If vLLM already solved LLM serving, why did SGLang appear?
Overview
Neysa and AIM Research convened a private, invite-only roundtable in Bengaluru, bringing together senior AI, data, and engineering leaders from digital-first enterprises to explore the realities of scaling agentic AI beyond the pilot stage—where production volumes, infrastructure demands, cost pressures, and governance become critical to enterprise adoption.
Ninety minutes, moderator-led, structured around five pillars: Exposure, Model, Ownership, Governance and Adoption. Each one surfacing a different reason organisations plateau, and none of them a model problem.
This is what they said.
Exposure.
Out of most initiatives, only a fraction survive deployment, and virtually none reach true scale. As agents interact with real-world production APIs, throughput limitations, network latency, and accelerator provisioning directly dictate user experience and operating margin.
Control.
Engineering teams are shifting toward open-weight, neoclouds to secure architectural sovereignty and control.
Ownership.
Having data physically stored within country borders does not guarantee operational command. True sovereignty resembles airport control: hosting assets on domestic servers matters little if an external entity controls the dispatch system and dictates operational status.
Governance.
You can explain your model, but you’ll face an uphill battle explaining your agent’s actions. The most repeated line in the room, independently, across fintech, logistics, retail and healthtech: we do not have an AI auditor. Agents ship fast and in silos, with no central harness tracking which tools an agent calls, what data it touches, or how cost and latency move as usage grows.
Adoption.
Everything else on this list can be bought, but this one cannot. The technical constraints are solvable with money and engineering. What stalls programmes is organisational: whether the right talent sits in-house, and whether the business will change process, headcount and reporting lines. The teams furthest ahead did not win with a big rollout. They won with visible wins, and real buy-in from the top.
What the room said
“Most organisations are one stage below where they place themselves. The gap shows up as blockers.”
Leaders self-placed on a five-stage maturity ladder before the discussion began. The largest group put themselves at Operational: AI embedded in select processes, with defined ownership, but not yet scaled across functions with measurable return.
The two concerns shared by the most people in the room were both economic. Inference spend growing faster than revenue, and token spend consuming the engineering budget inside a quarter. Cost governance and unit economics, not latency or GPU access, were the sharpest nerve in the room.
Optional third line, use only if you want the harder edge: Without visibility into what an agent costs and what it touches, unit economics do not survive 10x volume. The business case breaks before the technology does.
Continue the conversation
The roundtable closed with a single offer, and it still stands.
One conversation about whatever is actually blocking you. A look at your architecture, a walkthrough of where your inference spend is going, or a scoped pilot on Velocis.
FAQs
What was the Neysa x AIM Roundtable?
A closed-door, invite-only session hosted by AIM Research and Neysa in Bengaluru on 20 August 2026. Ninety minutes, neutral moderator-led, bringing senior engineering and business leaders together on the practical realities of running agentic AI in production.
Who attended?
Senior AI, data, engineering and product leaders from digital-first enterprises across fintech, logistics, healthtech, retail, media and AI-native product companies. The session was held under closed-door conditions and individual attendees are not named.
What did the session cover?
Five pillars: Exposure, what breaks when agents meet production volume. Model, the cost of depending on someone else’s roadmap. Ownership, what data sovereignty does and does not give you. Governance, the missing audit trail for agents. Adoption, the organisational work no vendor can do for you.
What was Neysa’s role?
Neysa co-hosted the session with AIM Research. Prashant Pandey opened each pillar and responded with Neysa’s point of view. Neysa is the AI Acceleration Cloud the discussion was grounded in: enterprises bring their own models and applications and run them on Neysa’s GPU compute, inference endpoints and networking, in India.