Comparison1 min read
Open-Source LLMs vs APIs: Cost, Privacy, Quality
"We’ll just run our own model" sounds cheaper and more private. Sometimes it is. Often the GPU bill and the operations work say otherwise.
TrueCodeAI Engineering
Agents, Voice & ML practice
- Published
LLMInfrastructureComparison
Side by side
| Self-hosted open weights | Hosted API | |
|---|---|---|
| Cost at low volume | High — GPUs idle | Low — pay per token |
| Cost at very high volume | Can be lower | Scales linearly |
| Top-end quality | Behind the frontier on hard tasks | Frontier models available |
| Data control | Full | Governed by provider terms and region |
| Operations | You run GPUs, scaling, updates | None |
When self-hosting makes sense
- Very high, steady volume of a narrow task a smaller model handles well.
- Strict requirements that data never leaves your infrastructure.
- Offline or edge deployment.
- A fine-tuned small model that replaces a large general one.
When APIs make sense
- Most business applications, especially early.
- Tasks that need strong reasoning or tool use.
- Variable or unpredictable traffic.
- Teams without GPU operations experience.
Many providers also offer enterprise terms, regional hosting and no-training guarantees, which answer most privacy concerns without self-hosting.
Frequently asked questions
Can we start with an API and move later?
Yes. Keep the model behind an interface and an eval suite, and switching is a measured change.
Are open-weight models safe for business?
Yes, with the same guardrails as any model. Check the licence terms for commercial use.
What about hybrid?
Common and sensible: a small self-hosted model for high-volume simple tasks, an API model for the hard ones.
