Neysa closed a $600 million equity round in February 2026 led by Blackstone at a $1.4 billion enterprise value. The round included participation from Teachers' Venture Growth, TVS Capital Funds, 360 ONE Asset, and existing investor Nexus Venture Partners.

The company previously raised a $20 million seed round in April 2024, followed by a $30 million Series A in October 2024 led by Nexus Venture Partners with participation from NTTVC and Z47 (Matrix Partners India).

Neysa plans to raise an additional $600 million in debt financing to support its infrastructure expansion. Total funding raised to date exceeds $650 million in equity capital.

Neysa is a full-stack AI cloud platform built on its Velocis GPU infrastructure. It combines a GPU infrastructure layer with integrated AI development tooling, accessible via web consoles or APIs.

The core infrastructure layer provides on-demand access to NVIDIA L4, L40S, H100, and H200 GPUs, plus AMD MI300X chips without waitlists. Customers provision clusters in under 30 seconds from pre-wired capacity in Mumbai and Bangalore data centers connected by a 3200 Gbps networking fabric.

On top of the compute layer, Neysa offers AI Studio with pre-configured JupyterLab and VS Code workspaces connected to PyTorch, TensorFlow, and HuggingFace. The MLOps Studio supports drag-and-drop pipelines that orchestrate data ingestion through Airbyte and Kafka, training via Spark and Airflow, experiment tracking with MLFlow and Weights & Biases, and deployment through Kubeflow and RayServe.

Users can deploy models as autoscaling inference endpoints with unified cost and latency monitoring. Additional modules include Overwatch for network observability and traffic optimization, and Aegis for model security against prompt injection and data exfiltration attacks.

The platform targets enterprise AI teams that need to keep data within India for regulatory compliance, along with AI-native startups seeking predictable GPU supply and lower per-hour costs than hyperscaler alternatives.

Neysa runs a vertically integrated cloud infrastructure model, owning and operating dedicated GPU data centers rather than reselling capacity from hyperscalers. The structure targets 40-60% lower unit economics versus major cloud providers by using lean, AI-focused facilities and open-source software stacks.

Go-to-market is B2B, serving large enterprises with data sovereignty requirements and smaller AI companies seeking cost-effective GPU access. Revenue comes through multiple pricing tiers, including pay-per-use on-demand instances, reserved capacity with volume discounts, and private cloud deployments for the largest customers.

Beyond GPU-as-a-Service, Neysa adds a Platform-as-a-Service layer that bundles MLOps orchestration, inference endpoints, security tools, and an AI marketplace into unified SKUs. The stack reduces customer integration complexity and expands revenue per GPU deployed.

Utilization tends to become more predictable as customers move from experimentation to production workloads. Reserved instances and private cloud contracts provide revenue visibility, and the integrated tooling creates switching costs that improve customer retention over time.

AWS, Microsoft Azure, and Google Cloud dominate the global GPU infrastructure market through massive scale and integrated AI services. AWS offers extensive GPU instances with Trainium custom silicon, while Azure leverages its OpenAI partnership for early access to cutting-edge hardware like GB300 and GB200 clusters.

Google Cloud differentiates through its seventh-generation TPU processors and JAX framework integration, targeting cost-efficient inference workloads. These incumbents benefit from global footprints and enterprise relationships but face challenges with GPU supply constraints, rising prices, and complex multi-service billing that creates opportunities for focused competitors.

CoreWeave and Lambda Labs represent the pure-play GPU infrastructure category, offering NVIDIA-optimized clusters with venture backing and streamlined provisioning. CoreWeave has secured significant NVIDIA partnership benefits and financing, while Lambda focuses on researcher and startup segments with competitive hourly pricing.

Nebius operates in similar markets with a focus on European and emerging market customers. These specialists compete on GPU availability, simplified pricing, and faster deployment compared to hyperscalers, but lack the geographic reach and integrated tooling that Neysa provides for the Indian market.

Oracle Cloud Infrastructure positions itself as offering the largest AI supercomputer deployments with predictable capacity reservations and sovereign cloud regions. Local Indian cloud providers and system integrators also compete for enterprise customers requiring data residency, though most lack dedicated GPU infrastructure and rely on partnerships with global providers.

Neysa can expand beyond raw GPU infrastructure into managed services including fine-tuning APIs, model hosting, and inference optimization. Neysa's AI marketplace allows ISVs and model publishers to distribute applications on Neysa's infrastructure, creating a revenue-sharing model similar to AWS Marketplace that increases monetization per deployed GPU.

Vertical industry packages for banking, insurance, manufacturing, and retail represent another expansion vector. Pre-built compliance templates, reference architectures, and domain-specific models can command premium pricing while reducing customer implementation complexity.

With the $1.2 billion funding round, Neysa can expand its GPU fleet from 1,200 to over 20,000 units and enter new geographic markets. Singapore, Middle East, and European sovereign cloud regions could extend Neysa's data residency coverage to multinational enterprises with similar regulatory requirements.

Regional expansion would also support the growing demand for edge inference capabilities as AI applications require lower latency for real-time use cases in autonomous vehicles, industrial automation, and consumer applications.

Neysa's empanelment under India's IndiaAI Mission provides access to government-funded GPU demand as the country targets 100,000 installed GPU units by December 2026. Public sector AI initiatives, academic research programs, and startup acceleration schemes represent an addressable market with multi-year contract potential.

The sovereign AI trend extends beyond India as countries seek domestic AI capabilities for national security and economic competitiveness. Neysa's experience building dedicated AI infrastructure could translate to similar opportunities in other emerging markets seeking alternatives to US-based cloud providers.

Supply chain concentration: Neysa's business model relies on securing adequate supplies of high-end NVIDIA GPUs, which face global shortages and allocation constraints. Disruptions to GPU procurement or unfavorable allocation decisions by NVIDIA could constrain the company's ability to scale infrastructure and meet customer demand, particularly as it attempts to grow from 1,200 to over 20,000 GPUs.

Hyperscaler price competition: Major cloud providers like AWS, Google Cloud, and Microsoft Azure have greater resources and can engage in sustained price competition to defend market share. If hyperscalers cut GPU pricing or offer preferential rates to Indian customers, Neysa's cost advantage could erode quickly, which could force the company to compete primarily on features and service quality rather than economics.

Regulatory and compliance shifts: While data sovereignty requirements currently favor local providers like Neysa, changes in Indian data protection regulations or international trade policies could shift competitive dynamics. New compliance requirements might increase operational costs, while relaxed data residency rules could reduce Neysa's differentiation versus global cloud providers.