Why Companies Leave the ChatGPT API
Two reasons come up again and again: cost and data. A token bill scales with usage and has no ceiling, so a busy month can quietly cost more than a server would have outright. And every request carrying sensitive information leaves your infrastructure for someone else’s servers, which is exactly what data-protection law is built to catch, whether that means GDPR-style rules in the EU, Russia’s data-protection law (152-FZ), or a sector rule like HIPAA. On top of that, you inherit the vendor’s problems too: rate limits, models deprecated on their schedule instead of yours, and billing or regional-access issues you can’t fix from your side. We go deeper on the economics in a separate comparison of on-premise LLMs versus the cloud.
The Migration, Step by Step
| Step | What it involves |
|---|---|
| 1. Audit your use cases | which tasks and volumes are actually running through the API today |
| 2. Pick a model | choose a self-hosted model for the workload, and test it on your own data |
| 3. Deploy | a server with an OpenAI-compatible API, often just a new endpoint and key |
| 4. Hybrid for the hard cases | rare, complex jobs still go to the cloud, but only after anonymization |
| 5. Run in parallel | the old and new API run side by side until quality is confirmed |
What to Migrate Carefully
- •Prompts and workflows: run them against the local model first, and adjust the ones that need it,
- •Anything built around a feature unique to the cloud model: replace it, or adapt the workflow around a local equivalent,
- •The security layer: set up anonymization for whatever still goes out through the hybrid path,
- •Quality checks: compare answers from the old and new setup on real requests, not test prompts.
The Result
After the migration, you pay for the server once instead of a bill that climbs every month, and sensitive data never leaves the building. Quality on the routine, high-volume tasks holds up, because that’s exactly what the parallel run was there to confirm. We cover the hardware and the full software stack in a separate step-by-step guide to building an AI server, if you want to see what’s actually involved. And if you’d rather we ran the migration for you, that’s what the AI server page is for.
FAQ
Will we have to rewrite everything?
Usually not. Self-hosted servers typically expose an OpenAI-compatible API, so for many integrations you just swap the endpoint and the key. The parts that need real work are the ones built around a feature specific to the cloud model — those get adapted case by case.
What if the local model can’t handle some of our workload?
That’s what a hybrid setup is for: routine work stays on the local model, and the rare, hard cases go to the cloud, but only after the data is anonymized. You don’t lose quality on the tricky top five percent of requests, and you’re not sending everything out just to cover them.
How long does the migration take?
It depends on how many scenarios you’re running, but a basic migration, chat, documents, simple integrations, usually takes 2–4 weeks, with the old API kept running in parallel until the new setup proves itself.