Top 10 Nebius Alternatives in 2025

Nebius has quickly risen as a cutting-edge AI cloud provider, offering state-of-the-art NVIDIA GPUs like H100 and H200 at highly competitive prices. Its focus on AI workloads, including features like ultra-fast InfiniBand networking for multi-GPU clusters, makes Nebius attractive to teams training large models. However, Nebius isn’t the only option. Whether you seek broader global coverage, different pricing models, or more integrated services, there are several strong alternatives to consider.

What Are the Top Nebius Alternatives for Cloud GPU Computing in 2025?

In 2025, a range of cloud providers – from specialized “AI-first” platforms to tech giants – offer GPU computing services comparable to or better than Nebius. Below we highlight the 10 best alternatives to Nebius, with a focus on GPU performance, pricing, and unique features. Each alternative is evaluated for its strengths, and we’ve noted why it might be a good choice for your AI/ML needs. Read on to find the ideal cloud GPU provider for your projects.

Runpod – Best Overall Nebius Alternative

Runpod is a developer-focused cloud GPU platform that stands out for affordability, flexibility, and global scale. Runpod provides thousands of GPUs across 30+ regions worldwide , giving you low-latency access no matter where you are. It offers over 30 different GPU models, from entry-level cards to top-tier NVIDIA A100 and H100 accelerators . Pricing is extremely competitive – pay-per-second billing starts around $0.00011 per second (≈$0.40/hour) for smaller GPUs , and there are no hidden fees (Runpod eliminates data egress charges entirely ). You can spin up dedicated GPU instances (“Pods”) in under a minute, or even deploy multi-node clusters with a single click using Runpod’s Instant Clusters feature . Developers get full control of the environment with Docker images and an intuitive UI/API. In short, Runpod delivers high-performance GPUs on-demand with unparalleled cost efficiency and ease of use. Sign up for Runpod to start running your AI workloads and claim a free bonus credit for your first GPUs. Runpod is our top pick for replacing Nebius – it’s cloud GPU computing made simple.
Lambda Labs – “AI Developer Cloud” with One-Click Clusters

Lambda Labs (Lambda Cloud) is a pure-play GPU cloud provider built for AI researchers and developers. Originally known for its deep learning hardware, Lambda now offers a cloud platform used by over 10,000 research teams . Lambda Labs brands itself as the “AI Developer Cloud,” emphasizing ease-of-use and fast scaling . It provides a range of NVIDIA GPUs (including A100 and H100) with pre-installed ML frameworks, Jupyter notebooks, and InfiniBand interconnected clusters for distributed training. A major draw is Lambda’s one-click GPU cluster deployments – you can launch multi-GPU setups in minutes without complex setup. The company competes aggressively on price and simplicity, much like Nebius. (For example, Lambda’s hourly rates for high-end GPUs are significantly lower than AWS’s, making it budget-friendly for deep learning.) Notably, NVIDIA is an investor in Lambda Labs , ensuring early access to the latest GPU architectures. If you want a Nebius alternative that’s laser-focused on AI workloads and developer experience, Lambda Labs is a top choice.
CoreWeave – High-Scale GPU Cloud with Enterprise Backing

CoreWeave is the largest of the new AI-focused “neocloud” providers, often considered the hyperscaler of GPU clouds. It has raised over $7 billion in funding (with investors like NVIDIA) and already completed a successful IPO. CoreWeave offers massive capacity – it operates GPU data centers at hyperscale, serving marquee clients like Microsoft, OpenAI, Google, and NVIDIA . For enterprises needing Nebius-like performance but at larger scale, CoreWeave can deliver: it provides ultra-large clusters (thousands of GPUs) and even object storage and CPU instances for a full-stack solution. CoreWeave’s strengths include cutting-edge hardware availability (H100s, upcoming Blackwell GPUs, etc.) and advanced networking for multi-GPU training. However, its pricing is still competitive versus traditional clouds – CoreWeave aims to undercut AWS/Azure on GPU costs by specializing in a narrower product set . In Q1 2025, CoreWeave reported nearly $1 billion in quarterly revenue and projected $5B annual run-rate , reflecting the surging demand. For enterprise-scale AI projects or startups that need to scale fast, CoreWeave is a powerhouse alternative to Nebius that brings hyperscaler-level capacity with more attractive pricing.
Paperspace (DigitalOcean) – User-Friendly GPU Cloud for Individuals and Teams

Paperspace – now part of DigitalOcean – has long been known as a go-to cloud GPU platform for individuals, startups, and educators. DigitalOcean acquired Paperspace in 2023 and has folded it into its cloud offerings . Paperspace (also called Paperspace Core) offers an accessible web console to launch GPU VMs pre-loaded with ML frameworks or interactive Jupyter notebooks. It supports a wide range of NVIDIA GPUs (from older GTX/RTX cards up to modern A100s), giving users flexibility in price/performance. Over 650,000 users have used Paperspace’s platform , a testament to its ease of use and community adoption. The pricing is generally reasonable and transparent – users often comment that Paperspace’s prices feel fair for the compute power provided . It’s an excellent Nebius alternative for those who value simplicity: the learning curve is gentle, and you can get a cloud GPU workstation running in just a few clicks. While it may not offer the ultra-high-end clusters of a CoreWeave or the ultra-cheap rates of a Vast.ai, Paperspace strikes a balance between capability and convenience. It’s a solid choice for developers and students who need cloud GPUs without the complexity.
Vast.ai – Decentralized GPU Marketplace for Lowest Prices

