Three Ways of Using LLMs in Business

Commercial teams today have three main ways to use large language models (LLMs): chat interfaces, cloud APIs, and running open‑source models directly. Each path trades off speed, control, and cost.​

1. Chat Web Interfaces

Chat interfaces (like consumer chatbots) are the easiest entry point. They are immediately available in the browser, need no engineering work, and are great for experimentation, ideation, and individual productivity. The downside is that they are generic: limited integration with your internal systems, no deep customization, and workflows often depend on copy‑paste rather than automation. This makes them useful for people, but weak as the core of a scalable product or internal platform.​

2. Cloud APIs

Cloud APIs expose LLMs via endpoints you can call from your own apps and workflows. This offers fast time‑to‑market: you can prototype or ship AI features quickly without training or hosting models yourself. However, there are constraints: API costs add up as volume grows, specialization is limited to prompt engineering and light fine‑tuning, and you must trust the provider’s data handling and regional compliance posture, which can be a concern in regulated industries.​

3. Direct Inference of Open‑Source Models

Running open‑source models yourself gives you maximum control. You can keep data fully in‑house, apply domain‑specific fine‑tuning, and design proprietary behavior that is hard to replicate elsewhere. The trade‑off is that this route is expensive and slower: it requires infrastructure, MLOps, and ongoing maintenance, all of which increase time to market. For many teams, this only makes sense once there is a validated business case and clear need for strong data‑security guarantees or deep specialization.​

Choosing the Right Path

For most businesses, the journey starts with chat interfaces, matures into API‑based automation, and only later justifies self‑hosted, open‑source models. The right choice depends on your current constraints: speed and experimentation, unit economics at scale, or strict control and security. Understanding these three options helps you design an AI strategy that matches where your company is today—and where it aims to go next.