# Top 5 AI Hosting Platforms

Eli Mernit  
January 11, 2025  
4 min read

With AI taking over the business world, there are a variety of platforms to host AI-powered apps. Whether you’re a startup deploying your first models, or a large company doing traditional data science, choosing the right AI hosting platform is an important step in the development process.

In this article, we’ll offer a breakdown of the top AI hosting platforms, and offer a roadmap for choosing the right AI hosting platform for your project.

## [Beam](https://beam.cloud/)

Beam is a serverless GPU platform. Developers add simple decorators to their code, and Beam automatically runs the code on GPUs in the cloud.

It can scale workloads from 0->100s of GPUs, and pricing is usage-based. Beam provides support for web endpoints, task queues, realtime websockets, scheduled jobs, and one-off functions.

```python
from beam import endpoint

@endpoint(gpu="A100-40", gpu_count=2, keep_warm_seconds=60)
def inference():
    llm = vllm.LLM(model="facebook/opt-125m")
    return {"prediction": llm.generate("The future of AI is")[0].text}
```

Beam supports both training and inference workloads and integrates with popular frameworks like TensorFlow, PyTorch, and Hugging Face.

## [Lambda Labs](https://lambdalabs.com/)

Lambda is a great option for low-cost GPU hardware. Lambda offers on-demand GPUs, like H100s and A100s.

The main feature of Lambda is hardware – you won’t get a full orchestration experience like Beam, or pre-built APIs like Replicate, but you will get hardware at an excellent price.

Instances are provisioned quickly, and there’s usually good availability across a variety of regions, like us-east, us-central, and asia-pacific and Europe.

One downside of Lambda is the network speed – private internet speed is often as low as 1gbps, which makes it tough to read large files at runtime.

Otherwise, Lambda strikes a good balance between convenience and affordability.

## [Runpod](https://runpod.io/)

Runpod offers on-demand GPUs. Their primary focus is hosting Docker containers on GPUs, which is a great option for running existing Docker apps on the cloud. Runpod offers a variety of hardware, like the H100, A6000, and AMD M300i GPU.

They also offer an assortment of out-of-the-box templates, that make it easy to quickly run popular ML apps as APIs.

Runpod is one of the cheaper options of the bunch. A downside is that network speed is often volatile, which can be problematic for high-throughput workloads.

But it’s a great option for running apps on a huge range of GPUs, and it’s easy to get started. Runpod makes it easy to get high-performance compute without long-term commitments.

## [Together.ai](https://together.ai/)

Together is a popular choice for using pre-built model APIs, such as Llama3, Flux, and Mixtral. Together focuses on serving enterprise-grade APIs for off-the-shelf models, and is a solid platform for running apps that require minimal customization.

Together also offers a fine-tuning service, so it’s a great platform if you’re willing to double-down on the most popular open-source models.

## [Paperspace](https://paperspace.com/)

Paperspace has been around since 2015 and is a classic player in the space. Paperspace offers GPU-backed notebooks, and also includes templates for popular use-cases. Paperspace offers a variety of hardware options, including the H100, A100-80, and A6000. You can also run CPU workloads on Paperspace notebooks, with pricing billed by the hour.

Paperspace is a solid choice for use-cases ranging from traditional data science, to high-performance ML inference on GPUs like NVIDIA H100s.

## Main Factors for Choosing an AI Hosting Platform

- **Prototyping Speed.** If you’re building a custom model or pipeline, Iteration speed is key to building a successful app. You’ll want to make sure you can make changes to your models quickly, with minimal build or wait times for each deployment.
- **Distributed File Storage.** Most AI apps require access to large files, like model weights. In order to achieve fast performance during inference, it’s important that these files are cached on a server geographically close to the GPU serving the requests. Ensure that the tool you choose has access to cloud storage, ideally at the edge.
- **Access to hardware.** If you’re experimenting with data, you’ll want access to a variety of hardware, including GPUs and CPUs. Workloads aren’t always the same, so you’ll want small CPU or GPU machines for lightweight tasks, and GPUs with a lot of VRAM for running state-of-the-art LLMs or training your own neural networks from scratch.
- **Reliability.** For mission-critical jobs, reliability is key. If you’re training models, you’ll want to ensure that the environment is robust enough to stay connected during long-running jobs. It’s incredibly frustrating to be 8 hours into a training job, only for the environment to suddenly crash and lose your work. And if you’re doing inference, consistently low-latency is key.

## Conclusion

Whether you’re using pre-built AI models or training your own from scratch, there are a variety of quality AI hosting platforms available in the market.

Today, AI hosting platforms often include powerful features like file storage, pay-per-use billing, model templates, and even tools to automatically fine-tune models by providing a simple dataset file.

We hope this guide has given you a framework for deciding which AI hosting platform to choose for your project.

If you’d to get started hosting your own AI apps, you can [signup for Beam and get started today](https://beam.cloud/)!

Eli Mernit  
Published January 11, 2025