Neysa and Pipeshift launch real-time AI inference platform fully deployed within India

Neysa and Pipeshift Target India's Growing AI Inference Market

Bengaluru GPU service provider Neysa and managed inference platform Pipeshift have announced a partnership to launch production-grade real-time inference for open-source AI models fully deployed within India2

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. The collaboration aims to tap into surging demand for AI inference services in India as enterprises scale AI adoption across customer support, software development, analytics, and enterprise workflows1

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. According to Karan Kirpalani, Chief Product Officer at Neysa, India's AI inference market is estimated at $28-30 billion as of 2025, while the global market stands at approximately $125 billion1

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Addressing Production-Scale Challenges in AI in India

As AI adoption in India accelerates, many enterprises face production-scale challenges including unpredictable latency, escalating token costs, shared infrastructure bottlenecks, and overseas data routing2

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. While shared token-based APIs helped companies initiate AI projects, much of the infrastructure serving these workloads continues to sit outside the country, creating dependency on foreign infrastructure and dollar-denominated APIs2

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. The partnership between Neysa and Pipeshift directly addresses this gap by extending Velocis, Neysa's AI Acceleration Cloud System, with dedicated, low-latency infrastructure for production-grade AI applications2

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Real-Time Inference for Open-Source AI Models With In-Country Data Control

Pipeshift's managed inference platform running on Neysa's AI-native GPU infrastructure enables enterprises to deploy single-tenant inference environments for open-source AI models including Gemma, Qwen, GPT-OSS, Llama, DeepSeek, and Mistral through OpenAI-compatible APIs2

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. The platform is designed for latency-sensitive workloads including voice AI, enterprise search, enterprise copilots, workflow automation, and reasoning-based systems2

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. Critically, the infrastructure keeps prompts, inference, and enterprise data fully within India, providing in-country data control that addresses sovereignty concerns2

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Optimized Infrastructure Eliminates Shared Rate Limits and Cold-Start Delays

Kirpalani explained that scaling open-source models introduces a dual bottleneck: volatile token economics and high Time-to-First-Token driven by shared rate limits and cross-region routing. "By integrating Pipeshift's inference-engine optimizations directly onto Neysa's single-tenant, optimized bare metal, we eliminate this friction entirely," he said2

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. The platform eliminates shared rate limits, cold-start delays, and cross-region routing overheads that often affect shared inference environments, while dynamically auto-scaling during demand spikes2

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Early Deployments Show Significant Performance Improvements

Early production deployments demonstrate the platform's capabilities for local AI systems. Nurix AI achieved a 3x reduction in Time to First Token for its voice AI deployments in India. "We needed sub-second LLM latency for voice agents in production, and real-time inference from Neysa and Pipeshift cut our TTFT 3x versus our prior setup in India," said Pushkar Patel from Nurix AI2

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. Arrowhead AI is using the platform for multilingual inference workloads, with CTO Vengadanathan Srinivasan noting that Neysa and Pipeshift had their fine-tuned model live as an inference endpoint within a day2

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Meeting Demand for Multi-Modal, Multilingual AI Systems

Kirpalani noted that India's inference landscape requires complex AI ecosystems at significant scale and diversity. "We are seeing a lot of demand for multi-modal, multilingual AI systems that are specialised and local. The partnership between Pipeshift and Neysa addresses those concerns," he said1

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. Arko Chattopadhyay, Co-Founder and CEO of Pipeshift, emphasized the production readiness focus: "There is a clear line between AI that works in a demo and AI that works in production. Crossing that line takes more than a good model. It takes infrastructure that holds latency under load and keeps costs predictable at scale"2

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. The platform is immediately available for enterprises evaluating production-scale deployments across customer support, workflow automation, and regulated AI workloads, with typical deployment timelines from evaluation to production under two weeks2

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