Artificial Intelligence (AI) is at a pivotal moment, as more businesses realize that the best place for their AI operations might not be the cloud, but on their own premises.
The choice between on-premises AI––popularly known as private AI––and a cloud-based approach is now less about “if” and more about “when,” as companies recognize the benefits of a private AI infrastructure. Unlike a single product or vendor-driven solution, private AI is an architectural strategy—a way of thinking—that brings substantial advantages in cost, control, and flexibility.
But let’s understand private AI for what it really is: not a one-size-fits-all product, but an architectural approach that optimizes an AI environment for an organization’s specific needs. Indeed, private AI isn’t a particular model or technology—it’s a strategy. It allows organizations to bring AI models to where their data lives instead of moving their data to the model, creating a powerful blend of efficiency, control, and compliance.
Why is this approach valuable? For many companies, their data is core to their business, and they need full control over where it’s stored and how it’s used. Moving it to the cloud may raise privacy, compliance, and even security concerns. By keeping AI on-premises, however, businesses get to keep their data where it’s most protected and under their control.
Cost Advantages of AI On-Premises
One of the biggest advantages of private AI is its cost efficiency, which has made this approach a real standout. In the cloud, every AI interaction is metered and billed as tokens. This pay-as-you-go model might work for some scenarios, but for AI workloads, it creates an unpredictable cost structure that doesn’t always scale well. That can lead to budget challenges and the need to enforce hard caps on usage, which can then limit the value of an AI service. Imagine having to turn off usage two weeks into a month because of hitting a cost ceiling.
When customers deploy private AI, they benefit from sharing GPU, network, and memory resources across applications. This kind of resource-sharing model offers a far more predictable and efficient cost structure, saving businesses from skyrocketing monthly bills. A consistent, predictable infrastructure cost allows organizations to better forecast AI spend and allocate resources where they’re truly needed.
Our customers tell us that running their AI services on-premises has turned out to be anywhere from a third to one-fifth of the cost of cloud-based options. With an on-premises strategy, any optimization to the infrastructure directly benefits the company’s bottom line, not a cloud provider’s margins. This level of ownership and control over infrastructure savings is a compelling argument for private AI.
Control Over the Full Stack
Beyond cost, private AI enables a level of operational control that cloud-based solutions simply can’t match.
Cloud providers offer a broad suite of services, but they’re often locked into a specific ecosystem, limiting an organization’s choices for hardware, models, and tools. With private AI, organizations aren’t bound by a single vendor’s roadmap. They can choose the best hardware for each workload, experiment with different models, and evolve their environment to meet their specific demands.
Take, for example, AI workloads in industries like finance, government, or healthcare. These sectors are under heavy regulatory scrutiny and require rigorous data governance. Private AI allows these organizations to run AI models where their data is already compliant and secure, avoiding the potential risks and costs associated with moving sensitive data off-premises.
When the model is close to the data, there’s no need to restructure or reconfigure that data to fit a third-party platform—a major advantage that allows organizations to deploy faster and more securely.
Measurable Business Value with Private AI
Private AI can also be a powerful tool for CXOs who are looking to maximize AI investments without getting swept up in the hype.
In many organizations, there’s pressure to implement AI quickly, but there’s a real risk of pursuing short-term wins without considering long-term business value. One of the most effective ways to show immediate returns with AI on-premises is through measurable use cases where business impact is clear.
In customer service, for instance, a company can measure the volume of cases closed per agent both before and after deploying an AI solution. These efficiency gains, sometimes in the range of 10% or more, are valuable, practical ways to demonstrate ROI.
Private AI also helps businesses stay focused on measurable outcomes rather than AI for the sake of AI. It enables CXOs to lead with pragmatism, choosing use cases that bring immediate value. Take information retrieval as another example: A police department using AI to cross-reference cold case files can see weeks or months’ worth of human detective work condensed into hours with the help of an on-premises AI-powered chatbot that ingests, organizes, and provides rapid access to complex case information.
Avoiding the Pitfalls of Technical Debt
When rolling out AI, adopting a platform-based approach is crucial. Companies that lock themselves into proprietary cloud ecosystems or vendor-specific solutions often face significant challenges down the line. Proprietary solutions may appear faster or easier initially, but they can create a “technical debt trap.” This happens when businesses can’t pivot to better models or technologies because their AI stack is tied to a specific vendor’s AI silo.
By taking a modular, platform-based approach to AI on-premises, organizations are well-positioned to evolve as new models and technologies emerge. This platform flexibility is critical in an industry that’s moving as fast as AI is today. Instead of being saddled with outdated technology, a platform-based approach allows organizations to adopt the latest models, ensuring that their AI remains competitive and responsive to change. Imagine finishing rolling out a new AI service and in a matter of weeks having buyer’s remorse because something faster and more accurate was just released by another vendor or in open source. With a platform approach, you can quickly pivot to the latest and greatest at the speed of software.
With private AI, it’s also much easier to manage the full stack, from hardware to applications, giving companies the flexibility to innovate at every layer. With distributed resource scheduling (DRS), organizations can dynamically adjust how infrastructure resources are allocated to meet shifting AI workload demands. This approach allows you to ensure full utilization of your AI infrastructure investment, even as demand across your AI service portfolio fluctuates. .
Best Practices for Expanding Private AI Initiatives
For organizations starting with on-premises AI, it’s often wise to begin with a specific, back-office use case where success can be closely measured. From there, an organization can scale the AI initiative to include more complex or public-facing use cases. Starting with an internal application helps teams establish a baseline of how AI can support human workers—whether it’s enhancing decision-making, accelerating research, or handling repetitive tasks.
Segmentation of data is also crucial in private AI. By assigning access controls and audit trails, organizations can keep their models secure and efficient. Access controls and thoughtful data architecture are key to maintaining compliance without the need for multiple models or redundant deployments that can drive up costs.
As the organization builds on its private AI infrastructure, a strategic, modular approach ensures that they’re prepared for future changes. With each iteration, they can expand functionality, test new models, and refine operational efficiencies. Each success helps build internal credibility and sets the foundation for a sustainable, enterprise-wide AI strategy.
A Pragmatic Approach for Long-Term Success
In a crowded AI landscape full of “all-in-one” promises, AI on-premises offers a balanced, practical approach that emphasizes measurable value, cost control, and operational flexibility. Private AI focuses on what matters most to businesses—solving real problems, protecting sensitive data, and retaining control over AI investments.
For organizations mindful of costs, concerned about data control, or cautious about AI hype, on-premises AI presents a practical way forward. It allows them to deploy powerful technology strategically, expand it gradually, and ultimately leverage it to create sustainable, long-term business value.
About Chris Wolf
Broadcom
Chris leads AI and Advanced Services for Broadcom’s VMware Cloud Foundation division. He is responsible for AI strategy, research, architecture, and development of VCF’s AI IaaS services.
