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

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.

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

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

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

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

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!

Eli Mernit
Published January 11, 2025