10 Best GPU Cloud Providers of 2026

1. 1

   CoreWeave

   A listed specialist AI cloud that rents NVIDIA GPUs from A100 to GB300 NVL72 with Kubernetes-native clusters.

   8.8overall

   A listed specialist AI cloud that rents NVIDIA GPUs from A100 to GB300 NVL72 with Kubernetes-native clusters.

   Best forAI labs and enterprises that need large multi-node training clusters from a provider with public capacity disclosures.

   Adoption data

   1.5 GWCoreWeave reported that it expanded active power by nearly 500 MW to 1.5 GW in the second quarter of 2026.

   CoreWeave, CoreWeave Reports Strong Second Quarter 2026 Results (Form 8-K, Exhibit 99.1) (2026): “Expanded active power by nearly 500 MWs to reach 1.5 GW”

   CoreWeave is the reference point for specialist GPU clouds because it discloses its scale in SEC filings. Its Q2 2026 release reported active power of 1.5 GW after adding nearly 500 MW in the quarter, total contracted power of about 3.7 GW and a revenue backlog of about $104 billion, not counting more than $25 billion of commitments added early in Q3. The pricing page lists on-demand rates for GB200 NVL72 at $42.00 per hour for four GPUs, HGX B200 at $68.80 and HGX H100 at $49.24 for eight GPUs, plus GH200, L40S, L40, A100 and RTX PRO 6000 Blackwell nodes. Spot rates appear for several systems, and inference workloads get a separate per-GPU price. The catch is cost and fit: an H100 works out to roughly $6.16 per GPU hour on demand, and the newest B300 and GB300 systems are sales-led. CoreWeave makes the most sense for teams that buy capacity by the rack.

   Strengths

   • +Largest disclosed specialist footprint, with 1.5 GW active and about 3.7 GW contracted
   • +Public list prices for nearly every current NVIDIA generation
   • +Revenue backlog of about $104 billion signals long-term supply commitments

   Limitations

   • −8-GPU H100 node works out to about $6.16 per GPU hour, above several rivals
   • −Newest B300 and GB300 systems require a sales conversation
   • −Built for clusters, so small single-GPU projects are better served elsewhere

   SummaryCoreWeave is the strongest pick for teams that need large, documented GPU capacity and current NVIDIA systems. Teams running a single GPU will pay less elsewhere.

2. 2

   Amazon Web Services

   The largest public cloud, offering H100, H200 and B200 instances through EC2 and short-term Capacity Blocks.

   8.7overall

   The largest public cloud, offering H100, H200 and B200 instances through EC2 and short-term Capacity Blocks.

   Best forTeams that want GPUs inside the same account as their data, security and managed AI services.

   Adoption data

   $46.0 billionAmazon reported AWS segment sales of $46.0 billion in the third quarter of 2026, a 39% increase from a year earlier.

   Amazon, Amazon.com Announces Third Quarter Results (2026): “AWS segment sales increased 39% year-over-year to $46.0 billion.”

   AWS is the default choice when GPU work has to sit next to everything else a company runs in the cloud. Amazon reported $46.0 billion of AWS segment sales in the third quarter of 2026, up 39 percent, which makes it the largest provider in this list by revenue. For GPU buyers the most transparent route is EC2 Capacity Blocks, which reserve clusters for a set period. The published effective rate for a p5.48xlarge with eight H100 GPUs is $47.757 per hour, or $5.970 per GPU, and the same rate applies across US East, US West, Tokyo and Jakarta. B200 capacity is available as p6-b200.48xlarge, listed at $113.666 per hour in Hyderabad, Stockholm and Paris, which is $14.208 per GPU. The value score reflects those rates: an H100 costs more here than at Nebius, Together AI or Runpod. The benefit is integration with S3, IAM, SageMaker and Bedrock without moving data between vendors.

   Strengths

   • +Largest cloud by revenue, with $46.0 billion of Q3 2026 segment sales
   • +Capacity Blocks give predictable access to H100 and B200 clusters
   • +Broad regional coverage for GPU instances

   Limitations

   • −P6-B200 Capacity Blocks cost $14.208 per GPU hour in some regions
   • −Instance-level billing means renting 8 GPUs at a time for P5
   • −Per-GPU H100 rate is above Nebius, Together AI and Runpod

   SummaryAWS is the safest choice for teams that already run their data and applications on Amazon. GPU-only workloads can usually be rented for less from a specialist.

