Choosing how to host an LLM
Managed API, serverless GPU or your own GPU box? A plain-English way to pick the right hosting model for your project.
There are three broad ways to run a large language model in production. Each trades convenience for control in a different way.
1. A managed API
You send prompts to someone else's endpoint and pay per token. Best for getting started, spiky traffic and teams without ML infrastructure experience.
2. Serverless GPUs
You bring the model and serving code, the platform runs it on GPUs that scale up and down. Best for custom or fine-tuned models with uneven traffic.
3. Your own GPUs
You rent or own GPU machines and run the whole stack. Best for steady, high volume, strict data control, or squeezing out the lowest cost per token.
A quick decision checklist
- Do you need a specific fine-tuned model? If yes, rule out curated APIs.
- Is traffic steady around the clock? Dedicated GPUs start to win on cost.
- Do you have someone to own the infrastructure? If not, stay managed.
- Are there data residency rules? Check which regions each host offers.