Dark modular cubes with red edges, representing the many GPU-cloud options an AI team can choose from.
Every GPU cloud is a modular block in the same stack. The skill is picking the block that matches your workload.

Training and running AI models needs GPUs, and most teams rent them rather than buy them. The market splits into two camps. General-purpose hyperscalers (AWS, Azure, Google Cloud, Oracle Cloud) offer GPUs alongside hundreds of other services. A newer set of specialists, often called a neocloud , focuses almost entirely on GPU compute for AI training and inference .

This page maps the landscape. It covers bare GPU providers, serverless platforms, inference APIs, and GPU marketplaces, then gives short guidance on how to choose. The table stays qualitative on purpose. Prices and instance specs change often, so check each provider’s own pricing page before you commit.

The two camps

A hyperscaler runs a broad cloud: compute, storage, databases, networking, identity, and much more. GPUs are one product among many. You reach for a hyperscaler when your AI workload sits next to a lot of other cloud services and you want one bill, one identity system, and one security model.

A neocloud is built for GPUs first. These providers buy large fleets of NVIDIA accelerators, wire them with fast interconnect, and rent them out for training and inference. They tend to offer newer hardware sooner, sharper pricing on raw compute, and less of the surrounding platform. Independent benchmarking from SemiAnalysis, whose ClusterMAX rating tracks GPU cloud reliability and performance, places CoreWeave at the top tier and Nebius, Crusoe, and others in strong positions.

Comparison table

Types below: bare GPU means you rent raw GPU instances or clusters and manage them yourself. Serverless means you deploy code or containers and the platform scales GPUs up and down, including to zero. Inference API means you call hosted models over an endpoint and pay per token or per request. Marketplace means you rent capacity from many independent hosts at spot-style prices.

ProviderTypeMain offeringBest for
CoreWeaveBare GPULarge managed GPU clusters with fast interconnectLarge-scale training, reliability at scale
LambdaBare GPUOn-demand GPU instances and clustersTraining and fine-tuning, simple setup
NebiusBare GPUGPU cloud with managed platform layerTraining with a full cloud platform around it
CrusoeBare GPUEnergy-optimized GPU clustersSustainability-conscious training workloads
Together AIInference APIHosted open models plus fine-tuning and trainingOpen-model inference and fine-tuning
Fireworks AIInference APIFast hosted open-model inferenceLow-latency open-model serving
GroqInference APIHigh-throughput inference on custom hardwareVery fast token generation
ModalServerlessRun Python with GPUs attached, scale to zeroCustom inference and batch jobs from code
BasetenServerlessDeploy and scale ML models, model APIsServing custom models with autoscaling
RunPodServerless plus bare GPUPay-as-you-go GPUs and serverless workersCost-conscious inference, varied GPU choice
Vast.aiMarketplaceSpot GPU rental from independent hostsLowest cost, tolerant of variable reliability
PaperspaceServerless plus notebooksGPU notebooks and deployments (DigitalOcean)Prototyping and notebook-based work
VultrBare GPUGPU instances across many regionsRegional GPU compute near a broader cloud
HyperscalersBare GPUGPUs inside AWS, Azure, GCP, OracleWorkloads that sit next to other cloud services

How to choose

Match the layer to your workload rather than chasing the lowest hourly rate.

Step 1 Training or inference Long training runs favour bare GPU clusters. Serving models favours serverless or an inference API.
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Step 2 Managed or raw Want the platform to handle scaling and endpoints? Pick serverless or an API. Want full control? Rent bare GPUs.
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Step 3 Cost or reliability Marketplaces cut cost but vary in reliability. Top-tier neoclouds and hyperscalers cost more and stay steady.

Training vs inference. If you fine-tune or pretrain models, you need sustained access to many GPUs with fast interconnect. Bare GPU neoclouds like CoreWeave , Lambda , Nebius , and Crusoe are built for this. If you only serve models, an inference API like Together AI , Fireworks AI , or Groq removes the operations work entirely, and you pay per token.

Managed vs raw. Serverless platforms such as Modal and Baseten let you deploy custom code or models and scale to zero when idle, which suits spiky traffic. Raw GPU rental gives you full control over the environment but leaves scaling, endpoints, and reliability to you.

Cost vs reliability. A marketplace like Vast.ai and cost-focused options like RunPod push prices down, with more variability in hardware and uptime. Top-tier neoclouds and hyperscaler GPUs cost more but deliver steadier performance for production. Paperspace and Vultr sit closer to a broader cloud experience, useful when GPUs are one part of a larger footprint.

If your models sit alongside existing cloud services, staying on your hyperscaler can be worth a higher GPU price for the single bill, shared identity, and shared security model. If GPU compute is the core cost, a neocloud usually wins on price and hardware availability.

Further reading

Sources