Self-hosted LLMs vs API models
Run open-weight models on your own infrastructure, or call hosted frontier APIs? The answer is driven by data sensitivity, scale economics, and how close to frontier quality you need to be.
At a glance
Self-hosted
- Data residency
- Stays in your environment
- Quality ceiling
- Open-weight frontier (high, not top)
- Cost shape
- Fixed infra + ops
API models
- Data residency
- Leaves your environment
- Quality ceiling
- Top frontier models
- Cost shape
- Per-token, scales with usage
Full comparison
| Self-hosted | API models | |
|---|---|---|
| Data residency | Stays in your environment | Leaves your environment |
| Quality ceiling | Open-weight frontier (high, not top) | Top frontier models |
| Cost shape | Fixed infra + ops | Per-token, scales with usage |
| Ops burden | You run inference + scaling | None — provider handles it |
| Best for | Regulated data, very high volume | Fastest path, top quality |
Which should you choose?
Use API models for the fastest path and the highest quality. Self-host when data residency or regulation requires it, or when your volume is high enough that fixed infrastructure beats per-token pricing.
Frequently asked questions
What's the difference between Self-hosted and API models?
Run open-weight models on your own infrastructure, or call hosted frontier APIs? The answer is driven by data sensitivity, scale economics, and how close to frontier quality you need to be.
Which should I choose, Self-hosted or API models?
Use API models for the fastest path and the highest quality. Self-host when data residency or regulation requires it, or when your volume is high enough that fixed infrastructure beats per-token pricing.