Private LLM vs Public API: How to Choose

"Should we use OpenAI's API or run our own model?" is one of the most common—and most consequential—AI architecture decisions. Here is how to choose without guessing.

The core trade-off

Public APIs give you the frontier of capability with no infrastructure—but your data goes to a third party and you pay per token. Private/self-hosted LLMs keep data in your environment and can be cheaper at scale—but you run the infrastructure and may trail the frontier on raw capability. This is a foundational AI enablement decision.

The decision factors

Factor	Favors public API	Favors private LLM
Data sensitivity	Low	High / regulated
Volume	Low or spiky	High and steady
Capability need	Frontier	Good-enough is fine
Infra appetite	None	You can run it
Latency/control	Acceptable	Must control

Data and compliance usually decide

For sensitive or regulated data, keeping it in your environment often settles the question—see enterprise AI data readiness and data security. But note: a private model is not automatically secure; security is engineered through access control, monitoring, and patching.

Cost is about volume

At high, steady volume, per-token API costs add up and private can win. At low or spiky volume, APIs are cheaper because you pay only for use. Model this against your real usage—see AI total cost of ownership.

The hybrid answer

Many teams run a hybrid: sensitive workloads on a private model, everything else on APIs, routed by data classification and task. It captures control where it matters and capability where it doesn't.

Why FISTA

FISTA Solutions architects the right LLM deployment—public, private, or hybrid—matched to your data, volume, and compliance needs. Explore AI enablement, backed by 150+ projects across 12+ countries.

Choosing an LLM architecture? Talk to FISTA.