On-Premise LLM Deployment: The Real Costs, Trade-offs & Decision Framework

This guide provides a comprehensive analysis of the considerations involved in deploying large language models (LLMs) on-premise, contrasting them with cloud-based solutions. It highlights the real costs and trade-offs, such as the substantial initial investment, ongoing maintenance, and staffing requirements, which can often outweigh the perceived benefits for many organizations. However, for entities with high, consistent inference volumes, existing infrastructure, and stringent compliance needs, on-premise deployments can offer advantages like lower long-term costs and complete data control, with latency benefits for real-time applications. The guide stresses the importance of a decision framework to evaluate the feasibility of on-premise deployment, considering factors like compliance requirements, volume consistency, latency needs, and existing GPU infrastructure, while also advocating for a potential hybrid approach to leverage both on-premise and cloud benefits effectively. It underscores that while on-premise deployment can lead to significant savings under specific conditions, cloud solutions often offer superior flexibility and access to cutting-edge models, making them suitable for varying demand scenarios and faster market deployment.