3. 3

   Google Cloud

   Google's public cloud, with A4X, A4, A3 and A2 accelerator machines plus G4 and G2 instances for graphics and inference.

   8.6overall

   Google's public cloud, with A4X, A4, A3 and A2 accelerator machines plus G4 and G2 instances for graphics and inference.

   Best forTeams that want low-cost L4 inference instances and H100 or Blackwell training machines in one cloud.

   Adoption data

   $30.7 billionAlphabet reported Google Cloud revenue of $30.7 billion in the third quarter of 2026, up 103% from a year earlier.

   Alphabet, Alphabet Announces Third Quarter 2026 Results (Form 8-K, Exhibit 99.1) (2026): “Google Cloud delivered record growth as revenues increased 103% to $30.7 billion”

   Google Cloud reported $30.7 billion of revenue in the third quarter of 2026, up 103 percent, the fastest growth among the three largest clouds, and much of that demand is AI infrastructure. Its accelerator range is wide: A4X and A4 machines for Blackwell, A3 Mega and A3 High with eight H100 GPUs, A2 with A100, and G4 and G2 machines for RTX PRO 6000 and L4 inference. The accelerator price sheet shows a g2-standard-4 with one L4 at $0.706832276 per hour on demand, the lowest verified hyperscaler entry in this list, while an a3-highgpu-8g costs $88.490000119 per hour, about $11 per H100. Committed use and spot columns on the same page cut those figures sharply, so the on-demand rate overstates what most steady users pay. The value score sits between AWS and the specialists because H100 list rates are high, but L4 inference here is cheap.

   Strengths

   • +Cheapest hyperscaler entry point here, an L4 machine at about $0.71 per hour
   • +Fastest growing of the large clouds, with revenue up 103% in Q3 2026
   • +Several discount models published side by side

   Limitations

   • −A3 High with 8x H100 lists at about $88.49 per hour on demand
   • −Machine and GPU pricing spans several pages and models
   • −Committed use discounts require a 1 or 3 year term

   SummaryGoogle Cloud is a strong choice for inference on L4 and for teams already using BigQuery or Vertex AI. Plan on committed use or spot rates for H100 training.

4. 4

   Nebius

   A Nasdaq-listed AI cloud based in Amsterdam that rents NVIDIA HGX and NVL72 systems with published on-demand and preemptible rates.

   8.6overall

   A Nasdaq-listed AI cloud based in Amsterdam that rents NVIDIA HGX and NVL72 systems with published on-demand and preemptible rates.

   Best forTeams that want low list prices on H100, H200 and B200 nodes from a provider that files quarterly results.

   Adoption data

   $582.3 millionNebius reported revenue of $582.3 million for the second quarter of 2026, up 454% from $105.1 million a year earlier.

   Nebius Group, Nebius reports second quarter 2026 financial results (Form 6-K, Exhibit 99.1) (2026): “Revenues 105.1 582.3 454 %”

   Nebius is the second listed specialist in this list and one of the cheapest places to rent current NVIDIA systems on demand. Its Q2 2026 results, filed with the SEC, show revenue of $582.3 million, up 454 percent from $105.1 million a year earlier, and purchases of property and equipment of more than $5.6 billion in the quarter as it builds capacity. The pricing page lists on-demand per-GPU rates of $4.50 for HGX H100, $5.40 for HGX H200, $8.50 for HGX B200 and $9.50 for HGX B300, with preemptible capacity from $0.79 or $0.99 per GPU hour. RTX PRO 6000 instances cost $1.80 per GPU hour and L40S starts at $1.55. GB200 and GB300 NVL72 racks are sales-led. Nebius has fewer managed services than AWS or Google Cloud, but for teams that mainly need GPUs and storage, its rates and disclosures make it a strong option.

   Strengths

   • +H100 at $4.50 per GPU hour on demand, below CoreWeave and AWS
   • +Quarterly SEC filings show revenue up 454% in Q2 2026
   • +Preemptible rates published for every HGX tier

   Limitations

   • −NVL72 rack systems are not priced publicly
   • −Spot prices are dynamic and can rise
   • −Smaller service catalog than a hyperscaler

   SummaryNebius offers the best mix of low list prices and public financial disclosure for HGX clusters. Teams that need rack-scale NVL72 systems will still talk to sales.