Vast.ai takes a different approach: it’s a peer-to-peer marketplace for renting GPUs. Rather than owning data centers, Vast.ai lets individual GPU owners (or smaller providers) offer their hardware for rent, leading to a wide array of options and very competitive pricing. If cost savings are your top priority, Vast.ai can be an attractive alternative to Nebius. Prices start extremely low – older GPU models (like NVIDIA A40s) can rent for as little as $0.12 per hour on Vast . Even high-end GPUs are cheaper than on most clouds: for example, renters offer NVIDIA H100 PCIe instances around ~$3.69/hour (pricing fluctuates by supply & demand). The Vast.ai platform aggregates these offers and lets you pick based on price, performance, and host reputation. The trade-off is that Vast.ai is a bit more DIY: you might need to sort through different hosts and there’s less of the polished, integrated experience that Nebius or Runpod provide. Reliability can vary by host, and support is primarily community-driven. That said, Vast.ai’s market-driven model has proven effective for many researchers on a budget. For cost-conscious users or short experiments where occasional preemptions are acceptable, Vast.ai can dramatically cut your GPU cloud bill while still giving you access to powerful hardware.
Crusoe Cloud – Sustainable GPU Cloud with Green Energy

Crusoe is an up-and-coming GPU cloud provider distinguished by its focus on sustainable and efficient infrastructure. Crusoe repurposes wasted energy (like flare gas and stranded renewables) to power its data centers, making it a “green” alternative in the AI cloud space. The company’s core business originally involved building data centers at energy sources, and now it’s applying that to AI with high-performance GPU servers . Crusoe has invested heavily in expansion – it announced 4.5 GW of natural gas contracts to fuel its AI centers and raised about $600 million to grow its cloud in late 2022 . In practice, Crusoe Cloud offers top NVIDIA GPUs (A100s, H100s, etc.) with an emphasis on efficiency and renewable-backed compute. They provide an API-driven platform and even managed services for orchestration. While not as famous as CoreWeave or Lambda, Crusoe is gaining attention from organizations that value energy sustainability alongside performance. If you’re considering Nebius but prefer a provider with a carbon-reduction mission (and U.S. data center presence), Crusoe Cloud is worth a look. It delivers the horsepower for AI while turning waste energy into useful work, aligning with the growing push for eco-friendly AI computing.
Amazon Web Services (AWS) – GPU Giants with Broad Ecosystem

AWS is the default cloud for many, and it remains a viable alternative to Nebius for GPU workloads – especially if you need the extensive AWS ecosystem. AWS offers a variety of GPU instance families on EC2 (e.g., P3, P4d, G5 instances) supporting NVIDIA V100, A100, and newer GPUs. One big advantage is scale and integration: AWS can spin up UltraClusters linking thousands of GPUs with high-speed networking, ideal for the largest training runs . Moreover, AWS integrates GPUs with services like S3 storage and SageMaker for end-to-end machine learning workflows . The downside is cost – AWS’s on-demand pricing for premium GPU instances is much higher than specialized providers. (For example, an EC2 P4d instance with 8×A100 GPUs runs about $32.77 per hour , whereas Nebius or Runpod might offer comparable H100s for around ~$3/hour.) AWS does have spot instances and savings plans to reduce costs, but even then, you typically pay a premium for the reliability and convenience of AWS. If your team already relies on AWS or needs the robust security, compliance, and variety of services it provides, AWS could be the sensible choice – just be prepared for the higher price tag on GPU hours. It’s essentially trading cost for the comfort of the most mature cloud provider.
Google Cloud Platform (GCP) – Flexible GPU Instances with Cutting-Edge AI Offerings

Google Cloud is another hyperscaler alternative, distinguished by its flexible instance configurations and Google’s AI tooling. GCP allows you to attach GPUs to custom VM shapes easily, giving more flexibility in CPU–GPU–RAM combinations than some clouds. Google keeps pace with new hardware – in fact, GCP recently launched A3 Ultra GPU supercomputers featuring the latest NVIDIA H200 GPUs for extreme AI performance. This means as of 2025, Google offers everything from T4 and A100 GPUs up to H100 and H200 instances for customers. In addition, Google has unique offerings like TPUs (if you’re open to non-GPU accelerators) and managed AI services (Vertex AI, etc.). The pricing on GCP’s GPU instances is comparable to AWS (high for on-demand), but Google sometimes edges out with sustained-use discounts or burst capacity for researchers (they historically had free credits on Kaggle/Colab). Google Cloud’s strength lies in its AI ecosystem – tight integration with TensorFlow, BigQuery, and Google’s research innovations. If your workflow benefits from Google’s stack or you need the latest GPU technology as soon as it’s available, GCP is a strong alternative. Just like AWS, however, expect to pay more per GPU-hour than on Nebius or other specialized platforms.
Microsoft Azure – Enterprise-Ready Cloud GPUs (Now with H100s and Confidential Computing)

