Free, Self-Hosted, or Paid: Score Your Constraints First

You spent last weekend renting a GPU box, pulling a quantized model, and watching it refuse to load. By Sunday night it was running. By Monday you realized the model was the cheap part — the real cost was your weekend.

Now the same project needs a coding assistant, and you are staring at three doors: a free tier, a self-hosted model, or a paid API. Everyone has an opinion. None of them know your constraints.

Every week another open-weight model drops, and every week another free tier appears. That makes the choice harder, not easier. The real question is not "which model is best" — it is "which setup can I sustain for the next six months?"

That question is a function of five constraints: usage volume, data sensitivity, ops capacity, latency tolerance, and cost ceiling. Model quality is a filter, not the decision. You first decide where you can run the thing, then you pick the best model that fits there.

One option in the free column is MonkeyCode, an open source coding assistant that offers free model access and a free server option. At the time of writing, the free tier includes 10 million tokens — enough for real experiments, not just toy examples. Disclosure: This article was prepared as part of MonkeyCode's product outreach.

I am not going to tell you MonkeyCode is right for everyone, because it is not. The point of this article is the framework, and the framework works whether you end up on a free tier, a self-hosted model, or a paid API. Run the scoring script below and let your own answers do the talking.

The five constraints

Usage volume decides how generous a free allowance feels. If you run one refactor a day, ten million tokens look infinite. If you run agent loops all day, the same number becomes a countdown timer.

Data sensitivity is the hardest constraint to negotiate. If your code can leave your machine, free hosted access is fine. If it cannot — NDA, regulated industry, proprietary algorithms — self-hosting stops being an option and becomes a requirement.

Ops capacity is where most people lie to themselves. Self-hosting a model is not a one-weekend project; it is a subscription to upgrades, crashes, and disk space. Some people genuinely enjoy that subscription. Most people discover they do not.

Latency tolerance separates interactive work from automated pipelines. A shared free server will not beat a local GPU on response time. If you are typing prompts by hand, seconds are fine. If you are building an agent that calls the model in a tight loop, latency multiplies with every step.

Cost ceiling is the constraint everyone respects but nobody weights properly. Zero budget is a legitimate position, not a character flaw. It forces you to optimize for what you actually need, which is usually less than the marketing suggests.

The scoring script

Here is the script I use when someone asks me to decide for them. It asks the five questions, scores three setups — free hosted, self-hosted, paid API — and prints a recommendation. The weights are transparent, so you can argue with them.

#!/usr/bin/env python3
"""pick_setup.py — score free hosted, self-hosted, and paid API setups."""

DIMS = {
    "usage_volume":      {"q": "How much will you run it?",           "low": "a few requests a week", "high": "continuous agent loops"},
    "data_sensitivity":  {"q": "Can your code leave your machine?",  "low": "fully public",          "high": "NDA'd or regulated"},
    "ops_capacity":      {"q": "Do you want to operate a server?",   "low": "never again",           "high": "I already run infra"},
    "latency_tolerance": {"q": "How patient are you per request?",   "low": "seconds are fine",      "high": "I want fast loops"},
    "cost_ceiling":      {"q": "What is your monthly budget?",       "low": "zero",                  "high": "hundreds of dollars"},
}

OPTIONS = ["free_hosted", "self_hosted", "paid_api"]

# For each option and dimension: score for answer 0, 1, 2.
SCORES = {
    "free_hosted": {"usage_volume": [2, 1, 0], "data_sensitivity": [2, 1, 0],
                    "ops_capacity": [2, 1, 0], "latency_tolerance": [2, 1, 0],
                    "cost_ceiling": [2, 1, 0]},
    "self_hosted": {"usage_volume": [0, 1, 2], "data_sensitivity": [2, 2, 2],
                    "ops_capacity": [0, 1, 2], "latency_tolerance": [1, 2, 2],
                    "cost_ceiling": [0, 1, 2]},
    "paid_api":    {"usage_volume": [1, 2, 2], "data_sensitivity": [2, 1, 0],
                    "ops_capacity": [1, 1, 1], "latency_tolerance": [1, 2, 2],
                    "cost_ceiling": [0, 1, 2]},
}

WEIGHTS = {"usage_volume": 0.25, "data_sensitivity": 0.25, "ops_capacity": 0.20,
           "latency_tolerance": 0.15, "cost_ceiling": 0.15}

def ask(dim):
    meta = DIMS[dim]
    while True:
        try:
            v = int(input(f"{meta['q']} (0={meta['low']}, 1=between, 2={meta['high']}): "))
            if v in (0, 1, 2):
                return v
        except ValueError:
            pass
        print("Enter 0, 1, or 2.")

def main():
    answers = {dim: ask(dim) for dim in DIMS}
    totals = {}
    for opt in OPTIONS:
        totals[opt] = sum(WEIGHTS[d] * SCORES[opt][d][answers[d]] for d in DIMS)
    print("\nScores (higher is better):")
    for opt in sorted(totals, key=totals.get, reverse=True):
        print(f"  {opt:14s} {totals[opt]:.2f}")
    print(f"\nPick: {max(totals, key=totals.get)}")

if __name__ == "__main__":
    main()

The interesting output is not the winner — it is the margin. If free hosted wins by a wide margin, you have headroom. If it wins by 0.05, you are one rate limit away from misery.

Where each path breaks

The free path breaks on data and volume. It is the right call when your code is public, your usage is light, and your budget is zero. It is the wrong call when a single request contains something you cannot afford to leak.

The self-hosted path breaks on time. It is the right call when data cannot leave your network, or when you already run infrastructure and a model is just another service. It is the wrong call when you have a day job and a side project.

The paid path breaks on cost scaling. It is the right call when you need an SLA, a support channel, or throughput that free tiers cannot give you. It is the wrong call when your usage is so light that you would be paying for a subscription you barely touch.

A few honest limitations before you run this. The ten-million-token figure and the free server are what MonkeyCode offers today; quotas and availability can change, and I would not build a business on any free tier. Free servers have no SLA.

If your work is regulated or your uptime matters, that alone disqualifies the free path. The script also scores setups, not models — you still have to evaluate model quality separately, on your own tasks.

So do not use this approach if compliance forbids third-party processing. Do not put a free tier in a CI pipeline that blocks releases. And do not mistake a free server for a production environment.

The cheapest way to test the framework is to run the script with your real answers, then spend one afternoon on the setup it recommends. If your answers land in the free column, MonkeyCode's free tier is a reasonable place to start — the repo is linked in my profile. If they do not, the script just saved you a weekend.