Cloud or On-Premises?

A study released earlier this year by Lenovo indicates that an AI solution deployed on-premises has a cost advantage of up to 8x compared to an implementation in the cloud (IaaS), and up to 18x compared to using cutting-edge LLM models 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 relevant costs associated with the on-premises alternative — such as specialized labor and software licenses — it is clear that, especially in medium- and long-term implementations with high token consumption volumes, 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 AI governance frameworks are beginning to be implemented in various jurisdictions. In this context, proper data management becomes even more strategic — particularly for companies operating in the financial, healthcare, and government sectors, where regulatory requirements regarding privacy and data localization are particularly stringent.

These factors are compounded by technical considerations, such as latency and model capabilities. 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 analytical perspective, enterprise adoption of artificial intelligence is likely to follow a hybrid model. In general, the exploration, prototyping, and testing stages benefit from models available via the cloud: they are easy to implement, cost-accessible, safe for experimentation, and enable rapid results. As these systems and applications mature and move into production environments, the trend is for them to run on on-premises infrastructure, where control, performance, and cost efficiency at scale become decisive factors.

It is no coincidence that we are seeing the emergence of AI platforms designed to orchestrate this hybrid approach. These are vendor-agnostic solutions capable of integrating with any model or provider available on the market. They allow agents to dynamically switch their underlying LLM model according to variables such as cost per token, data volume, or the nature of the task being performed. They also implement a security layer through guardrails across any LLM currently used by the platform — or one that may be incorporated in the future.

The question, therefore, is not whether to choose cloud or on-premises, but rather how to build an AI strategy that takes advantage of the best of both approaches. Companies that understand this dynamic — and invest in the right governance, architecture, and tools to orchestrate it — will be better positioned to scale their AI journey securely, efficiently, and with long-term economic sustainability.