On-demand webinars

The AI trilemma: What breaks when AI moves into production

AI rarely breaks in the lab. It breaks when it has to scale.

Moving AI from a successful pilot to production introduces a different set of challenges: infrastructure costs grow faster than expected, model quality can shift without warning, data and regulatory requirements become harder to manage, and engineering teams spend more time managing infrastructure than building AI. 

In this session, we unpacked what really changes when AI moves into production — from the hidden costs of compute, storage and infrastructure to the operational challenges of availability, integration, data residency and governance. We also looked at the trade-offs between public cloud, on-premises infrastructure and emerging AI infrastructure models, and where each approach works — and where it starts to break.

In this webinar, you’ll learn

• Why AI costs often grow disproportionately at production scale, and what happens when compute, storage, networking and workload growth start compounding. 
• What causes production AI systems to degrade, including model quality shifts, infrastructure constraints and the visibility gaps that make issues difficult to detect and manage. 
• Why infrastructure availability and integration become bottlenecks, and how engineering teams can end up spending more time operating infrastructure than building AI. 
• Why data residency and regulatory visibility matter more in India, and how these requirements influence infrastructure architecture and operational control. 
• What you actually trade off between public cloud, on-premises and emerging AI infrastructure models when balancing cost, control and deployment speed. 
• How technology leaders can evaluate AI infrastructure choices, with the right questions around cost predictability, governance, performance, availability and scale.

Who you’ll hear from