Azure offers a range of GPU VM sizes under its NV- and ND-series, and it has aggressively expanded its high-end AI infrastructure. By 2025, Azure provides NVIDIA A100 and H100 GPU instances (e.g., NDv5 series) and even previewed VM sizes with the newer H200 GPUs . One notable aspect is Azure’s focus on security and enterprise features – it was the first to introduce confidential GPU VMs with H100, enabling encryption-in-use for sensitive AI workloads. This is a differentiator if you work with confidential data and require trusted execution environments along with GPU acceleration. Azure’s GPU offerings integrate with the broader Azure ecosystem: you can use Azure Batch for scheduling jobs, AKS for Kubernetes with GPUs, and services like Azure ML or OpenAI Service tied into Azure’s GPU backend. Like other hyperscalers, Azure’s GPU pricing is on the higher side (e.g. an 8×H100 VM lists around $10/hour per GPU on Azure’s price sheet) – but it does offer enterprise-level support, SLAs, and hybrid cloud options (Azure Stack). If your organization is Microsoft-centric or needs features like Active Directory integration, enterprise contracts, and global data centers for compliance, Azure is a credible Nebius alternative. It delivers on performance and adds a layer of enterprise security that specialized clouds might lack, albeit at a premium cost.
Oracle Cloud Infrastructure (OCI) – High-Performance GPUs with Aggressive Pricing Deals

Oracle Cloud has emerged as a dark-horse contender for AI workloads thanks to its strategic partnership with NVIDIA. Oracle offers GPU instances in both VM and bare-metal form – including NVIDIA A100 (bare metal with 8×A100) and the latest H100 GPUs – with robust networking (OCI supports RDMA over Converged Ethernet for clustering GPUs ). Oracle’s list prices for GPUs tend to be somewhat lower than AWS/Azure; for example, a bare-metal server with 8×H100 80GB is officially around $10.00 per GPU hour on OCI . Oracle also often negotiates custom discounts for large commitments (they’ve attracted big AI startups like Cohere as customers ). The benefit of OCI as a Nebius alternative is that you might achieve hyperscaler-level infrastructure at a lower cost, especially if you need bare-metal access or want to avoid virtualization overhead. OCI’s ecosystem is smaller than the Big 3 clouds, but it covers the basics (object storage, container orchestration, etc.) and has no egress fees for data in many cases . The main caution is that Oracle’s cloud, while much improved, can be a bit less user-friendly – some users find the interface and onboarding process bureaucratic or complex . If you have patience and possibly an Oracle account team to assist, OCI can deliver serious GPU power. It’s an option to consider for large-scale AI deployments where getting a better price/performance deal is worth navigating a different cloud environment.

Conclusion: Choosing the Right GPU Cloud Provider

The best Nebius alternative ultimately depends on your specific needs – budget, scale, workflow, and support requirements. For many AI teams, a specialized platform like Runpod offers the sweet spot of low cost, ease of use, and performance, making it an excellent first choice. Others might opt for Lambda or CoreWeave for cutting-edge features, or a hyperscaler like AWS/Azure if deep integration and enterprise services matter more. It’s wise to evaluate factors like pricing (on-demand vs reserved costs), data center locations, instance flexibility, and any value-added services (storage, MLOps tools, etc.) that come with each provider.

If you’re unsure where to start, we recommend giving Runpod a try – with its user-friendly interface and pay-as-you-go model, you can experiment without heavy commitments. By leveraging these Nebius alternatives, you can find a cloud GPU provider that best fits your project’s needs and accelerate your AI initiatives with confidence. Here’s to your next breakthrough on whichever platform you choose – happy computing!

Internal Resources: For further reading, check out our guide on [How to Choose a Cloud GPU for Deep Learning] which covers key factors in evaluating GPU clouds, and explore Runpod’s product pages (e.g. Instant Clusters for multi-GPU scaling) to learn more about our offerings. Feel free to reach out to the Runpod team if you have any questions – we’re here to help you make the most of GPU cloud computing. Good luck with your AI projects!

Sources:

Network World – “Neoclouds roll in, challenge hyperscalers for AI workloads” (June 2025)
Runpod Documentation and Blog – Runpod pricing & features
Medium – “AWS vs Nebius: Choosing the Right Cloud GPU Provider” (Dec 2024)
PoolCompute – Nebius vs Vast.ai pricing comparison
AIMultiple Research – “Top 30 Cloud GPU Providers in 2025”
NVIDIA Blog – “H100 GPUs now GA in Azure” (Sep 2024) (for Azure updates)
Oracle Cloud Documentation – GPU instance pricing and features

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