If vLLM already solved LLM serving, why did SGLang appear?
Overview
Neysa and AIM Research convened a private, invite-only roundtable series across Bengaluru, New Delhi, and Mumbai; bringing together CXOs from digital-first enterprises to examine what actually breaks when AI moves beyond experimentation.
The discussion focused on production, where latency constraints, inference economics, hallucination control, observability, and SLA commitments define outcomes, and where most initiatives stall when organisations treat AI as a feature instead of an engineering system that demands cost discipline and continuous iteration.
This is what they said.
The Production Gap.
Organizations easily experiment with models in sandboxes. Real complexity hits during production. Latency, inference economics, hallucination control, observability pipelines, and SLA assurance define success. Most AI initiatives stall between a working demo and a deployed system.
Systems Over Models.
The industry shifted. Enterprises no longer just evaluate models. They invest in evaluation frameworks, continuous fine-tuning, multi-model orchestration, and AI-ready infrastructure. The model is just one component. The system is the product.
Continuous Engineering Reality.
AI maturity demands more than powerful models. It requires scalable compute, responsible design, cost visibility, robust pipelines, and continuous iteration. Organizations pulling ahead treat AI as an engineering discipline, not a feature launch.
The New OS.
Foundational models operate as the new OS. Real value does not come from using generic models better. It comes from embedding your domain expertise, regulatory logic, and industry-specific nuances into foundational models. Organizations doing this work today define their categories tomorrow.
Read the White Paper
AIM Research compiled the insights from these three roundtables into a single document detailing what is actually working for enterprises scaling AI in India.
FAQs
What was the Neysa x AIM Roundtable?
A closed-door series hosted by AIM Research and sponsored by Neysa, bringing together senior engineering and business leaders to discuss the practical realities of scaling AI infrastructure and economics.
Who attended?
Invite-only CXOs and technical operators from digital-first enterprises across fintech, e-commerce, healthcare, and mobility.
What does the white paper cover?
“Scaling AI in the Real World” documents the primary consensus from the roundtables, focusing on the transition from model experimentation to system deployment, alongside participant perspectives on managing infrastructure costs and continuous engineering.
How do I get the white paper?
You can download it for free using this link.
What was Neysa’s role?
Neysa sponsored the event series to facilitate independent discussions among practitioners. While our experience in AI infrastructure provided context, the sessions focused entirely on the ground-level challenges facing enterprise operators.