365 Data Centers has announced a partnership with Robot Network to deliver a new generation of private cloud AI solutions. The collaboration aims to merge colocation, connectivity, and artificial intelligence into a unified platform that enables organizations to securely deploy and scale AI workloads closer to their data – while reducing cost and latency.
The joint initiative represents a significant evolution in how data centers operate. Rather than functioning as passive environments for hosting compute and storage, 365’s facilities will now act as an “optimization layer” for AI, intelligently distributing workloads between edge and core environments. According to the company, more than 90% of AI operations can now run entirely within the colocation footprint, leaving only the most compute-intensive tasks to centralized high-density GPU clusters.
This architectural shift brings cost, performance, and sustainability advantages. By running AI workloads in a distributed private cloud, enterprises can minimize data transfer overheads, improve security, and achieve higher revenue per watt in colocation environments. The approach supports power densities of 10–50 kW per rack, accommodating advanced small-language models (SLMs), analytics, and business intelligence applications in a more energy-efficient manner.
“Our objective is to meet AI where colocation, connectivity, and cloud converge,” said Derek Gillespie, CEO of 365 Data Centers, in announcing the partnership. “This platform will provide seamless integration and economies of scale for our customers and partners, giving them access to AI that is purpose-built for their business initiatives.”
Robot Network’s proprietary AI stack underpins the new platform. It combines small and large language models (LLMs) and is optimized for AMD EPYC processors and NVIDIA GPUs. The system leverages models from leading AI developers – including Meta, OpenAI, and Grok – to deliver enterprise-ready generative AI capabilities while maintaining predictable costs and data governance.
Robot Network CEO Jacob Guedalia described the partnership as an effort to democratize access to AI through a secure, private cloud environment. “By pairing 365’s proven infrastructure and colocation expertise with our proprietary AI framework, we’re offering enterprises a trusted and cost-optimized platform to accelerate adoption,” he said.
Initial use cases include private AI chat systems, business intelligence, predictive analytics, and data reporting. These workloads are powered by smaller, fine-tuned models rather than massive public LLMs – a trend gaining traction among enterprises seeking control, compliance, and customization without the prohibitive cost of training models from scratch.
For 365 Data Centers, the partnership underscores its transformation from a traditional infrastructure provider to an AI-driven infrastructure-as-a-service (IaaS) leader. The company’s hybrid model blends colocation and private cloud computing, enabling enterprise clients to evolve their IT environments toward more autonomous, AI-assisted operations.
As enterprises face growing regulatory and data sovereignty pressures, private AI environments like 365’s promise stronger control over where and how information is processed. They also help mitigate risks associated with public cloud exposure and vendor lock-in – concerns that have become increasingly relevant in sectors like finance, healthcare, and government.
The collaboration between 365 Data Centers and Robot Network highlights a broader industry movement toward “AI-native infrastructure”, where compute, networking, and storage architectures are optimized for continuous machine learning and inference. By integrating AI directly into the colocation fabric, this model bridges the gap between traditional IT environments and emerging agentic AI systems that require constant optimization and real-time adaptability.
In effect, the partnership creates a blueprint for the next generation of enterprise infrastructure, one that combines physical resilience with AI-driven intelligence.
FAQ: Private Cloud AI in the Enterprise
What is private cloud AI?
Private cloud AI refers to deploying AI workloads within a secure, dedicated cloud environment – often hosted in colocation or on-premise data centers. It provides the benefits of scalability and automation while maintaining control over data and infrastructure.
How does private cloud AI differ from public AI services?
Public AI models, such as those from major hyperscalers, operate on shared infrastructure and may expose sensitive data to third parties. Private cloud AI keeps models, data, and compute resources isolated within a customer-controlled environment for compliance and security.
Why are small-language models (SLMs) important?
SLMs are optimized, domain-specific models that offer high performance with lower compute requirements. They make AI adoption affordable and feasible for enterprises that lack hyperscale resources while supporting on-prem or hybrid deployments.
What are the security benefits of a private AI cloud?
Private AI environments enhance compliance with data protection regulations by allowing organizations to define access controls, monitor data movement, and apply encryption at every layer. This minimizes exposure to external networks or unauthorized use.
How does this approach impact infrastructure efficiency?
By running AI workloads in colocation facilities with hybrid cloud integration, enterprises can optimize power usage, reduce latency, and lower costs. It also allows them to scale incrementally – aligning compute resources with demand rather than overprovisioning.
