In the fast-paced world of artificial intelligence (AI), the hardware driving innovation is paramount – AI servers. These servers serve as the backbone for ground breaking AI applications, from natural language processing to computer vision. With AI demand skyrocketing, the market is flooded with servers, each promising unparalleled performance.
Once your AI model is trained and ready, you must host or “deploy” it. AI model hosting is the process of making your AI model accessible to other users or applications. Typically, the model exposes an API (application programming interface) that authorized users and software systems can use to communicate with your model via code.
As a simplistic overview, you can picture the hosted AI model running on a remote server (hosting environment) and awaiting input. An external application performs an “API call” and sends input data to the model. The model processes the input and returns any predictions or new data generated as output back to the external application.
Of course, in practice, things get a LOT more complicated. You typically have large AI workloads running in a distributed environment spanning hundreds (if not thousands) of servers. There are several technology layers between your model and the underlying server hardware.
AI hosting options are based on who owns and manages the different technology layers. Before making a choice, you must consider factors like performance, cost, security, the technologies you’ll have to work with, and your team’s technical capabilities.
Technology layers in AI model hosting
AI model hosting options require you to consider who manages which layer in your AI deployment stack. Let’s consider these layers.
Compute
This layer includes specialized hardware needed to speed up AI processing. CPUs handle general-purpose tasks but also require GPUs for parallel processing and matrix calculations. Specialized hardware such as TPUs (Tensor Processing Units) and FPGAs (Field-Programmable Gate Arrays) are modern advances for generative AI models. You also need high-performance network infrastructure like RDMA (Remote Direct Memory Access) and ROCE (RDMA Over Converged Ethernet) for fast data transfer between computing nodes in a distributed environment.
Storage
This layer stores interim model output, the data the model works with, model metadata, and more. Block storage, file storage, object storage, etc., are a must. You may also need high-performance file systems like Lustre, designed to work with large-scale AI compute clusters.
Compute unit
These are technologies necessary for running the AI model on the hardware. They act as an intermediary and manage the hardware resources for the model.
Containers are lightweight, portable units that package the application and its dependencies. They ensure consistency across environments. VMs are virtual machines that create several server instances on the same hardware. You may also use the bare metal option with a light operating system and no virtualization. Typically, you may have several containers running across several VMs.
Orchestration
Technologies in this layer let you scale, start, and stop underlying infrastructure based on workload demand. They can withstand hardware failures and network outages.
Kubernetes is an open-source platform for managing containerized workloads. Ray and JARK are emerging frameworks designed for distributed computing.
Environment & tools
This is the layer data scientists are most familiar with. It includes all frameworks, software libraries, and tools needed to build and run your AI models.
Deployment
Deployment is the technical term for hosting. The topmost layer in the image shows some managed deployment options. You can choose the self-managed route and purchase and organize everything you need for each layer. Or you can go the managed route and let third parties manage some of (or all) the layers for you. You could even go 'serverless', letting others manage your server for you while you just focus on your model. Cloud technologies give a lot of flexibility in this regard.
Summary of AI model hosting options
| Option | Description |
|---|---|
| Self-managed on-premises | You purchase, configure, and manage all the hardware and software layers in the AI model deployment stack. |
| Self-managed cloud | You lease some infrastructure from a cloud provider. The cloud provider manages the hardware layers (1, 2 in the diagram above), but you set up and manage all the other software configurations yourself. |
| Serverless | Your lease server infrastructure from a cloud provider. The cloud provider manages layers 1, 2 and 3, so you can run the model without thinking about the underlying server environment. You manage layers 4 and above yourself. |
| Managed cloud | You lease some infrastructure from a cloud provider. The cloud provider manages the hardware layers. The cloud provider or another third party manages some of the software layers. You can pick and choose what you manage and what others manage for you. |
| AI PaaS | AI Platform as a Service gives you fully managed layers from 1 to 5. You only focus on the model. |
Depending on your chosen solution, you need to work with different tools and technologies. You get different degrees of flexibility, convenience, and control from each.
Self-managed on-premises AI model hosting
On-premises AI model hosting requires you to first invest in server hardware. You typically have to find a vendor partner in the AI hardware space (like an NVIDIA vendor partner) who will suggest the best solutions for your use case. You can select different computing, storage, and networking solutions (e.g., NVIDIA GPUs with IBM storage) or pick a turnkey AI data centre like NVIDIA AI for Enterprise.
Next, you must install the operating system, container technologies, relevant machine learning libraries, etc. You also have to configure web servers, such as NGINX or Apache, to serve requests to the model.