5. 5

   Runpod

   A developer GPU cloud with on-demand pods, serverless endpoints and clusters across 31 regions.

   8.5overall

   A developer GPU cloud with on-demand pods, serverless endpoints and clusters across 31 regions.

   Best forDevelopers and small teams that want cheap single GPUs and serverless inference billed by the second.

   Adoption data

   1.2 millionRunpod said in October 2026 that more than 1.2 million developers build on its platform, up from about 495,000 a year earlier.

   Runpod, Runpod Surpasses $325 Million in ARR, Appoints Brian Erickson as Chief Financial Officer (2026): “More than 1.2 million developers now build on Runpod, up from approximately 495,000 a year ago.”

   Runpod is built for developers who want a GPU running in minutes and billed in small increments. Its pricing page lists pods from $0.27 per hour for an RTX A5000 with 24 GB of VRAM, $0.59 for an L4, $0.89 for an RTX 4090, $1.79 for an A100, $2.89 for H100 PCIe, $3.99 for H100 SXM and $7.99 for a B200 with 180 GB of VRAM. Pods run across 31 regions, and serverless endpoints, now including batch inference, cover API-style inference without managing machines. In October 2026 Runpod said annual recurring revenue passed $325 million, up from $120 million in January, and that more than 1.2 million developers build on the platform, up from about 495,000 a year earlier. Those are company figures rather than audited results, which keeps the adoption score below the listed providers. For prototyping, fine-tuning and bursty inference, the value is hard to beat.

   Strengths

   • +Lowest verified starting price in this list, $0.27 per hour
   • +H100 SXM at $3.99 and H100 PCIe at $2.89 per hour
   • +Fast-growing developer base of more than 1.2 million

   Limitations

   • −Adoption figures are company-reported, not audited
   • −Large reserved clusters need a sales conversation
   • −Pods and serverless endpoints use separate rate cards

   SummaryRunpod is the best value for single-GPU work and serverless inference. Teams planning long multi-node training runs should compare cluster terms with CoreWeave or Nebius.

6. 6

   Microsoft Azure

   Microsoft's public cloud, with ND-series virtual machines for large training clusters and NC and NV series for smaller GPU jobs.

   8.4overall

   Microsoft's public cloud, with ND-series virtual machines for large training clusters and NC and NV series for smaller GPU jobs.

   Best forMicrosoft-centric enterprises that want GPU clusters under existing Azure contracts and identity.

   Adoption data

   19%Synergy Research Group estimated Microsoft's share of worldwide cloud infrastructure services spending at 19% in the third quarter of 2026, behind Amazon at 26% and ahead of Google at 16%.

   Synergy Research Group, Cloud Market Growth Rate Surges to Well Over 50% per Year; Q3 Revenues Reach $168 Billion (2026): “Q3's worldwide market share for these companies was 26%, 19%, and 16%, respectively.”

   Azure is the GPU cloud most enterprises already have a contract with, and Synergy Research Group put Microsoft's share of worldwide cloud infrastructure spending at 19 percent in the third quarter of 2026, second only to Amazon. Microsoft's documentation describes the ND H100 v5 series as a flagship for deep learning training and tightly coupled generative AI and HPC workloads: each VM starts with eight NVIDIA H100 GPUs, deployments scale to thousands of GPUs, and every VM gets 3.2 Tbps of interconnect bandwidth with a dedicated 400 Gb link per GPU. Smaller NC and NV series cover inference and visualization. Azure's pricing pages did not show US dollar GPU rates when fetched from our location, so the price is marked pending and the value score is conservative. Microsoft also does not break out Azure revenue, which is why adoption relies on a third-party share estimate. For teams already standardized on Microsoft agreements, it is the simplest procurement path.

   Strengths

   • +Second-largest cloud by Synergy Research Group's Q3 2026 share estimate
   • +ND H100 v5 is built for tightly coupled multi-node training
   • +Fits companies that already buy through Microsoft agreements

   Limitations

   • −GPU prices did not show in US dollars from our location, so value is scored conservatively
   • −ND H100 v5 starts at eight GPUs per VM
   • −Microsoft does not publish standalone Azure revenue

   SummaryAzure suits enterprises that want GPU clusters inside their existing Microsoft contracts. Check current US rates on the vendor page before comparing it with specialist clouds.

