As enterprise AI moves from experimentation to production, infrastructure decisions are becoming business decisions. Organisations are no longer evaluating AI solely on model performance; cost efficiency, latency, data governance, security, and compliance are becoming equally important to scaling AI reliably.
In an interaction with CXOToday, Vinay Chhabra, Co-Founder & MD, AceCloud, shared his perspective on how enterprises in India are rethinking AI infrastructure as they move from pilots to production-scale deployments.
“The biggest shift is that AI is moving from a technology project to an operating capability.”
He discussed how enterprises are looking beyond models to build the right infrastructure around their AI workloads, with inference economics, data architecture, compute, networking, security, and governance becoming critical to long-term scalability. He also highlighted the growing role of smaller, specialized models and the importance of bringing AI closer to the data to improve latency and operational efficiency.
The interaction also explores how evolving data protection requirements are influencing cloud strategies, driving greater interest in hybrid and sovereign infrastructure. Vinay shared why enterprises, particularly in regulated sectors, are increasingly looking for greater control over where data is stored and processed, while retaining the flexibility of hybrid environments.
Looking ahead, Chhabra also shared his views on the growing demand for sovereign AI infrastructure, including AI inference, model hosting, and AI-led voice applications, and why the ability to balance performance, cost, control, and compliance will become increasingly important as AI becomes embedded in business-critical workflows.