Most SMB AI decisions eventually land on this question: pay OpenAI's enterprise rate, or run something ourselves?
Neither answer is universally right. Here is how to think through it at the 50-person mark — a size where both options are technically viable but operationally very different.
The short version
Pick ChatGPT Enterprise if your team needs to ship productive AI workflows in the next 30 days and you don't have a dedicated IT person who can own infrastructure.
Pick self-hosted if you handle sensitive regulated data, have a compliance team that's already told you cloud AI is off the table, or you have a systems administrator who can own the deployment day two.
Side-by-side
| Factor | ChatGPT Enterprise | Self-Hosted (e.g., Llama, Mistral) |
|---|---|---|
| Monthly cost at 50 seats | ~$1,500–2,500/mo (OpenAI list pricing) | $300–800/mo cloud GPU, or $15–25k hardware one-time |
| Data leaves your network? | No (Enterprise BAA, zero data retention) | No — model runs on your infra |
| Compliance fit | HIPAA-eligible with BAA; SOC 2 Type II | Depends on where you host; you own the compliance story |
| Deployment effort | Low — IT admin sets up SSO, done in a day | High — model download, serving stack, monitoring |
| Model quality | GPT-4o; best-in-class for general tasks | Good; narrowing gap, but still behind on complex reasoning |
| Day-2 operations | Zero — OpenAI handles uptime, updates, scaling | Yours — you patch, scale, and monitor |
| Custom fine-tuning | Available via API (extra cost) | Full control; can train on proprietary data locally |
| Vendor lock-in risk | Moderate — pricing and API can change | Low — open weights, portable |
When ChatGPT Enterprise wins
For most 50-person companies, ChatGPT Enterprise is the practical choice because it removes the operations burden entirely. You're paying for someone else to keep the lights on, maintain the model, and handle security updates.
The zero-data-retention BAA is meaningful. OpenAI will sign a HIPAA Business Associate Agreement, which covers a lot of the compliance surface for healthcare-adjacent businesses. If your compliance posture requires that a vendor not train on your data, Enterprise delivers that out of the box.
The limit: you're paying per seat, and that seat cost adds up. At 50 users running it heavily, $2,000/month is a real line item — not catastrophic, but worth modeling out.
When self-hosted earns its keep
Self-hosted makes sense in a few specific situations:
Regulated data that can't touch cloud at all. Some legal, financial, or government-adjacent businesses have policies or contractual requirements that prohibit sending data to external APIs even with a BAA. In that case, self-hosted isn't optional — it's mandatory.
High inference volume at the commodity end. If your use case is summarizing internal documents or classifying support tickets — tasks where GPT-4o is overkill — a smaller self-hosted model (7B–13B parameters) does the job at a fraction of the cost. At high volume, the math flips quickly.
You want fine-tuning on your own data. Training a model on your product catalog, your contracts, or your support history is straightforward with open weights. On ChatGPT Enterprise it's possible via the API, but you're working within OpenAI's constraints and paying their rates.
The real cost self-hosted companies undercount: the person-hours to maintain it. Someone needs to own the GPU instance, monitor the inference server, apply security patches, and test model updates before pushing them to prod. For a 50-person company without a dedicated ML engineer, that burden usually lands on an already-stretched IT generalist.
The hybrid play
A number of companies land here: ChatGPT Enterprise for general-purpose employee use, self-hosted for the one workflow that can't touch external APIs. It adds operational complexity but keeps costs reasonable and satisfies both the productivity team and the compliance team.
What to ask before you decide
- Does any data you'd process count as PHI, PCI, or fall under a data-handling agreement you've signed?
- Do you have someone who can realistically own an inference server day to day?
- What's your monthly inference volume — hundreds of calls or hundreds of thousands?
- Is vendor lock-in a board-level concern, or a nice-to-avoid?
If you're stuck on the decision, our Audit covers exactly this kind of evaluation — we map your data flows, model your actual costs, and tell you which option fits your risk profile. See /services for what that looks like.
Drafted with AI assistance, edited by Jaymar L. before publication.