Every quarter, I talk to a platform lead who just "saved" $800 a month by self-hosting a coding model. Then they open the time log: eleven hours a month of GPU restarts, image rebuilds, and a pager rotation nobody wanted. At a blended $120 per engineering hour, that's $1,320 of invisible spend.
The self-hosted setup wasn't cheaper. It was just a different invoice.
I see the same mistake in both directions. Teams compare "free hosted" and "self-hosted" as if they were line items on the same bill. They're not. They're two different cost structures with different break-even points — and that break-even moves every time your token volume, ops load, or exit cost changes.
So here's the model I use when a team asks: should we take the free hosted path, or run our own stack?
MonkeyCode is one of the options on the free side right now. It's an open-source project, and its current offer includes free model access with a 10-million-token allowance plus a free server option, as of this writing. Disclosure: This article was prepared as part of MonkeyCode's product outreach. But don't take my word for either side. Run the numbers below first.
The four variables
Every "free vs. self-hosted" debate reduces to four numbers:
| Variable | Meaning | Unit |
|---|---|---|
| V | tokens consumed per month | tokens/month |
| P | price per token if you bought them | $/token |
| O | ops hours per month to run your own stack | hours/month |
| C | blended cost of an engineering hour | $/hour |
Plus two secondary numbers: H, the monthly amortized hardware cost of self-hosting, and M, the one-time cost of migrating off the free path later.
Monthly cost of the free hosted path:
F = 0 (tokens) + 0 (ops) + R
where R is your risk premium — the subjective cost of quota changes, price changes, or the offer disappearing.
Monthly cost of the self-hosted path:
S = V × P + O × C + H
The decision rule is boring on purpose: choose free hosted while F < S. The interesting part is that the inequality flips faster than most teams expect.
A worked example
Take a six-person team. They burn 3 million tokens a month. A comparable paid model runs about $6 per million tokens on a typical list price — plug in your own provider's number, because this is an assumption, not a quote.
- V = 3,000,000 tokens/month
- P = $0.000006/token
- O = 8 hours/month (updates, monitoring, restarts)
- C = $100/hour
- H = $200/month (amortized GPU or rented box)
S = 3 × $6 + 8 × $100 + $200 = $18 + $800 + $200 = $1,018/month
The free path costs $0 plus a risk premium. Even if you price the risk at $300/month — the offer changes, the quota shrinks, the server gets slow — free hosted wins by roughly $700/month. Easy call.
Now flip the team. A 40-person platform org, 80 million tokens a month, and a platform team that already runs Kubernetes.
- V = 80,000,000 tokens/month
- P = $0.000006/token
- O = 3 hours/month (they have automation)
- C = $100/hour
- H = $0 (spare capacity)
S = 80 × $6 + 3 × $100 = $480 + $300 = $780/month
Now the free path only wins if your risk premium stays below $780. For a 40-person org, that premium — quota cliffs, data controls, migration effort — can easily exceed it. Same formula, different answer. The decision is structural, not ideological.
Sensitivity: where does it flip?
Here's the table I actually show teams. It answers: "at what token volume does self-hosting beat free hosted, given my ops load?"
| Ops hours/month | Break-even token volume (at $6/M tokens, $100/hr) |
|---|---|
| 2 | 33.3M tokens/month |
| 5 | 83.3M tokens/month |
| 10 | 166.7M tokens/month |
| 20 | 333.3M tokens/month |
The formula behind it: V × $6/M = O × $100, so V = O × 16.7M. Below the line, free hosted wins on pure math. Above it, you're paying more for tokens than the ops would cost.
The punchline: at typical token prices, one hour of ops equals about 16.7 million tokens. Most teams overestimate their token bill and underestimate their ops bill. That's why the free server option matters more than the free tokens — the server removes the expensive variable.
Four hard gates
The math is necessary but not sufficient. Run these four gates before you commit:
- Token volume gate. If V × P already exceeds O × C + H, the free path is leaving money on the table.
- Ops capacity gate. If you don't have 5+ spare hours a month for maintenance, free hosted wins even when the token math says otherwise. Your team's time is the real budget.
- Data boundary gate. If code or prompts must not leave your environment, free hosted is disqualified. No risk premium can fix a compliance violation.
- Exit cost gate. If M — the cost of migrating off the free path later — is larger than six months of savings, the free path is a trap, not a deal.
The exit plan you need anyway
Here's the part most free-tier users skip, and it's the part that turns a good deal into a bad one.
Keep a portability ledger in your repo: prompts, custom instructions, tool configurations, and model-specific workarounds. Log token usage weekly, not monthly. Set a review date — 90 days out — where you re-run the break-even with real numbers instead of estimates. And define the archive rule: what happens to your agent's history and artifacts if you leave?
If the free path disappears and your entire workflow is entangled with it, M explodes. That's the hidden price of free. It's not in the token bill; it's in the exit.
Who should not use this approach
Be honest about the limits:
- Teams with hard data-residency or compliance requirements should not use free hosted AI, period.
- Teams already near a quota cliff should treat the free allowance as a trial, not a production plan.
- Teams that need a guaranteed SLA for agent output should budget for the paid path from day one.
The numbers in this article are assumptions, not quotes. Token prices drift, free offers change, and the 10-million-token allowance and free server I mentioned are current as of this writing — not a promise about next quarter. Run the model with your own V, P, O, C, H, and M.
If the free path wins on paper, the next question isn't "is it free?" It's "what's my exit plan?"
And if you want to test the free path without a credit card, MonkeyCode's free model access and free server are a reasonable place to start — after you've run the numbers, not before.
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