Performance, compliance, and control: The on-premises advantage for AI workloads

The cloud has many well-known benefits, most notably limitless on-demand scalability and high reliability, both of which are ideal capabilities for hosting AI workloads. However, according to a recent Business Application Research Center (BARC) report, only 33% of AI workloads are hosted in public clouds. On-premises and hybrid environments almost evenly split the remainder, with on premises having the slimmest of edges (34%).[1]

Certainly, the cloud can be the right choice for some AI workloads. If the enterprise needs to serve users in disparate locations with low latency, the public cloud’s global infrastructure could serve that use case well. Many IT professionals also prefer using hyperscalers’ pre-built AI services and large language models because they eliminate the complexity of model deployment, scaling, and maintenance.

But as many in IT have discovered, there are also many good reasons for keeping AI workloads on premises. For starters, AI workloads are notoriously resource intensive. If a model takes longer than expected or requires multiple iterations to train, cloud-based graphics processing unit pricing, which can run over $100 per hour, can rapidly rack up massive overruns. Likewise, if there is a need to transfer large data sets from the cloud, egress fees can further increase costs, and the time required to move data can extend project timelines. Also, given that AI models require intense compute resources, low network latency can be critical to achieve real-time inference, and shared cloud resources may not provide the level of consistent performance required.

Finally, many AI applications handle sensitive information, such as trade secrets or personally identifiable information that falls under strict regulations governing the data’s use, security, and location. Ensuring the required level of compliance and security may be difficult in a public cloud, due to the lack of control over the underlying infrastructure.

“Market dynamics are increasing buyer interest in on-premises solutions,” says Sumeet Arora, Teradata’s chief product officer.

Of course, building out an AI-ready infrastructure on premises is no simple task, either. An on-premises solution gives IT complete control over compliance and security, but these tasks remain challenging, especially when doing custom integrations with multiple tools. Additionally, on-premises solutions need to maintain a complex infrastructure, with the power, speed, and flexibility to support the high demands of AI workloads.

Luckily, the market has matured to the point where tightly integrated, ready-to-run AI stacks are now available, which eliminates complexity while enabling compliance, security, and high performance. A good example of just such a pre-integrated stack is Teradata’s AI Factory, which expands Teradata’s AI capabilities from the cloud to make them available on premises.

“Teradata remains the clear leader in this environment, with proven foundations in what makes AI meaningful and trustworthy: top-notch speed, predictable cost, and integration with the golden data record,” Arora continues. “Teradata AI Factory builds on these strengths in a single solution for organizations using on-prem infrastructure to gain control, meet sovereignty needs, and accelerate AI ROI.” 

This solution provides seamless integration of hardware and software, removing the need for custom setups and integrations. And, because it’s all pre-integrated, users won’t have to gain multiple layers of approval for different tool sets. As a result, organizations can scale AI initiatives faster and reduce operational complexity.

Many practitioners prefer on-premises solutions to build native retrieval-augmented generation (RAG) use cases and pipelines. Teradata AI Microservices with NVIDIA delivers native RAG capabilities for ingestion and retrieval, integrating, embedding, reranking, and guardrails. Users can query in natural language across all data, delivering faster, more intelligent insights at scale. This comprehensive solution enables scalable and secure AI execution within the enterprise’s own datacenter.

While cloud provides scalability, global access, and infrastructure on-demand for AI workloads, many organizations may prefer on-premises solutions for better cost control, security compliance, and performance consistency. Integrated AI stacks can make on-premises deployment a much simpler task while accelerating time to value.

Learn more about how Teradata’s AI Factory can help your organization with on-premises deployment.

[1] Petrie, K,  Cloud, On Prem, Hybrid, Oh My! Where AI Adopters Host their Projects and Why, Datalere, April 3, 2025.