Soaring Cloud Costs Drive Enterprises to On-Premise AI

Amidst the rapid adoption of generative AI (GenAI), rising cloud costs are prompting enterprises to consider on-premises deployments as a cost-effective alternative, reports TechTarget.

While organizations have been experimenting with GenAI in the cloud for several years, escalating expenses are pushing CIOs to explore on-premises solutions, experts say.

"It's not uncommon for us to get bills that we review for our members, and they're spending $750,000 or a million dollars or more a month in the cloud," notes John Annand, an analyst at Info-Tech Research Group, to TechTarget.

This shift is driven by the significant cost savings offered by on-premises AI deployments, particularly for large organizations.

"In 2025, enterprises shifting generative AI to production after two years of experimentation will consider on-premises deployment as a cost-effective alternative to the cloud," the report states.

The move toward on-premises AI is also influenced by advancements in hardware and software.

"Better software from startups and packaged infrastructure from vendors such as HPE and Dell make private data centers a way to balance cloud costs," the article explains.

This trend is supported by data from industry reports and hardware makers. In fall 2024, Menlo Ventures found that 47% of companies developed GenAI in-house, while a TechTarget survey showed a significant increase in organizations considering both on-premises and public cloud options for new applications in 2025.

At the same time, HPE and Dell have reported substantial increases in AI system sales, indicating strong demand for on-premises AI solutions.

"Customers are looking for all shapes and sizes of AI-ready or AI-capable servers," says David Schmidt, senior director of Dell's PowerEdge server line, to TechTarget.

The shift to on-premises AI is not limited to cost considerations. Companies with strict data privacy and security requirements, as well as those operating in highly regulated industries, have traditionally favored on-premises solutions.

"What's different is that Fortune 2000 companies will pursue on-premises AI because it offers more cost controls than the cloud," Annand observes.

The article also highlights the emergence of "as-a-service" offerings from Dell and HPE, which provide pay-per-use pricing for AI servers, potentially making on-premises deployments more attractive.

"Organizations are now looking at wanting more predictable costs," notes Tiffany Osias, vice president of global colocation services at Equinix, to TechTarget. "The cost of cloud is so high that infrastructure costs . . . are low enough to where they can get much better economics in purchasing equipment and running it on their own."

The move to on-premises AI is also being facilitated by the growing maturity of software tools for developing in-house GenAI applications. Many startups are offering solutions that can run on-premises hardware, providing companies with more control over their AI development and deployment.

"I would say 80% of what generative AI requires is a push-button, turnkey solution, and that already exists in a series of startups today," states Tim Tully, a partner at Menlo Ventures, to TechTarget. "They're out there selling software, and they're doing fairly decently."

The shift toward on-premises AI suggests a growing desire among enterprises for greater cost control and flexibility in their AI deployments. This trend could reshape the landscape of the AI market, as companies seek to optimize their AI strategies for both cost and efficiency.