Factors To Consider in Cloud-Based vs. On-Premises AI

When organizations begin to scale artificial intelligence initiatives, the economics of token consumption quickly come to the forefront. At first glance, the trade-off may appear simple: Continue paying for AI token use or invest in on-premises AI infrastructure. But this decision involves several considerations — not only cost but also data center capacity, data governance and ease of management.

To start, let’s look at what contributes to AI “token sprawl.” Unlike a typical human-to-AI conversation with generative AI, agentic AI involves agents carrying out multistep tasks, often with limited human intervention. Those tasks could include continuously summarizing a user’s emails, analyzing a finance portfolio or performing medical coding. For individual consumers, AI applications have built-in limits that can be extended based on subscription levels or when prompted to purchase additional tokens. For enterprise customers, agents can potentially work indefinitely without proper guardrails in place, which can quickly run up a hefty bill.

The need to control that cost is often what prompts organizations to ask whether they want to continue relying on third-party providers, bring some AI workloads on-premises or use both approaches. On one hand, this is a math exercise: How many tokens do we expect to consume? How many tokens do we need to generate, and what technology investments do we need to generate that amount? Ultimately, though, organizations need to understand what they are paying for each useful business outcome — not just each token. But as we’ll see, cost is just one factor to consider.

An organization might also bring AI workloads on-premises because they don’t want to share data with third-party providers. Organizations must carefully review how third-party providers handle submitted data, including whether it may be retained, used for service improvement or included in model training. This can be a concern, especially for organizations in regulated industries.

Organizations may decide that it makes sense to invest in a small data center environment, assuming they have access to sufficient power, cooling and physical space. They can make that investment little by little, refreshing in phases as budgets allow. If an organization lacks the physical space to build out its data center, it may look to more powerful, high-efficiency graphics processing units, but these are more expensive. An alternative is a data center colocation provider that houses and manages the environment, or a provider that rents out both the space and equipment.

For some organizations, especially those with predictable, high-volume or sensitive workloads, standing up AI capabilities in their own data center makes sense. That raises a different set of questions, including whether the organization has the expertise on staff to effectively manage, govern and secure AI.

At the end of the day, the hardware component may be the simplest piece to address. Organizations that want to manage AI in-house need the right people and processes to bring the pieces together effectively, develop the models and ensure they deliver value.

Often, a “crawl, walk, run” approach is the best way to scale AI capabilities. Small and midsize businesses can avoid making data center investments out of the gate and instead run some workloads on high-performance AI PCs or local workstations.

A shared pool of compute that lives across high-capacity workstations would take pressure off the data center. A smaller rack could support a small business for quite some time, until the technology advances and it’s eventually time for a refresh or a larger investment. As the business grows and AI workloads expand, they can use colocation providers or partners to rent dedicated hardware space and scale up incrementally.

With multiple ways to reach the desired AI destination, organizations can make the best decisions when they determine what they can afford and what their priorities are — cost, ease of adoption, privacy or something else. Answering these questions and understanding the pros and cons of each option can guide organizations toward the best strategy for their AI workloads.