Open-Source LLMs vs Proprietary LLMs
Proprietary models (GPT, Claude, Gemini) offer top capability with zero infrastructure — the fastest path to value. Open-source models (Llama, Mistral) win on control, data privacy and cost at very high volume, at the price of running them yourself.
| Criterion | Open-Source LLMs | Proprietary LLMs |
|---|---|---|
| Peak capability | Strong, closing the gap | Frontier |
| Time to start | Setup / hosting needed | Instant API call |
| Data privacy / control | Full — self-hosted | Vendor terms |
| Cost at high volume | Lower — your hardware | Per-token adds up |
| Ops burden | You run inference | None |
| Customisation | Full — weights are yours | Limited |
| No vendor lock-in | Yes | Some |
Choose Open-Source LLMs when
Sensitive data that can't leave your boundary, very high inference volume, deep customisation, or avoiding vendor lock-in.
Choose Proprietary LLMs when
Fastest time to value, frontier capability, low/medium volume, and teams that don't want to run model infrastructure.
The verdict
Start on a proprietary API to ship fast and validate value; move sensitive or high-volume workloads to a self-hosted open model when the economics or privacy case is clear. Lazlo builds both and keeps your architecture portable between them.