All Cloud GPU Providers We Track — Updated 4月 2026

The Complete Cloud GPU Provider Directory — Every Provider, Unfiltered

Our homepage shows a curated ranking of the top cloud GPU providers sorted by Trustpilot score. This page is different — it is the full, unfiltered directory of all 8 cloud GPU providers we monitor, including newer platforms, niche specialists, and providers that may not crack the top rankings but still excel for specific workloads or budgets.

This page is built for engineers and researchers doing deep comparison — evaluating lesser-known providers, hunting for a specific GPU model or pricing structure, or benchmarking a provider someone on your team recommended. Use the filters above to slice the full list by GPU model, pricing, features, and more.

How to Research Cloud GPU Providers Using This Directory

Start with the filters to narrow down providers that match your requirements — GPU model availability, pricing tier, billing granularity, or specific features like Kubernetes support or serverless inference. Once you have a shortlist, click into each provider's profile page for the full breakdown of their GPU inventory, pricing, infrastructure, and developer tools.

When evaluating providers beyond just their Trustpilot score, pay attention to the combination of signals. A provider with a 4.3 rating and thousands of reviews may be a safer bet than one with a 4.9 rating and only a handful of reviews. Similarly, providers that have been operating for several years have survived GPU supply crunches and proven their reliability under pressure.

Deciding between two providers? Use our head-to-head comparison tool to see every data point side by side — GPU models, pricing, billing granularity, interconnects, developer tools, and more — so you can identify the differences that actually matter for your workload.

Finding the Right Cloud GPU Provider for Your Workload

The best cloud GPU provider for you depends entirely on what you are building. An ML engineer training a 70B parameter LLM, a startup deploying a real-time inference API, and a researcher running experiments on a tight budget all need fundamentally different things from a GPU cloud. That is why the full directory exists — to let you find providers that match your specific requirements.

Use our guide pages to explore specific criteria in depth: providers with NVIDIA H100 or A100 GPUs, spot instances for cheaper compute, Kubernetes support for orchestration, serverless GPU inference, or pricing under $1/hr.

For workload-specific recommendations, see our guides on the best GPUs for AI model training, inference and model serving, and fine-tuning LLMs.

Understanding Cloud GPU Pricing — What to Watch For

Cloud GPU pricing is more nuanced than a simple per-hour rate. The same GPU can cost 2-5x more depending on the provider, billing model, and commitment level. When browsing this directory, consider these pricing factors:

• Billing granularity — Per-second billing can save significantly over per-hour billing for short jobs. A 15-minute training run billed per-hour costs 4x what it should. Check each provider's billing model before committing.
• Spot vs on-demand pricing — Spot instances offer 50-80%% discounts but can be interrupted. If your workload supports checkpointing (most modern training frameworks do), spot instances dramatically reduce costs.
• Egress and storage fees — Some providers charge for data transfer out and persistent storage on top of compute costs. These hidden fees can add 10-30%% to your actual bill. Look for providers offering zero egress fees or generous included storage.
• Free credits and trials — Several providers offer free GPU credits for new users. Use these to benchmark real-world performance before scaling up your commitment.

What to Verify Before Choosing a Cloud GPU Provider in 2026

The cloud GPU market is growing rapidly, with new providers launching regularly. Not every provider in this directory is equally battle-tested. When browsing the full list, verify these factors before committing your workloads and budget:

• GPU availability vs listed inventory — Some providers list GPUs they cannot actually deliver on demand. Check whether the GPU model you need is actually available or has a waitlist before planning your project timeline around it.
• Uptime and reliability track record — For production inference workloads, uptime SLAs matter. Providers with clear uptime guarantees and established track records are safer bets for mission-critical deployments.
• Support responsiveness — GPU infrastructure issues at 2 AM can halt your training run and waste compute budget. Check what support channels are available and whether the provider offers real-time assistance.
• Data security and compliance — If you are working with sensitive data (healthcare, finance, government), verify the provider's compliance certifications, data residency options, and private networking capabilities.

When in doubt, start with established providers that have strong review histories. Our FAQ pages cover detailed questions about specific providers, and the comparison tool makes it easy to evaluate any two providers you are considering.