Find self-service GPU-as-a-Service solutions for AI workloads.

Self-Service GPU-as-a-Service Solutions for AI Workloads

Cloud-native GPU marketplaces (fastest to get started, no sales calls):

• RunPod —

offers self-serve instant GPU clusters with Slurm/K8s and expert support, plus managed fine-tuning/inference APIs, ideal for teams wanting both raw cluster access and higher-level training services
. (runpod.io)

• Nebius —

provides immediate access to up to 8 NVIDIA GPUs plus CPU, storage, and services through a self-service console, available on-demand with no waitlists or minimum commitments
. Larger allocations up to 32 GPUs are also available
without approval or talking to sales
. (nebius.com)

• Together AI — recently launched "Instant Clusters," which

emulate the user experience of conventional cloud infrastructure while handling the specific demands of AI workloads
, with
hardware checks, stress tests and inter-node communication validations before making clusters available
. (siliconangle.com)

• Lambda Labs —

has moved to self-serve, on-demand access with no formal
sales gate for many GPU types. (spheron.network)

Enterprise/hybrid GPUaaS platforms (for building internal or provider-facing services):

• NorthWind GPU PaaS —

delivers on-demand access to GPU compute through APIs or self-service portals, similar to how cloud platforms deliver CPU-based infrastructure, providing a platform layer that turns raw infrastructure into a scalable, governed service for AI/ML workloads
. It also integrates with
NVIDIA Run:ai, giving customers a dedicated environment without ever needing to touch infrastructure, transforming it into a self-service, cloud-managed service
. (northwind.co)

• Red Hat OpenShift AI —

is a Kubernetes-based platform that can be used to implement a multi-user GPUaaS solution, with dynamic allocation based on workload demand so GPUs are quickly reclaimed to minimize idle time
. (redhat.com)

Hyperscalers (broad ecosystem, best for existing cloud customers): AWS EC2 GPU Instances, Google Cloud GPU, and Microsoft Azure GPU VMs remain top picks for
outstanding features and versatility
.

Sources:

• runpod.io
• nebius.com
• siliconangle.com
• northwind.co
• redhat.com