Open-Source vs Proprietary LLMs: Strategic Trade-offs for Enterprises

Open-Source vs Proprietary LLMs: Strategic Trade-offs for Enterprises At some point in every serious AI discussion, this question comes up: Do we build on open-source models, or do we rely on proprietary LLM APIs? This isn’t a philosophical debate about openness. It’s a strategic infrastructure decision. And like most infrastructure decisions, the right answer depends on constraints, not trends. Enterprises evaluating generative AI trends often focus first on performance benchmarks. That’s understandable. But raw capability is rarely the deciding factor. The real differences show up in control, cost structure, compliance posture, and long-term dependency risk. Let’s unpack the trade-offs in practical terms. What “Proprietary” Actually Means in Practice When enterprises adopt proprietary LLMs, they typically consume them as managed APIs. The provider handles: Model training Infrastructure Scaling GPU management Upgrades Optimization From an engineering standpoint, integration is straigh