Does the data cross a boundary it shouldn't? Code, logs, and customer PII have different risk. If your policy says no, the comparison is over. Self-host, or buy a paid tier with a data-processing agreement.
2. Is latency part of the product? An agent that takes 40 seconds for a refactor is fine for a human. It's a disaster inside a CI gate. Measure p95, not the median.
3. What is the cost ceiling? Free tiers have a ceiling — tokens, requests, or concurrency. Compute what happens the day you hit it.
4. Who operates the alternative? Self-hosting isn't free. It's an ownership transfer to your team. If nobody owns it, you don't have a self-hosted option; you have a future incident.
5. What is the exit plan? If the free tier changes terms, can you switch without rewriting every prompt and integration?

If a branch kills your favorite option, you just saved yourself a migration. If nothing dies, run the harness.

The 20-Minute Comparison Harness

Here's the artifact: a small Python script that sends the same prompt set to two endpoints and reports latency percentiles, error rate, and token burn.

# compare_endpoints.py — run the same prompts against two AI endpoints
import argparse, json, statistics, time, urllib.request

PROMPTS = [
    "Explain what this function does and name its failure modes: "
    "def retry(fn, times=3):\n    for i in range(times):\n        try:\n            return fn()\n        except Exception:\n            if i == times - 1:\n                raise",
    "Write a pytest suite for a function that parses ISO 8601 timestamps.",
    "Refactor this loop to remove the O(n^2) behavior: "
    "for a in items:\n    for b in items:\n        if a.id == b.id and a is not b:\n            print(a)",
]

def call(endpoint, headers, payload, timeout=90):
    start = time.perf_counter()
    req = urllib.request.Request(
        endpoint, data=json.dumps(payload).encode(), headers=headers
    )
    try:
        with urllib.request.urlopen(req, timeout=timeout) as resp:
            body = json.load(resp)
        return time.perf_counter() - start, body
    except Exception as exc:
        return time.perf_counter() - start, {"error": str(exc)}

def run(endpoint, headers, n=10):
    latencies, errors, tokens = [], 0, 0
    for i in range(n):
        prompt = PROMPTS[i % len(PROMPTS)]
        latency, body = call(endpoint, headers, {"prompt": prompt})
        latencies.append(latency)
        if "error" in body:
            errors += 1
        else:
            tokens += body.get("usage", {}).get("total_tokens", 0)
    latencies.sort()
    return {
        "p50_s": round(statistics.median(latencies), 2),
        "p95_s": round(latencies[max(0, int(len(latencies) * 0.95) - 1)], 2),
        "error_rate": round(errors / n, 2),
        "avg_tokens_per_ok": round(tokens / max(n - errors, 1)),
    }

if __name__ == "__main__":
    ap = argparse.ArgumentParser()
    ap.add_argument("--endpoint", action="append", required=True)
    ap.add_argument("--header", action="append", default=[])
    args = ap.parse_args()
    headers = dict(h.split(":", 1) for h in args.header)
    for ep in args.endpoint:
        print(ep, run(ep, headers))

Run it:

python compare_endpoints.py \
  --endpoint https://free-hosted.example/v1/complete \
  --endpoint http://localhost:8080/v1/complete \
  --header "Authorization: Bearer $TOKEN"

Three prompts, ten iterations, two endpoints. Twenty minutes, including coffee.

What it gives you: p50 and p95 latency, error rate, and average token burn per successful call. That's the raw material for the decision. The p95 here is an approximation for small samples — bump n to 30 if you need tighter numbers.

The Worked Example

Say your team makes 120 agent calls per day, 22 working days a month. That's 2,640 calls. Your harness shows an average of 3,800 tokens per call. Monthly burn: ~10 million tokens.

Now the free tier's 10M token allowance stops being a number and becomes a date: you hit the ceiling around day 22. Every call after that is either slower, rejected, or billed. Which one? That depends on the terms — and terms change.

Same numbers, different workload: 40 calls per day at 1,200 tokens each. Monthly burn: ~1.1M tokens. The ceiling is irrelevant. Now the decision turns on latency and data policy, not cost.

That's the point of measuring first. The free tier isn't good or bad. It's a fit question, and fit is a function of your numbers.

Who Should Pick Which Path

Your situation	Pick	Because
Prototype, eval harness, no sensitive data	Free hosted	Zero ops, fast to start, ceiling is far away
Regulated data, offline work, air-gapped	Self-hosted	The data boundary is the requirement
CI gates, customer-facing latency SLA	Paid hosted	p95 matters and someone must own it
Small team, no dedicated ops	Free or paid hosted	Self-hosting without an owner is a liability
High volume, stable workload	Self-hosted or paid	A free tier's ceiling becomes a recurring incident

What This Harness Does Not Measure

Three things, and they matter.

First, answer quality. The harness measures speed and errors, not whether the refactor is correct. Run a small quality pass with the same prompts and have a senior engineer grade the outputs blind.

Second, terms of service. A free tier is a probe, not a contract. The allowance, the model, the retention policy — all of it can change. Re-run the harness quarterly, and keep the exit plan warm.

Third, your own time. Self-hosting has a real cost in engineering hours. If no one on your team wants to own it, a "free server" is a gift you can't afford.

So here's the honest summary. Free hosted AI, including MonkeyCode's free model access and free server option, is a legitimate starting point — for workloads whose numbers fit. The way to find out is not a blog post. It's a 20-minute harness, your prompts, and two endpoints.

Run it this week. The numbers will argue for you.