Open-Source vs Proprietary LLMs

Proprietary LLMs lead on capability; open-source models offer control and privacy. Here are the real trade-offs, and how to choose—often both.

The trade-off

Factor	Proprietary (API)	Open-Source (self-host)
Capability	Often leads	Closing the gap
Ease to start	Easiest	Needs setup
Data control	Sent to provider	Full control
Cost	Per request	Infra + can be cheaper at scale
Maintenance	Provider handles	You handle

Both are large language models; the difference is where they run and who controls them.

When open-source wins

Choose open-source when data control, privacy, or residency requires keeping data in-house, or when cost-at-scale favors self-hosting and you have the expertise. See private LLM vs public API and cloud vs on-premise AI.

When proprietary wins

Choose proprietary for speed and top capability—easiest to start, best on the hardest tasks, no infrastructure to run. Ideal for MVPs and most applications.

Evaluate on your use case

Don't assume either is better—evaluate on your actual task. Open-source has closed much of the gap for many use cases; the top proprietary models still lead on the hardest.

Often the answer is both

Many teams use both, matching the model to each task—a strong model for hard tasks, a cheaper or self-hosted one for simple, high-volume, or sensitive work.

Why FISTA

FISTA Solutions builds the right model strategy—proprietary, open-source, or a mix—based on your capability, privacy, and cost needs, through AI enablement, backed by 150+ projects across 12+ countries.

Choosing your LLM strategy? Talk to FISTA.