Cheapest GPU Cloud Providers for AI (2025)

The best GPU cloud provider depends on your project needs and personal preferences. Below is a list of some affordable GPU options and their benefits to help you find a platform that fits your budget without compromising performance.

Pricing overview across GPU cloud platforms

* “Starting from” prices taken from each vendor’s public page on April 17 2025.

How to choose between cloud providers

1. Budget vs. reliability – Crowdsourced GPU cloud providers beat hyperscalers on cost but can flake on long‑running jobs.

2. Ecosystem lock‑in – AWS/GCP integrations are handy if you’re already there; otherwise outbound data fees bite when you migrate.

3. Billing granularity – Paying per minute (Thunder, RunPod) is ~40 % cheaper than hourly for bursty workloads.

4. Scale ceiling – Need 100× A100 tonight? Go Lambda Labs clusters or AWS UltraClusters; hobby clouds have stricter limits.

1. AWS, GCP, Azure, Oracle (“the big guys”)

Price highlight – AWS g4dn.xlarge (T4) $0.53/hr, p4d (8× A100) $32.77/hr

You are likely already familiar with these options; they are likely the first names that come to mind when you think about cloud. If you are looking for robust storage solutions, built-in Kubernetes support, and integration with existing cloud infrastructure, one of these is likely your best option. Additionally, if you work for a startup, these programs have generous credit offerings which often total hundreds of thousands of dollars.

Unfortunately, you pay a price for the complete ecosystem you receive—AWS, GCP, Azure and Oracle are often the most expensive cloud GPU providers, are difficult to set up, and lock you in with data egress costs. If you don’t have an existing cloud presence and want to get started quickly, it is often best to look elsewhere.

2. Modal

Modal focuses on developer experience and has earned an excellent reputation in the developer community. Modal is container-based and focused on scaling apps to production. To deploy to Modal, developers must annotate their Python code to containerize and scale certain functions. Modal is built on top of GPUs provided by Oracle Cloud, with support for AWS, GCP, and Azure. The major drawback is cost, Modal is often the most expensive cloud provider on a per-hour basis.

3. Thunder Compute

“For an 8‑hour fine‑tuning run on an A100 GPU, Thunder Compute costs $4.56 vs. $32.80 on AWS.”

Thunder Compute balances ultra-low cost (up to 75% lower than AWS or GCP) with simple user experience. Thunder's secret sauce is their GPU virtualization technology, which allows them to provide cloud instances built on AWS and GCP for a fraction of the cost. Thunder Compute focuses on on-demand virtual machines and is the smallest provider on this list. This solution is best suited for indie developers and small teams looking for cheap instances for research and development workflows.

Use Thunder Compute's VSCode extension to access a cheap A100 GPU in one click.

4. Lambda Labs

Lambda Labs sells a mix of enterprise and on-demand cloud services. Their Lambda On-Demand GPU Cloud provides access to powerful GPU clusters, while also offering colocation services for companies' AI infrastructure. Lambda Labs has carved out a niche providing clusters for large-scale AI projects and excels at projects that require a combination of cloud and on-premises hardware solutions. Lambda Labs’ on-demand instance pricing is higher than some other options on this list, with more robust reliability than crowdsourced options.

5. TensorDock

TensorDock offers a decentralized marketplace for GPU cloud instances, with costs ~60% lower than larger providers. TensorDock provides a traditional VM-based experience for a fraction of the cost. To achieve this lower cost, TensorDock crowdsources compute, which is often less reliable than providers with dedicated data centers. Additionally, other cloud features like storage buckets are not available.

6. RunPod

RunPod focuses on providing a seamless user experience for container deployment, similarly to Modal at lower cost. They have optimized infrastructure for low cold start times and auto-scaling capabilities for efficient resource management in production inference scenarios. RunPod GPUs are notably less reliable than those sourced from traditional cloud providers and used by Modal, at a much lower cost. RunPod is a great option for quickly starting and scaling AI apps, however reliability concerns often limit long-term viability for production apps at scale.

7. Vast.ai

Vast.ai is another low-cost marketplace for renting GPUs. Vast.ai is primarily container-based, although they have begun rolling out support for traditional Virtual Machines. Similarly to TensorDock, users frequently complain about reliability and setup issues due to the crowdsourced nature of the GPUs.

Frequently Asked Questions about Cloud GPU Providers

How much does a cloud GPU cost per hour?

On‑demand rates in April 2025 range from $0.05 on marketplace spot nodes to $4+ for an A100 GPU on AWS. Thunder Compute’s virtualized A100 GPUs are $0.57/hr.

Which cloud GPU provider is cheapest in 2025?

Marketplace vendors like TensorDock and Vast.ai post the lowest headline prices, but when you factor reliability, Thunder Compute is the cheapest dependable GPU cloud running on Tier‑1 hardware.

Is there any free cloud GPU credit?

Yes. Thunder Compute gives every new user $20 in GPU credit each month, enough for ~35 hours on a T4. Google Cloud offers a one‑time $300 general credit (expires in 90 days).

What’s the difference between a GPU cloud instance and a CPU instance?

A GPU cloud instance comes with one or more NVIDIA or AMD GPUs attached, letting you accelerate deep‑learning training, rendering, or parallel compute. CPU‑only instances run the same workloads far slower.

Can I use Thunder Compute for graphics workloads?

Not today. Thunder’s virtualization stack prioritizes compute‑heavy tasks (AI, data science). For graphics or 3D rendering, choose a provider with full‑pass‑through GPUs like AWS G4 or Lambda Labs.

Conclusion

With dozens of GPU cloud providers on the market, start by matching cost, reliability, and ecosystem fit to your project. When in doubt, pick a cheaper, simpler option—you can always scale up later.

Ready to run your first notebook? → Launch a Thunder Compute GPU instance and get building.