Feature
When you are debating where to host your AI workloads, you might need a decision tree, a thinking cap and AI to help you. This is a big decision, and it’s an important one.
You have to consider cost, speed, privacy and a host of other technical matters. But this isn’t merely a technical or financial decision. It is a strategic one.
There are many advantages to moving to the cloud but there are also reasons for keeping your AI implementation on your own premises. Whether you choose one of these options or opt for a mix of the two solutions depends on what your business does, your budget, future plans, speed and power needs, security and privacy concerns and how much control and customization you need.
Spend Big Now or Pay More Later
If you buy your own servers, load them with your own tools and host your own AI implementation, you have to buy a lot of equipment up front. You will also have to set up, maintain and upgrade that equipment. This involves a considerable initial capital investment, as well as your own labor. You will need space, too, to house this equipment. You will also be responsible for protecting your data from threats.
“On-prem setups require a hefty initial investment in hardware, cooling systems and energy solutions, and lack the flexibility that the cloud offers for scaling during peak demand periods,” said Arsalan Zafar, CTO at Deep Render.
With a cloud-based solution, you pay a third party to manage that equipment, store your data, keep that data safe and ensure it's always available to you. This is that company’s bread and butter and, if you choose carefully, it’s likely they are staffed and equipped to do it well. They will also charge you for that service, and you will have no control over those costs, especially once you're committed. You may not have to pay the initial costs of equipment with a cloud solution, but that company is in business to profit, so you may pay more in the long run.
“Typically, the cloud offers pay-as-you-go flexibility,” explained Fergal Glynn, AI security advocate at Mindgard. “However, it comes with unpredictable expenses (as the data grows). On-premises might have higher upfront costs, but it can become cheaper over time for stable, long-term workloads. A hybrid model balances both! It can leverage cloud for demand spikes while keeping core operations on-premises.”
Related Article: Why 90% of Companies Are Rethinking Cloud Strategies
Short-Term Projects, Long-Term Decisions
The ability to scale up or down based on need is one reason enterprises move, at least in part, to the cloud. “Cloud-based solutions offer great scalability, flexibility and access to specialized hardware,” noted Zafar.
If you're running a short-term project or training a model, turning to the cloud might make the most sense, despite the higher long-term costs. Buying powerful hardware probably does not make sense if the planned AI implementation is not part of your core business operations and may not continue long enough to realize the financial benefits of that investment over time. Plus, pulling back your AI investment when your short-term project is complete is easy if you run that project in the cloud.
The Best of Both Worlds — With Caveats
If security and privacy are big concerns for the data you plan to use in your AI efforts, you might prefer to keep it in-house and maintain complete control over when, how and by whom it is accessed.
“On-prem AI deployments give companies more control over their infrastructure and security, which is important for those with strict data privacy needs,” says Zafar.
But you might not need to house everything on-premises. When it comes to security and managing the privacy of data used in your AI implementations, “most enterprises now favor hybrid setups,” claimed Glynn. “They use on-premises for security-sensitive operations (e.g., healthcare data) and cloud for resource-heavy model training. Opting for this strategy can cut cloud costs by up to 50% while maintaining control over critical data.”
A hybrid solution introduces complexity to your compute environment, though. Integrating and managing both environments can be a challenge. But this approach can offer a tempting balance between control over data, costs and uptime and recovery speeds.
Zafar added that a hybrid approach "allows businesses to run sensitive workloads on-prem while leveraging the cloud for scalability during high-demand times, helping to balance cost and performance.”
Latency Could Make or Break Your AI
Performance is a big consideration when it comes to any decision to use on-prem servers versus the cloud for AI. Your hardware choices will be driven by your particular need for speed. Using a cloud solution — where the vendor takes care of hardware upgrades and investments — is tempting if performance is important. But proximity to servers also affects the time it takes for data to travel between the source and server. The potential for delay here, called latency, is one reason businesses — especially those in healthcare and other areas where real-time data and monitoring is critical — move their servers close to, or onto the premises of, the day-to-day work.
Using your own equipment on your own property gives you full control over the hardware as well as your proximity to it. “It is ideal for real-time analytics or any such task that requires low latency,” said Glynn. “But when it comes to intensive workloads (that require parallel processing), choosing the cloud is the better option as it can scale effortlessly.”
You don’t have to choose either/or, though. You can pick and choose the operations you host in house and those that you send to the cloud. “They can keep critical operations local and offload intensive training to the cloud,” says Glynn.
Related Article: The Cloud's Pivotal Role in AI and Business Intelligence
Power-Hungry AI and Your Energy Bill
AI is a hungry beast. It sucks down so much power that it is affecting the global power grid, causing it to groan under the weight. If you are concerned about the planet, you might also be concerned about the detrimental impact of your AI implementation on it. Even if climate is not your priority, your AI’s power use will still hit you where it hurts — the budget.
“AI workloads, particularly those using power-hungry GPUs, lead to higher energy consumption, prompting many data centers to invest in energy-efficient technologies and renewable energy sources,” said Zafar.
This is certainly something to think about when purchasing equipment for an on-premises AI effort. If this is a big concern, though, consider the cloud more seriously than an on-premises solution. According to one report, you're likely to find nearly 80% energy savings from using the cloud for business applications compared to on-premises infrastructure.
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When You Want Total Control
If you want to build your dream system, change it whenever you like and control every aspect of your AI implementation, you're a candidate for an on-premises solution.
If you run your AI in house, you're free to make any customizations you like. Store your data with an air gap. Keep your machines under lock and key and allow only key personnel to have access. Use the greenest — or most powerful — equipment you can find. Optimize low-energy machines to save power.
If you build your own system, the world is your oyster — limited only by your budget and skills. You will not have to beg for features, pay for extra data storage or negotiate for anything.
