The World’s Leading Neoclouds Run on Rafay

Rafay helps neoclouds, sovereign AI clouds, Telcos, and enterprises turn GPU infrastructure into self-service, governed AI cloud services, from Token Factory and inferencing to Kubernetes, SLURM, bare metal, and VMs.

Trusted by leading enterprises, neoclouds and service providers

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Find your path from AI infrastructure to AI services

Whether you’re building a neocloud, launching a sovereign AI cloud, or scaling enterprise AI, Rafay helps you move up the stack from GPU capacity to self-service compute, packaged environments, and monetized AI services.

Build a differentiated AI cloud, not just another GPU rental business

Neoclouds need to stand out beyond raw GPU availability. Rafay helps GPU-first cloud providers turn infrastructure into a full AI cloud platform with self-service access, multi-tenant governance, packaged compute, usage visibility, and higher-value AI services.

• Launch revenue-ready services across bare metal, VMs, Kubernetes, SLURM, inferencing, and Token Factory
• Move beyond commodity GPU rental with packaged compute and AI services customers can consume on demand
• Support secure multi-tenancy across customers, teams, projects, quotas, and policies
• Build a branded AI cloud experience with portals, catalogs, APIs, metering, and service delivery workflow

Operate AI infrastructure with confidence, before small issues become outages

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

Turn inference into token-metered AI services

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

Package compute into services users can consume

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

Standardize Kubernetes as the foundation for AI infrastructure

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

Start a conversation with Rafay

Talk with Rafay experts to assess your infrastructure, explore your use cases, and see how teams like yours operationalize AI/ML and cloud-native initiatives with self-service and governance built in.