The Best Serverless GPU Providers in 2023
Discover the best serverless GPU providers for building AI products.
Thanks to ChatGPT, the craze for AI is only intensifying. With more businesses seeking to integrate this technology into their applications, the use of AI has almost become universal.
OpenAI has emerged as a go-to tool for developers who want to incorporate AI into their applications. Developers can integrate various models with just 10–15 lines of code thanks to their user-friendly OpenAI API. But when it comes to creating truly effective AI tools (building your own machine learning models) that actually work, this strategy falls short.
In order to create an AI app that truly delivers results, it is essential to follow a more rigorous approach. This includes cleaning the data, building a model, training it, making predictions, and then evaluating and improving the results.
The ML development process requires a GPU environment for training and deployment, and this is where Serverless GPU comes into play. Today, there are several vendors who provide Serverless GPU platforms that provide a simple and cost-effective way of building AI applications.
The Benefits of Serverless GPU
Building a machine learning model is time-consuming. But there are also operational challenges in running an ML workload on the cloud. First, GPU availability is limited, so it can be difficult to access a GPU server from the major cloud providers.
Second, running a GPU server is expensive: developers can expect to pay a minimum of $350 per month for a basic GPU on AWS or GCP. And finally, maintaining a server requires developers to maintain the infrastructure themselves, which requires an understanding of DevOps principles and CI/CD.
This is where serverless GPU platforms come in handy. These platforms offer a quick, cost-efficient, and low-maintenance system with all the features required to support machine learning development.
Without further ado, here are the top serverless GPU providers in 2023.
1. Beam
Beam is a powerful tool that gives developers access to serverless GPUs. One of the coolest things about Beam is the developer experience: as you develop your models, you can work locally while running your code on cloud GPUs.
In addition to providing serverless GPU, Beam provides helpful abstractions for building your ML applications, including file volumes, and the ability to schedule training jobs.
Beam also offers straightforward and detailed documentation. They provide tutorials for popular ML use cases, like Stable Diffusion, Open AI Whisper, LangChain. Whether you’re using a pre-trained model or building one from scratch, Beam has you covered.
The best part is that Beam is totally free to use for the first 10 hours; they don’t even ask for your credit card information. For those who want to test it out without making any commitments, this makes it a fantastic choice.
After that, you only have to pay $0.00059998 per second.
If you’re looking for a thorough and user-friendly platform for serverless GPU, give Beam a try.
2. Banana
Let’s take a look at Banana, which is another excellent option for serverless GPUs.
Banana provides a serving architecture that uses GPUs to process queries, as well as client-side SDKs that are easy to integrate into your code.
And despite the lack of training features, it offers excellent serverless GPU hosting. Even the documentation that Banana offers is simple to read and grasp. Additionally, there is a section called “templates” that offers a ton of scenarios using Whisper Base, Stable Diffusion, and more.
Banana also offers one hour of free hosting, which is an excellent choice for anyone looking to try it out before committing.
After that, you only have to pay $.00051992 per second. It is approximately the same price as the other serverless GPUs we’ll be covering.
In short, Banana is an excellent option for those seeking a reliable and simple-to-use serverless GPU hosting solution with excellent documentation and a diverse set of examples to choose from.
3. Cerebrium
Most of us have heard of Amazon Web Services (AWS), a well-known cloud computing software that provides a variety of services for building cloud applications. AWS SageMaker is one of these services, and it’s made to make it simple for users to create, train, and deploy machine learning models.
Cerebrium, a brand-new alternative to AWS SageMaker, has recently come onto the scene. Like SageMaker, Cerebrium helps users build, develop, and deploy machine learning models. Cerebrium, however, differs from alternatives with a few cutting-edge features.
For instance, it enables users to implement model ensembles, which are collections of models that collaborate to generate predictions with higher precision. It also provides pre- and post-processing functions that can be used to clear and prepare data before and after it is used to train or evaluate a model.
Cerebrium is a complete machine-learning platform that also enables users to transmit file inputs, uses persistent memory for processing, and saves file outputs.
The fact that it supports all of your favorite frameworks is one of its finest features. This implies that it is simple to execute customized Python code as well as PyTorch, scikit-learn, XGBoost, ONNX, TensorFlow, spaCy, and Hugging Face. This makes it simple for you to use the frameworks you’re already comfortable with while also enabling you to try out new frameworks.
Finally, new users of Cerebrium receive a $10 credit. This enables you to test out the tool risk-free and see how it can facilitate the development, training, and deployment of machine learning models.
And later, you have to pay $0.00055 per second.
4. Replicate
Now consider Replicate, another serverless GPU option worth considering.
Replicate, like Beam, provides code examples to assist users in getting started with building models. It also has excellent documentation, which includes step-by-step directions for running a model from Node.js, running a model with Python, integrating Replicate into a Next.js application, and much more.
Users can also access a collection of pre-trained models through Replicate. It’s worth mentioning, however, that customization and training options are limited.
One of the best aspects of Replicate is how simple it is to use. With just a few lines of code, you can take advantage of its powerful serverless GPU capabilities.
According to their documentation, Replicate allows you to run machine learning models without needing to understand how machine learning works.
Here’s an example of using Stable Diffusion through their API:
import replicate
output = replicate.run(
"stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
input={"prompt": "an astronaut riding on a horse"},
)Additionally, Replicate provides a large selection of models that have already been pre-trained for a variety of tasks, including Diffusion, video creation and editing, super-resolution, and image restoration. Users have a wide range of choices to choose from to meet their unique requirements thanks to the thousands of versions that are available.
Replicate’s pricing is also usage-based:
If you’re interested in learning more about how Replicate works, you can find in-depth information by clicking here.
5. Runpod.io
Let’s take a look at the Runpod serverless service, which is renowned for being one of the most cost-effective choices available, costing only $0.2 per hour. Runpod provides two cloud services: Secure Cloud and Community Cloud.
- Secure Cloud operates in T3/T4 data centers, which provide high reliability, redundancy, security, and a quick reaction time to minimize outages. It is suggested for projects with a large volume of traffic and workload.
- Community Cloud, on the other hand, is a compute cloud service intended for communities, with a decentralized network connecting individual compute providers to users, including peer-to-peer GPU computing.
Like other vendors, Runpod provides a ton of templates to use.
The cost of using Runpod depends on which computing service you choose. For Secure Cloud computing, the cost of 1x A100 80GB GPU type is $2.090 per hour, while for Community Cloud computing, it’s slightly cheaper at $1.89 per hour.
To learn more about this GPU provider, check out the Runpod website.
Hope you like it.
That’s it — thanks.
