In collaboration with GAI Insights, a new report from Inflection AI challenges conventional wisdom about AI deployment, revealing significant cost, security, and strategic advantages for enterprises that bring AI inference in-house. The study argues that relying solely on cloud-based AI inference can lead to inflated costs, intellectual property vulnerabilities, and vendor lock-in. In addition, on-premise solutions offer greater control and long-term benefits.
“As businesses increasingly rely on generative AI to drive innovation, the ability to manage infrastructure in-house becomes a strategic imperative,” said Ted Shelton, COO of Inflection AI. “On-premise AI capabilities empower organizations to control costs, safeguard intellectual property, and innovate at their own pace, ensuring they stay ahead in a rapidly evolving landscape.”
The report predicts a dramatic surge in computing costs driven by generative AI workloads, highlighting the critical importance of AI infrastructure decisions. While acknowledging the productivity gains enabled by AI – citing examples of substantial cost savings and increased efficiency in call centres and insurance companies – the study demonstrates that on-premise inference consistently outperforms cloud-based alternatives in cost.
For example, large call centres could also save hundreds of thousands of dollars annually by deploying AI models in-house.
The legal risks
Beyond cost, the report reveals growing legal and IP risks associated with cloud-hosted models. The need to unencrypt data for model use creates vulnerabilities for proprietary information, and the ongoing wave of copyright lawsuits against major LLMs poses potential legal challenges for companies using these models.
On-premise deployments offer greater control over sensitive data, mitigating IP risks and ensuring compliance.