7. 7

   Crusoe

   An AI factory company that builds its own data centers and rents NVIDIA and AMD GPUs through Crusoe Cloud.

   8.2overall

   An AI factory company that builds its own data centers and rents NVIDIA and AMD GPUs through Crusoe Cloud.

   Best forTeams that want NVIDIA or AMD capacity from a vertically integrated provider with managed inference and Kubernetes.

   Adoption data

   4.9 GWCrusoe announced in June 2026 that it had contracted 4.9 GW of AI infrastructure across its data center projects and Crusoe Cloud capacity.

   Crusoe, Crusoe's Contracted AI Infrastructure Capacity Approaches 5 Gigawatts Across Data Centers and Cloud (2026): “today announced the company has contracted 4.9 gigawatts (GW) of AI infrastructure spanning its data center projects and capacity for Crusoe Cloud, its AI cloud platform.”

   Crusoe builds and powers its own AI data centers, then rents the capacity through Crusoe Cloud. In June 2026 it announced 4.9 GW of contracted AI infrastructure across its data center projects and cloud, which is a large figure but one that includes capacity built for other customers. The GPU catalog is the widest here outside the hyperscalers: NVIDIA Vera Rubin, GB300, B300, GB200, B200, H200, H100, A100 and L40S, plus AMD MI355X and MI300X. Current on-demand rates on the pricing page start at $1.50 per GPU hour for the L40S, and the page lists new rates from December 1, 2026, including $4.50 for H100, $5.40 for H200 and $9.90 for B300. Managed Kubernetes costs $0.10 per cluster hour, persistent disks $0.08 per GiB per month, and managed inference and fine-tuning are priced per million tokens. Budget against the December schedule if your work runs past that date.

   Strengths

   • +Offers AMD Instinct GPUs alongside NVIDIA
   • +Large contracted capacity of 4.9 GW
   • +Storage, Kubernetes and inference priced on the same page

   Limitations

   • −Several on-demand rates rise on December 1, 2026
   • −Newest NVL72 and Vera Rubin systems are sales-led
   • −Private company, so capacity figures are not audited

   SummaryCrusoe is a strong option for teams that want AMD as well as NVIDIA GPUs and managed services from one provider. Use the December 2026 rates when planning longer jobs.

8. 8

   Lambda

   An AI cloud that sells on-demand GPU instances, self-serve 1-Click Clusters and private superclusters.

   8.2overall

   An AI cloud that sells on-demand GPU instances, self-serve 1-Click Clusters and private superclusters.

   Best forML teams that want self-serve H100 and B200 clusters with fixed per-GPU prices for two weeks to a year.

   Adoption data

   Tens of thousandsLambda describes itself as an AI cloud infrastructure provider serving tens of thousands of customers, in its October 2026 financing announcement.

   Lambda, Lambda closes $1 billion senior secured fixed rate financing (2026): “Lambda, the Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers.”

   Lambda sits between developer clouds and the large specialists. Its 1-Click Clusters give teams 16 to more than 2,000 B200 or H100 GPUs for terms of two weeks to a year at published rates: H100 runs $6.16 per GPU hour at 16 GPUs, falling to $5.54 at 256, and B200 runs $9.86 to $8.87. Single instances are also on the page, from $0.69 per GPU hour for a Quadro RTX 6000 to $2.59 for a GH200, $4.89 for a single H100 SXM and $7.99 for a single B200, with lower per-GPU prices on 8-GPU configurations. The page states these prices take effect on November 16, 2026. Lambda is private, so it does not file results, but its October 2026 financing release says it serves tens of thousands of customers and that the loan is backed by contracts with two hyperscale customers. That lack of a hard capacity figure is why its adoption score trails the listed providers.

