If you signed up for a paid subscription plan before November 20, 2017, please see our Legacy Plans FAQs.

Please see here for Billing FAQs

Which Plan is right for me?

Beginner Plan

If you're just getting started with deep learning, the Beginner plan is for you. It is free! You can create unlimited public projects and datasets, and can run 1 job at a time. It comes with 20 hours of CPU compute time per month.

If you want to run your jobs with GPU support, you can purchase GPU Powerups.

Data Scientist Plan

The Data Scientist plan is for, well, data scientists. It offers private projects/datasets and higher levels of job concurrency and storage. As in the Beginner Plan, it comes with 20 hours of CPU compute time per month. You can also purchase Powerups to add GPU compute hours to supplement your plan.

For more details on our different plans, visit our pricing page.

What is in the Trial plan?

All new users get a free 2 hour GPU Powerup for a 14 day trial period.

Once you've signed up, take FloydHub for a spin with our Quick Start Guide or Jupyter Notebook Guide!

How can I run GPU jobs?

To run GPU jobs, you can purchase GPU Powerups from your Powerups Dashboard.

Do the plans come with preemptible or dedicated instances?

The CPU compute hours included in your plan (Free or Data Scientist) are preemptible instances. This means that there is a small chance that your job will be terminated without notice. In practice, this happens infrequently and this is perfect for most users. If you need dedicated instances for your jobs, you can buy the GPU+ or CPU+ Powerups.


What is job concurrency?

Job concurrency is the number of jobs you can run in parallel. Each plan has a limit on the number of concurrent jobs you can run. For example, in the Beginner plan, you can only run 1 job at a time. In the Data Scientist plan, you can run up to 8 jobs in parallel.

Having a higher concurrency is useful when you want to parallelize your training, for example while hyperparameter sweeping.

What will happen to my running job when I run out of computing credits?

You job will be shutdown immediately when you run out of computing credits.

If you run long-running jobs and expect them to exceed the computing hours offered by your plan, you can purchase Powerups.

You can also enable auto-refresh on your Powerups to ensure your long-running jobs are never killed because you ran out of computing hours. We'll automatically refresh your selected Powerup so that your job can continue running.

Preemptible vs. Dedicated Instances

Preemptible Instances

Preemptible instances have medium job uptime SLA of 98%. This means that there is a small chance that your job can be terminated (preempted) at any point during its runtime by FloydHub if it requires access to those resources for other, higher priority tasks.

Preemptible instances (CPU / GPU) offer top notch compute at affordable prices, in exchange for fault tolerance.

Note that SLA refers to what we can guarantee. In practice, this happens infrequently. Historically, less than 0.1% of jobs run on FloydHub have encountered interruption. However, you need to be aware that there is the possibility.

Why Do You Use Preemptible Instances?

To be able to offer you compute at a much lower cost.

We have a fixed pool of resources that we have to allocate amongst all our users. Some of our users require dedicated instances and are willing to pay the premium for uninterrupted access. But, the majority of our users can tolerate a 98% job uptime SLA for the significant price savings that preemptible instances offer.

Will I Get a Refund if My Job is Preempted?


Our preemptible instances have a 98% job uptime SLA. By using them, you are accepting a small chance of your job being terminated without notice, in exchange for paying a much lower price than dedicated instances.

How Will I Know When My Job Is Preempted?

Your job's state will turn from Running to Preempted. We will send you a notification informing you about this. Unfortunately, we are currently unable to warn your ahead of time of an impending preemption.

What is the SLA of Preemptible Instances?

Preemptible instances have 98% job up time SLA.

Dedicated Instances

Dedicated instances have high job uptime SLA of 99.95%. Use dedicated instances for your jobs if they are critical or not fault tolerant. You can purchase '+' Powerups (CPU+ / GPU+) to utilize dedicated instances.

Why do you need Dedicated Instances?

If your job is not fault tolerant and cannot withstand a small (<2%) chance of your job being shutdown without notice, you should use our "+" Dedicated instances.

Price sensitivity also plays a factor - dedicated instances are more expensive than premptible instances.

Given that deep learning models typically train over long periods of time, it is good practice to build your application to be fault tolerant by regularly checkpointing your training.

What is the SLA of Dedicated instances?

Dedicated instances have 99.95% job up time SLA.

What is the difference between GPU vs. GPU+ and CPU vs. CPU+?

GPU and CPU are preemptible instances. GPU+ and CPU+ are dedicated instances.


What are Powerups?

Powerups are addons that offer you the flexibility of purchasing compute or storage depending on your needs. You can buy Powerups for additional CPU or GPU computing hours, or for additional storage, from your Powerups Dashboard

What Powerups should I buy?

This depends on your computing needs. We offer multiple tiers of Powerups:

  • Preemptible vs. Dedicated: CPU / GPU are affordable preemptible instances, CPU+ / GPU+ are high-reliability dedicated instances.
  • 10 vs. 50 vs. 100 hours: Purchase a pack that suits your computing needs. Note that the larger packs offer compute at a much cheaper rate/hour than smaller packs.
  • Auto-refresh: You can enable auto-refresh on any pack.
  • Storage Powerups: You can purchase storage Powerups to supplement your plan's storage limits.

If you are just starting out and need more computing hours than your plan offers, you can start with the GPU10 Powerup.

If you run long-running jobs, you should purchase the GPU100 with auto-refresh enabled, to ensure that you never run out of computing credits.

If you run critical jobs that are not fault-tolerant, you should purchase the GPU+ Powerup.

How can I buy Powerups?

You can purchase them from your Powerups Dashboard

Why would I enable auto-refresh?

Auto-refresh ensures your long-running jobs are never killed because you ran out of computing hours. We'll automatically refresh your selected Powerup so that your Job can continue running.

Can I buy a Powerup if I am in the Free Plan?


Do Powerups expire?

Yes. Compute Powerups are valid for 1 year from the date of purchase.

How will my Powerups be used?

Your compute hours will be consumed in the following order:

  • Hours from your subscription plan
  • Hours from free credits
  • Hours from Powerups


How much storage do I get?

Each plan comes with its own storage limit. Please see the feature comparison table for details.

What counts against my storage?

Storage is consumed by the datasets that you upload, your code and the data that your jobs output.

Note that you are only responsible for the data that you own. For example, if you use a public dataset in your job, you won't be charged for it.

Can I buy more storage than my plan offers?

You can purchase Storage Powerups to increase your storage limits from your Powerups Dashboard

Help make this document better

This guide, as well as the rest of our docs, are open-source and available on GitHub. We welcome your contributions.