The market for large language models (LLMs) is undergoing a transformation, with a drastic reduction in API prices. Models like K2.5, Deepseek, and Gemini offer competitive rates and, in some cases, free usage tiers.

The On-Premise Dilemma

This scenario challenges the cost-effectiveness of managing on-premise infrastructure for running LLMs. While data privacy remains an unassailable argument in favor of on-premise solutions, other traditional advantages, such as the absence of usage limits and the supposed free operation after hardware costs are amortized, appear less clear.

Comparing Costs and Benefits

The hardware required to run large models locally, such as a high-end GPU (e.g., RTX 3090), has a significant cost, in addition to energy consumption and the time required for configuration and optimization. Faced with increasingly cheaper APIs, the return on investment (ROI) of an on-premise solution may require processing millions of tokens.

Latency and Customization: The Real Advantages?

Beyond privacy, the main arguments in favor of on-premise solutions remain latency control and the ability to customize models for specific domains. However, these are needs that concern a limited subset of applications. For those evaluating on-premise deployments, AI-RADAR offers analytical frameworks on /llm-onpremise to assess trade-offs.

Final Considerations

The choice between APIs and on-premise depends on a careful evaluation of the specific needs, costs, and benefits of each option. The collapse in API prices requires a reconsideration of traditional deployment models.