Neysa Velocis
The AI Acceleration Cloud to Build and Run Your Models
Train, fine-tune, and serve your models on GPU compute infra built for AI, with full observability and governance over every workload.
The teams shaping
India’s AI future build with us.
Why Velocis
Full AI lifecycle in one place.
Everything your team needs to take AI from prototype to production.
Performance that holds at scale.
High-performance networking keeps every GPU fed, so training stays fast and inference holds under load.
Reliability you can trust.
High-availability design backed by SLAs, so your long runs and endpoints keep running.
Support that picks up.
A 24×7 NOC, named technical account manager, and AI engineers who work alongside you.
platform
The AI cloud for training, inference, and everything in between.
Velocis runs the whole AI lifecycle on GPUs built for it: foundation-model training and distributed fine-tuning, notebooks and experiments, dedicated inference in production, and the orchestration, observability and governance around all of it.
Open by default: Kubernetes, Slurm, MLflow, vLLM, and S3-compatible storage.
USE CASES
Everything your AI needs.
Dedicated inference endpoints.
Serve open-source models or your own on dedicated, single-tenant endpoints tuned to your latency and traffic, on vLLM and SGLang. Deterministic performance under production load, and your model IP stays yours.
AI platform to build on.
Get frameworks and tools, with pre-integrated environments: Jupyter, PyTorch, Hugging Face, MLflow and Kubeflow. Notebooks spin up in minutes and stop billing when the work stops.
Orchestration and MLOps.
Distributed training on Slurm, Kubernetes on VKE, and an MLOps pipeline from data to CI/CD: model registry, versioning, experiment tracking and monitoring, wired to Git and containers.
Unified monitoring and management.
One Command Center for full-stack observability: GPU, disk and NVMe utilisation in real time, fleet-to-GPU health with DCGM, and custom metrics where you need them.
Catalog
Private model repositories and a curated catalogue inside your VPC: infrastructure, models, tools and environments, ready to deploy and reuse across teams, projects and stages.
Security by design.
Dynamic RBAC, immutable audit logs, policy enforcement, encryption, and Zero Trust at the network layer, across the platform.
Marketplace ecosystem.
*coming soonA curated set of AI-native apps, agents and tools, ready to deploy on Velocis and integrated with the cloud. Extend capabilities without integration work.
Solutions by team
What teams build on Velocis.
rom strategy to scale, Velocis empowers every layer of the enterprise AI stack; from CXOs driving outcomes to engineers managing training and deployment.
Lower TCO with optimized, transparent pricing.- Security, compliance and governance built into the platform
- Capacity that scales ahead of your roadmap
- Your choice of deployment: shared VM, or a dedicated single-tenant environment
- Notebooks in minutes with an idle killer, GPUs wired into your MLOps pipeline
- Distributed training on Slurm, with MLflow and Kubeflow
- Open-source and open-weight models and toolkits, your stack and your frameworks
- Pre-built environments for PyTorch, Hugging Face and Jupyter
- One place to watch health, utilisation and spend across the fleet.
- Role-based access and per-team cost tracking.
- High availability, backed by SLAs.
Put your AI to production
Neysa Velocis gives your AI teams the system they’ve been waiting for — to build, operate, and scale AI with speed, security, and real ROI.