   Strengths

   • +Self-serve clusters with prices on the page, from $5.54 per H100 hour at 256 GPUs
   • +Wide on-demand range down to $0.69 per GPU hour
   • +Financing backed by hyperscale customer contracts

   Limitations

   • −No public capacity or revenue figures
   • −New instance prices take effect November 16, 2026, so check the effective date
   • −Year-plus cluster terms are not priced publicly

   SummaryLambda is a good fit for teams that want self-serve clusters with clear per-GPU pricing. Confirm which price schedule applies to your start date.

9. 9

   Together AI

   An AI cloud for open models that combines GPU clusters with serverless inference, fine-tuning and post-training.

   8.1overall

   An AI cloud for open models that combines GPU clusters with serverless inference, fine-tuning and post-training.

   Best forTeams building on open-weight models that want GPU clusters and managed inference from the same vendor.

   Adoption data

   500 MWTogether AI said with its $800 million Series C that it had secured commitments for more than 500 MW of compute capacity, to be financed by its new investors.

   Together AI, Announcing our $800M Series C to accelerate the shift to open-source AI (2026): “In addition to the equity capital, we have secured commitments for over 500 MW of compute capacity to be capitalized independently by our new investors to support our expected compute growth in the coming years.”

   Together AI is best known for open-model inference, but its GPU cluster pricing is among the most transparent in this list. The pricing page shows HGX H100 at $1.99 per GPU hour preemptible and $3.99 on demand, with reserved rates of $3.69 for 3 to 30 days, $3.45 for 31 to 90 days and $3.19 for 91 to 180 days. HGX H200 runs $5.99 on demand, B200 $8.19 and B300 $9.99, while GB200 and GB300 NVL72 are sales-led. Dedicated inference instances cost more, $5.49 per GPU hour for HGX H100, but include autoscaling and custom model support. Alongside its $800 million Series C, the company said it secured commitments for more than 500 MW of compute capacity, financed separately by its investors. That figure describes future supply rather than GPUs in service, so adoption is scored conservatively. The draw is having clusters, serverless inference, fine-tuning and post-training under one account.

   Strengths

   • +Clear reserved discounts, H100 from $3.99 down to $3.19 per GPU hour
   • +Preemptible H100 at $1.99 per GPU hour
   • +Strong managed tooling for open models

   Limitations

   • −Compute commitments are future capacity, not GPUs in service
   • −NVL72 systems and terms over 181 days need sales
   • −Narrower chip range than CoreWeave or Crusoe

   SummaryTogether AI is the best fit for open-model teams that want training clusters and production inference from one vendor. Teams that only need raw GPUs can find lower on-demand rates at Runpod.

10. 10

    Vast.ai

    A GPU marketplace where independent hosts and data centers rent GPUs at prices set by supply and demand.

    7.9overall

    A GPU marketplace where independent hosts and data centers rent GPUs at prices set by supply and demand.

    Best forCost-sensitive developers and researchers who can choose hardware by price and tolerate variable hosts.

    Adoption data

    20,000+ GPUsVast.ai reports more than 700,000 monthly transactions across more than 20,000 GPUs in over 40 data centers.

    Vast.ai, Vast.ai homepage (2026): “700K+ transactions/mo 20,000+ GPUs 40+ data centers 68+ GPU types”

    Vast.ai works differently from every other provider here: it is a marketplace where hosts list GPUs and prices move with supply and demand. The company reports more than 20,000 GPUs across more than 40 data centers, 68+ GPU types from an RTX 3060 to a B200, and more than 700,000 transactions a month, and it now offers GB200 systems. Buyers pick on-demand instances with guaranteed uptime, interruptible instances that Vast says are 50 percent or more cheaper, or reserved terms of one, three or six months at up to 50 percent off. Billing is per second with no minimum, and accounts start with as little as $5. The live price cards load by script, so we could not capture a US dollar quote and the price is marked pending. Adoption figures are self-reported, and hardware quality depends on the host, so check the reliability and location filters before running anything sensitive.

    Strengths

    • +Interruptible instances described as 50%+ cheaper
    • +Accounts start with as little as $5 of credit
    • +Widest range of consumer and data center GPUs here

    Limitations

    • −Live prices load by script, so we could not verify a USD quote
    • −Host quality and location vary across the marketplace
    • −Reserved terms only run 1, 3 or 6 months

    SummaryVast.ai is the budget pick for flexible workloads that can move between hosts. Production teams with strict security needs should choose a provider that runs its own data centers.

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