Why Healthcare AI Needs Distributed Infrastructure 

Artificial intelligence is redefining the structure and efficiency of modern healthcare. Providers are implementing AI across manifold use cases, including shortening waiting times, delivering more accurate diagnoses, personalizing care experiences, and reducing the administrative burden on doctors, nurses and medical staff. Nearly 80% of healthcare organizations were using AI just two years ago, and the pressure to adopt has only grown. 

Is healthcare ready for such broad-scale automation today? The needs and investments are there, but the infrastructure to support them may not be. 

Healthcare organizations recognize that AI maturity and AI readiness don’t always happen at the same time. While AI has a comparatively long history in medicine – the first artificial medical consultant was created in 1971 – the industry is taking a slow and steady approach to operationalizing today’s AI capabilities. Clinics and hospitals are high-stakes environments; they need assurance that they’re building the right systems to enable reliable AI performance. That means making scalable investments that are hardwired for sustainable growth, evolving in line with patients’ needs and business priorities without driving up costs. 

All this points to infrastructure as the functional core of AI for the healthcare enterprise. Proper infrastructure not only supports increasing model complexity and usage, but ensures consistent system performance, constant uptime, and critical protection for their most sensitive data. However, it has become increasingly difficult to achieve all these outcomes with a single cloud provider. To advance their AI initiatives – and therefore, advance the quality of care – it’s time for healthcare providers to break away from the single-cloud status quo and explore the advantages of distributed AI infrastructure. 

The Impact of Inference

Gartner expects over 60% of AI projects will fail to deliver and will be abandoned in 2026, largely due to an infrastructure gap that prevents real-time AI inference. 

Inference is the process of running an AI model. For example, if a doctor prompts an approved large language model (LLM) for an AI-generated summary of a patient’s chart, inference happens when that LLM is processing the input and generating the summary.

Inference is a highly compute-intensive process: it takes a considerable amount of energy and extremely powerful hardware to process the prompt and generate the output. As healthcare organizations adopt more advanced AI workflows and models, the inference workload increases, requiring heftier infrastructure to meet inference demand. 

Naturally, this gets expensive. The economic cost of inference is one of the primary obstacles to scaling AI initiatives in healthcare, but instant inference is necessary to implement AI responsibly. In a clinical situation, a mismatch between a model’s compute demands and the infrastructure it runs on can delay the delivery of critical insights, potentially jeopardizing the quality of care. 

Fortunately, the cloud services market is responding. The economization of infrastructure in line with more efficient inference is a critical priority for cloud services providers, data center owners and hardware developers alike. 

Healthcare organizations are also taking action to manage their inference loads, turning to open-source models as a framework for their AI initiatives. This offers greater transparency into the compute resources needed to run the models, as well as their data needs. By distributing their AI workloads across a network of cloud providers, healthcare organizations gain access to additional open-source programs and models, further bolstering efficiency, reliability and transparency. 

Where Legacy Infrastructure Lags 

Traditionally, healthcare organizations have relied on a single vendor – typically a hyperscaler– to host and manage their cloud operations. However, as healthcare AI continues to mature, organizations are finding their current resources are inadequate to support real-time inferencing and model management. Additionally, they may need more robust data storage options, especially as they collect more patient data and expand AI to new operations functions. Conversely, hyperscalers may offer too many features for a healthcare organization’s AI initiatives, resulting in unnecessary costs and confusion for IT teams. 

Instead of leaning on a single vendor, healthcare organizations can instead distribute their cloud workloads across a mix of smaller alternative providers, known as neoclouds, in addition to hyperscalers and their own internal data centers. This allows healthcare AI tools to operate more efficiently in line with their direct compute demands, as well as compliance and security requirements. Working with different providers may also come with helpful additions to their overall software stack, unlocking greater efficiencies while lowering costs. When today’s healthcare providers are under immense pressure to do more with less, multi-cloud solutions can help them to stretch their computing budgets without sacrificing agility or security. 

Even where legacy infrastructure meets the mark on capacity, it often falls behind in operational speed, especially if an organization has an older contract running on soon-to-be-obsolete hardware. Healthcare AI depends on the availability of low-latency infrastructure: the time it takes for data to move from one point to another can quite literally be life or death in an acute care situation. Organizations know this – it’s a key rationale for their deliberate, results-driven approach to AI implementation. AI isn’t running the operating theater (yet), but it is reshaping how medical professionals do their jobs, and as these tools are integrated into everyday clinical practice, they cannot fail when providers and patients need them most. 

In fact, reliability is emerging as one of the most critical challenges to operationalizing AI in healthcare. A new analysis of hundreds of healthcare AI tools reveals that while accuracy is improving, the tools struggle with reliability. As Axios reported, the reliability issue only worsened as the tools were integrated as part of a broader system. When interoperability is fundamental to scaling healthcare AI, gaps in performance will inevitably stall innovation. 

Reliability, like accuracy, is an infrastructure issue. Not only do healthcare organizations need sufficient compute power to run their AI models, but also to regularly test, train and retrain them. A multi-cloud approach gives healthcare providers the flexibility to ensure performance without overextending their resources. More agile infrastructure also promotes easier integration with existing IT systems, making data retrieval and analysis seamless.

This will be exceedingly crucial as agentic AI becomes commonplace – and the healthcare sector, with its comparative AI maturity, is primed to introduce agentic AI sooner rather than later. AI agents are incredibly compute-intensive, but many are also highly specialized, performing only a single task with a hyper-focused training dataset. Flexible, multi-cloud infrastructure gives organizations the ability to scale agentic capabilities sustainably. 

Ensuring Sovereignty and Security in a Multi-Cloud Environment 

Healthcare organizations manage some of our most sensitive personal data. Allowing AI tools to access that data enable deeper personalization and predictive insights that elevate the quality of care. However, patients need ironclad guarantees that their medical records and PII won’t be compromised or misused. Organizations need those same guarantees to ensure compliance with HIPAA and other regulations governing healthcare data. Otherwise, they may face serious legal consequences: protecting their patients means protecting themselves. 

Single-provider infrastructure, even with a hyperscaler, can run the risk of uncontrolled data replication. Personal data copied onto another server might not have the same safeguards. 

For healthcare providers operating across multiple jurisdictions, there are additional complexities to consider. A parent organization that manages urgent care practices across several states, or a health system in a major metro with satellites outside the city limits (or even in different states), are often subject to different data compliance strictures at each location. In other cases, a patient’s medical information has to travel across national borders, further complicating 

Sovereign clouds, which host data locally within state or national borders, give healthcare companies the added security they need to ensure data privacy compliance is built-in, and not an auxiliary function for their IT teams to manage. 

Data sovereignty also helps healthcare organizations to navigate evolving AI laws. In the U.S., regulation around healthcare in AI is more prevalent at the state than federal level: 33 new healthcare AI laws went into effect in 2025, across 21 different states. Many involve specific use cases – such as restrictions on AI for the delivery of mental healthcare – that could influence how data is collected, used and stored. 

As regulations continue to evolve, sovereign clouds ensure that innovation doesn’t come at the cost of security and compliance. 

Conclusion: Breaking the Compute Wall

Infrastructure should not be the barrier to AI innovation in healthcare. AI tools have the potential to quite literally save lives, but they cannot reach that potential without the infrastructure to support accuracy, reliability and security of training data. As healthcare providers reassess their various IT contracts and plan expansions for the years to come, distributing their cloud services across a strategic mosaic of infrastructure will enable healthcare organizations to convert AI maturity to AI transformation.