This approach provides full control over the hardware configurations and security, but it also comes with responsibilities for infrastructure maintenance. It is expensive to get started and typically out-of-reach for start-ups and small organizations. It also limits flexibility and capacity. For example, you are limited in running your model in a specific location, which may increase latency for users in other locations. You also cannot scale up or down quickly and are limited to your pre-purchased capacity.
Self-managed cloud
You can lease server infrastructure from cloud providers. You only pay for the time your workload runs. You do not have to pay for idle resources or pay upfront fees, making this a more cost-effective option.
Public cloud providers like AWS, Azure, and GCP offer a range of server instances — you can pick and choose the GPU/CPU/network combination necessary to get started.
However, public cloud providers have many services and don’t cater specifically to AI workloads. Cloud tech support and services are available only for premium clients. Most importantly, cost estimation is challenging to predict. The public cloud provider may lock you into using various services — bloating your bill without realizing it. You need specific public cloud expertise to navigate and choose the best solutions for your use case.
Instead, consider looking for an AI cloud provider that provides customized service. You will get customized consultation from the start, predictable billing, and full support throughout your project.
Serverless
All three public cloud providers offer “serverless” capabilities. You can run your workloads on their servers without worrying about underlying server configuration. This is done through serverless functions like Lambda for AWS or Azure Functions for Microsoft.
However, running AI through serverless requires complex coding skills. You must integrate with several other services, including storage and API Gateway. You also have to tackle the “cold start” problem. When a serverless function hasn’t been used for a while, the cloud provider shuts down the server instance that runs the function to save resources. The next time the function is triggered, the underlying cloud technology restarts the server, loads your function, and initializes all its dependencies — causing a 7–10-second delay. The delay can be a deal-breaker for most enterprise AI use cases.
Serverless is not a practical solution for most enterprise use cases. It is suited for experimentation and early prototyping.
Managed cloud
Managed cloud is a game changer for most AI teams. Hosting your AI model requires Ops expertise, but AI teams have more development experience. Trying to ramp up on Kubernetes, databases and other deployment layer tech can take away time and energy from AI development.
Managed cloud solves this problem for AI teams. The managed service handles all configurations, scaling, and maintenance of the infrastructure layer so you don’t have to. For example, you may choose managed Kubernetes or managed SQL data. You pay for hour based usage, the same as for hardware layers. Managed cloud gives you convenience and flexibility at lower cost.
AI PaaS
AI Platform as a Service gives you an all in one AI platform with your favorite frameworks and tools ready to go. With AI PaaS, the network, storage, orchestration, and compute is handled for you. You just have to focus on training and developing your model. Typically, a few clicks in a UI based console are enough to host the model and move it to production. Most platforms also autogenerate the API’s you need.
AI platforms may be slightly less flexible in terms of the software you have to use. However, the returns in terms of productivity and cost-efficiency are very high. Your team can focus on their core tasks without getting distracted by cluster scaling, resource management, networking, etc.
Criteria for choosing the best AI model hosting option
When choosing between the different options consider the following criteria.
Inference type
Do you plan to run your AI workloads in batches or expect real-time input? In batch mode, input data is collected over time and then sent to the hosted AI model to generate predictions. In real-time mode, your model has to process incoming data with minimal latency and provide immediate responses. Batch mode requires hosting options that can scale quickly when the workload runs and then remain idle for a period without adding to your costs. Real-time mode requires hosting options that can process at high speed and provide load balancing, caching etc. to meet performance criteria. Self-managed on-premise is inefficient for batch processing. It can work in real-time if your input comes from a specific geographic area. Managed cloud or AI PaaS are preferable for both as they provide autoscaling at low latency and low cost.
Cost considerations
When budgeting, you have to consider both initial costs and ongoing costs. On-prem infrastructure typically has a high initial investment with average ROI of 3–5 years. However, you do require operational specialists on the team for ongoing maintenance.
In contrast, cloud infrastructure shifts capex to opex. Your monthly bills can be estimated upfront for increased predictability. Managed cloud and AI PaaS also lets you manage with your existing team so you don’t have the expenses of hiring specialists.
Security
AI projects in heavily regulated industries must meet compliance considerations. There are rules around where your data resides and who can access it. On-premise may be preferred if your data must sit in specific geographic locations on hardware fully controlled by you. Private cloud is another option — you can lease cloud infrastructure so that more layers are in your control. However, cost tends to be higher. Managed cloud and AI PaaS provide a happy medium for most industries. These providers can meet stringent requirements without adding to your bill, as long as they meet security criteria in managing their hardware. You will have to check your vendors security policies to determine this.
Customizability
You want hosting options that are fully customizable to your project needs. You should be able to configure (or request configurations from providers) for every layer of your deployment stack. You don’t want to be locked into a tech stack you are uncomfortable with.
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.
Top AI Hosting Platform
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. 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.