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Enterprise AI Scalability and Cost Pressures
Enterprises are confronting rising token costs from agentic AI workloads, driving renewed interest in on-premises infrastructure and open-weight models, while governance frameworks struggle to keep pace with always-on autonomous agents.
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Enterprise AI scalability is hitting budget constraints as agentic workloads multiply and cloud token bills consume allocations faster than planned, prompting companies to reconsider where inference should run. This cost pressure is driving renewed interest in on-premises infrastructure and open-weight models as alternatives to cloud-based inference, discussed at Dell Technologies' AI Leadership Symposium (SiliconANGLE, October 12). Relatedly, autonomous AI agents running continuously across sprawling platforms are straining governance frameworks originally designed for deterministic software, with cost and control emerging as recurring themes among enterprise AI deployers (SiliconANGLE, October 12).