Why AI Adoption Will Be Hybrid
A study released earlier this year by Lenovo indicates that an AI solution deployed on-premises offers a cost advantage of up to 8x compared to a cloud-based implementation (IaaS), and up to 18x compared to the use of state-of-the-art LLMs accessible via API.
Although there have recently been price reductions of more than 50% per token for some LLM models offered by Anthropic and OpenAI, and although Lenovo’s study omits significant costs associated with the on-premises alternative—such as skilled labor and software licenses— it is clear that, especially in medium- and long-term deployments with high token consumption, a hybrid AI approach should be seriously considered.
Furthermore, cost is not the only relevant variable in this equation. Data sovereignty represents another critical factor to consider, especially at a time when the use of artificial intelligence is receiving increasing attention from government institutions around the world and when AI governance frameworks are beginning to be implemented in various jurisdictions. In this context, proper data management becomes even more strategic—especially for companies operating in the financial, healthcare, and government sectors, where regulatory requirements regarding privacy and data localization are particularly stringent.
In addition to these factors, there are technical considerations, such as latency and model capacity. Certain workloads—especially those involving large volumes of data—may not be suitable for cloud processing, either due to performance constraints or the required response time, making on-premises infrastructure the most appropriate option. On the other hand, since models made available via API tend to incorporate the latest innovations, more complex and sophisticated tasks may necessarily require the use of the cloud.
From any perspective, the adoption of artificial intelligence in businesses tends to follow a hybrid model. In general, the exploration, prototyping, and testing phases benefit from cloud-based models: they are easy to implement, cost-effective, safe for experimentation, and deliver quick results. As these systems and applications mature and migrate to production environments, they tend to run on on-premises infrastructure, where control, performance, and large-scale cost efficiency become key factors.
It is no coincidence that we are seeing the emergence of AI platforms designed to orchestrate this hybrid approach. These are agnostic solutions capable of integrating with any model or provider available on the market. They allow agents to dynamically switch their underlying LLM model based on variables such as cost per token, data volume, or the nature of the task being performed, and they implement a security layer via guardrails over any LLM currently used by the platform—or that may be incorporated in the future.
The issue, therefore, is not choosing between the cloud and on-premises, but rather building an AI strategy that leverages the best of each approach. Companies that understand this dynamic—and invest in the governance, architecture, and right tools to orchestrate it—will be better positioned to scale their AI journey safely, efficiently, and with long-term economic sustainability.