NexGen Cloud unveils Hyperstack AI Studio for enterprises
NexGen Cloud has launched Hyperstack AI Studio, an end-to-end platform for AI application development targeted at enterprises and research organisations.
The new service is designed to support the full lifecycle of AI applications, from conceptualisation and evaluation through to final testing and production deployment. The AI Studio is built on NexGen Cloud's Hyperstack platform and operates on a usage-based pricing model, with customers paying only for the AI resources they use.
Enterprise applications
The Hyperstack AI Studio is aimed at organisations working in sectors such as software, SaaS, and those operating under regulatory oversight. Beta testers so far have applied the studio in areas ranging from surveillance monitoring using AI analysis to AI-driven bookkeeping agents and content marketing solutions.
According to NexGen Cloud, the platform enables development teams to create, evaluate, and deploy AI applications in a unified environment. Hyperstack provides access to what the company claims is one of the highest performance AI clouds globally, supporting rapid scaling from initial testing to large-scale deployment via a virtualised infrastructure with secure high-speed interconnects.
"Companies and organisations are desperate to gain competitive advantage through AI applications but lack the resources and skills to make this happen," explains Youlian Tzanev, Chief Strategy Officer & Co-Founder of NexGen Cloud. "With Hyperstack's AI Studio, we've removed the operational overhead and made it radically easier to build and deploy custom AI applications at scale - all while giving users full control of their models and data. Our end-to-end AI application platform is designed to take users from uploading a dataset to a customised and fine-tuned LLM-powered application, without needing infrastructure support or external tooling."
The AI Studio integrates development and deployment processes so that applications can be designed, tested, and rolled out via a single workflow. It gives users access to enterprise-grade GPUs and direct hardware access, with transparent pricing and an emphasis on security and reliability.
Open-source flexibility
The platform supports open-source large language models (LLMs) such as Llama and Mistral. This approach enables teams to fine-tune and manage their own models, delivering applications via private API endpoints and avoiding reliance on third-party services or proprietary APIs.
The AI Studio also includes tools for dataset management, model fine-tuning, performance evaluation, and prompt testing. All features are accessible through a single user interface, which allows developers to prototype, compare model variants, and deploy private endpoints with minimal friction. While developers operate applications and models through this interface, infrastructural concerns such as GPU provisioning, secure storage, and scaling are handled by Hyperstack using Kubernetes-based container orchestration.
Security and deployment options
Security measures and access controls are integrated throughout the platform, making Hyperstack AI Studio suitable for regulated and production environments. By default, it runs on a secure, virtualised and serverless architecture optimised for rapid inference. However, organisations that require stricter data governance or dedicated resources can move workloads to isolated GPU servers.
For organisations with specific sovereignty or compliance needs, the AI Studio can be delivered as a private, single-tenant deployment on NexGen Cloud's infrastructure. This offers clients full control over aspects such as data residency and model accessibility.
Background and context
Hyperstack AI Studio builds on NexGen Cloud's pre-existing offers in AI infrastructure, expanding its on-demand GPU services for customers in both the enterprise and research spaces. The company positions itself as a supplier of production-ready platforms for businesses with modern generative AI workloads, as well as providing private cloud solutions for large-scale, secure AI deployments.
