There is no single right way to deploy a large language model in the enterprise. The decision hinges on three questions: what kind of data will the model touch, which regulations govern that data, and how much infrastructure your organization is willing to own. A customer-support chatbot fed with public product documentation carries a very different risk profile than a document summarization system ingesting patient records or privileged legal communications. Treating those workloads identically is the most common planning failure we see, and it usually ends in either unnecessary GPU spend or a compliance exposure discovered after deployment.
Introduction: Navigating LLM Deployment Choices for the Enterprise
Enterprises today choose among three broad architectures. A SaaS deployment routes prompts and data through a provider's shared cloud infrastructure. A self-hosted deployment — on-premises or in a private cloud — keeps inference inside infrastructure the enterprise controls. A hybrid approach splits workloads across both, typically sending low-sensitivity requests to a shared service while keeping regulated data local.
Writing for Airia, Cristina Peterson argues that the choice should be deliberate rather than default: "The solution isn't to avoid the cloud. It's to choose your deployment architecture deliberately." Her framing is useful because it resists the two reflexes enterprises fall into — reflexively reaching for a SaaS API because it is fast to integrate, or reflexively demanding on-premises because "security" has become a synonym for control. Neither reflex maps to actual obligations.
The right unit of analysis is the use case, not the organization. A single enterprise will typically run several LLM workloads with different data sensitivities, and the evidence supports mapping each to the deployment model that fits it. That mapping — use case to architecture to security controls to compliance exposure to hardware cost — is the framework this article delivers.
