May 21, 2026

Unisys Teams with Rafay Systems to Streamline Enterprise AI Infrastructure Orchestration

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Unisys Teams with Rafay Systems to Streamline Enterprise AI Infrastructure Orchestration. The two firms announced a joint effort to give large‑scale AI infrastructure a unified, governed layer that spans on‑prem, edge and public clouds. By marrying Unisys’ managed services and AI expertise with Rafay’s self‑service orchestration platform, the partnership promises to reduce the operational friction that still hampers many enterprise AI projects.

A unified layer for AI workloads

The collaboration introduces a software‑as‑a‑service (SaaS) stack that abstracts agents, models and the underlying compute resources into a single control plane. Customers can spin up GPU‑accelerated clusters, attach LLM inference services, or launch custom training pipelines from a browser‑based console, while Rafay enforces policy, metering and cost controls automatically. For organizations that have been juggling separate Kubernetes clusters, on‑prem GPU farms, and third‑party MLOps tools, the offering provides a “one‑stop shop” for provisioning, scaling and governing AI workloads.

Why the timing matters

According to a 2024 Gartner survey, only 36 % of enterprises feel ready to handle production‑grade AI workloads, a gap that translates into missed revenue and slower time‑to‑market for AI‑driven products. The Unisys‑Rafay solution tackles three pain points identified by Forrester: (1) fragmented infrastructure, (2) opaque cost structures, and (3) regulatory compliance overhead. By embedding token‑based metering and role‑based access controls, the platform helps CFOs and security officers keep a clear line of sight on AI spend and data residency, a requirement that is increasingly scrutinized under GDPR, CCPA and emerging AI‑specific regulations.

The platform also improves search visibility into AI costs, allowing finance teams to allocate budgets more precisely.

Implications for enterprise marketing teams

Marketing departments are increasingly dependent on AI for personalization, predictive analytics and content generation. The new orchestration layer reduces the time required to provision a GPU‑backed inference endpoint from days to minutes, meaning campaign teams can test and iterate on LLM‑driven copy or recommendation engines in near‑real time. Moreover, the built‑in compliance checks ensure that customer data used for model training stays within approved jurisdictions, mitigating brand‑risk exposure.

The platform’s cost‑visibility features also empower marketing finance to attribute AI spend directly to campaign ROI, a capability that has been elusive when AI resources are billed as a lump sum by cloud providers.

For marketing teams, this translates into faster go‑to‑market cycles and clearer budget justification.

Looking ahead

Unisys and Rafay plan to extend the partnership with pre‑built integrations for popular AI frameworks such as PyTorch, TensorFlow and the emerging LangChain ecosystem. If the joint solution gains traction, it could pressure the cloud giants to open their orchestration stacks to truly multi‑cloud and edge deployments—a shift that would accelerate the broader enterprise AI adoption curve.

Market Landscape

Enterprise AI adoption is entering a “scale‑up” phase. IDC projects that global AI‑related spending will reach $1.2 trillion by 2027, with infrastructure accounting for roughly 40 % of that budget. Hybrid‑centric orchestration platforms are positioned to capture a share of this spend, especially as regulators tighten data‑localization rules. While pure‑play cloud services dominate the low‑cost, high‑velocity segment, solutions that can bridge on‑prem, edge and multiple clouds are expected to grow at a CAGR of 22 % through 2028, according to a recent Forrester Wave.