Sovereign AI clouds must deliver local control, governed access, and production-grade AI services. Rafay helps operators turn in-region GPU infrastructure into a secure, multi-tenant AI cloud where users can consume compute, models, and AI services within required boundaries.

• Operate in-region, private, or air-gapped environments with data and workloads kept within defined borders
• Deliver sovereign-ready AI services across GPU compute, K8s, SLURM, workbenches, model APIs, and Token Factory
• Enforce hard and soft multi-tenancy across customers, teams, projects, and regulated environments‍
• Turn national AI infrastructure into consumable services for enterprises, developers, research, and public sector users

Enterprise AI stalls when platform teams are forced to manually provision environments, enforce access, and manage fragmented infrastructure. Rafay gives developers and data scientists governed self-service access to the compute and AI services they need, while platform teams retain control over cost, policy, and usage.

• Give teams self-service access to GPU compute, Kubernetes, SLURM, workbenches, inference, and model services
• Standardize environments across public cloud, private cloud, and hybrid infrastructure
• Control cost and usage with quotas, chargeback, policy enforcement, and per-team visibility
• Support enterprise AI from prototype to production with reusable environments, governance, and operational consistency

AI clouds span GPUs, clusters, storage, networking, tenants, and services. Rafay Observability gives operators a multi-tenant view across the data center, with AI-assisted dashboards, synthetic monitoring, automated triage, and workflow-driven remediation so teams can see what is happening, understand why it is happening, and act faster.

• See across the full AI cloud estate with unified visibility into clusters, hosts, GPUs, storage, network fabric, firewalls, alerts, logs, and tenant-level health
• Monitor services proactively with synthetic checks for SKUs, platform services, dependencies, latency, availability, and status pages
• Accelerate root-cause analysis with Atlas AI and micro-agents that correlate logs, metrics, traces, topology, and Kubernetes signals
• Automate Day-2 operations with reusable cookbooks, event-triggered workflows, human approvals, and integrations with tools like Slack, Teams, PagerDuty, Jira, ServiceNow, Datadog, Elasticsearch, and Loki

AI factories create more value when users consume models and APIs, not just infrastructure. Rafay Token Factory helps organizations expose AI services through governed APIs, track usage at the token level, and monetize consumption across teams, tenants, and customers.

• Deliver model APIs instead of raw infrastructure with governed, OpenAI-compatible access
• Track token usage for billing, chargeback, cost attribution, and consumption visibility
• Package models and AI services as revenue-ready offerings for internal teams or external customers‍
• Scale inference across shared GPU infrastructure with multi-tenancy, governance, and elastic capacity

Rafay helps platform teams and cloud providers convert GPU infrastructure into standardized compute services. Offer users the right abstraction for the job, including bare metal, VMs, Kubernetes, SLURM, containers, notebooks, and AI workbenches, all delivered through self-service.

• Deliver GPU-as-a-Service and packaged compute through SKUs, catalogs, portals, and APIs
• Support VMs, K8s, SLURM, containers, and bare metal from one governed platform
• Improve utilization with shared infrastructure, quotas, scheduling, and usage visibility‍
• Give users cloud-like access while platform teams control policy, tenancy, cost, and lifecycle

Kubernetes remains a critical foundation for AI and cloud-native workloads, but it should not be the whole story. Rafay helps teams manage Kubernetes consistently across environments while connecting it to the broader AI factory operating model: self-service, multi-tenancy, governance, and workload automation.

• Manage Kubernetes fleets across public cloud, private cloud, and on-prem environments
• Standardize clusters, add-ons, policies, and lifecycle operations without building custom tooling
• Support AI workloads on Kubernetes with GPU-aware governance, access controls, and workload automation‍
• Use Kubernetes as a foundation for higher-value services like workbenches, inference, and Token Factory