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Neysa at Cypher 2026, AI Cloud Partner at Booth 3, KTPO Bengaluru

Neysa at Cypher 2026

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What is Neysa’s role at Cypher?
Neysa is the AI Cloud Partner at Cypher, engaging with AI builders, technology leaders and enterprises around the infrastructure required to run AI workloads at scale.
What will Ritesh Dhoot’s session cover?
Ritesh will explore the real cost of production AI – moving beyond token pricing to repeated context, capacity utilisation, data movement, model choice and infrastructure architecture. He will also show why cost per outcome is a more useful measure for production workloads.
Why isn’t the published price per million tokens enough?
Token pricing captures only one component of the overall cost. AI workloads can also involve repeated conversation history, multiple model calls, infrastructure capacity, data movement and other operational costs.
Is running open-weight models always cheaper?
No. Running open-weight models can provide greater control over infrastructure and workload economics, but organisations also take on the cost of hardware, utilisation and the people required to operate it. The economics depend on the workload and volume.
How can I calculate what my AI workload actually costs?
Start with real traffic from a single workload over a month. Measure requests, actual tokens processed, infrastructure utilisation and data leaving your environment. Then factor in retries, human fixes, idle capacity and AI applications purchased directly by business teams to build a more complete picture of cost per successful outcome.
Where is Neysa’s booth at Cypher 2026?
Neysa is at Booth 3 on the Cypher 2026 expo floor at KTPO, Whitefield, Bengaluru, all three days from 7 to 9 October 2026. Drop by for live demos of Neysa Velocis, from bare-metal GPUs to served inference endpoints.
How do I book a meeting with Neysa at Cypher?
Use the meeting form on this page to share your AI workload and preferred day. The Neysa team will confirm a slot at Booth 3 to discuss your infrastructure requirements, cost per outcome and the path from experimentation to production.