On-Premises vs. Cloud: where does your Enterprise AI perform best?
When companies are about to implement a larger enterprise AI for the first time, a fundamental decision must be made: Where should the AI software be hosted? In your own IT infrastructure (On-Premises) or in the Cloud? The choice of the right deployment environment is a strategic decision with far-reaching consequences for the costs, performance, security, and agility of your AI initiatives. In this article, we outline the key considerations, advantages, and disadvantages.
Why is the right hosting for Enterprise AI so important?
The right hosting for enterprise AI is critical for several key reasons:
- Performance and speed: AI systems require computational power. Low latency is often crucial during operation. The hosting must efficiently provide both.
- Data volumes and access: AI thrives on large amounts of data. Hosting must store, manage, and provide quick access to this data.
- Scalability and costs: There are times when the AI system is used more or less. The hosting must allow flexible scaling up and down to ensure performance with optimized overall costs, as AI resources are expensive.
- Security & compliance: AI is often trained with sensitive data. The hosting determines control over data, its location, and compliance with data protection regulations (e.g., GDPR).
In short: A wrong hosting choice can lead to poor performance, high costs, delays, and security gaps, thus jeopardizing the success of the AI project and the overall company.
What does On-Premises mean for Enterprise AI?
On-Premises (also: On-Prem) means that you operate your entire AI infrastructure — servers, storage, network components, and the AI software itself — within your own data center or at least under your direct control. This is also called hosting.
“On-Premises” does not necessarily mean that your own IT team must build and manage the entire infrastructure. There are specialized providers who can design, implement, and sometimes even operate a tailored On-Premises solution for your enterprise AI directly in your data center or at your desired location. This allows you to benefit from the advantages of data control in an On-Premises environment without having to manage the entire complexity internally.
Benefits of On-Premises for AI
Hosting AI On-Premises offers several interesting advantages:
- Maximum control & data sovereignty: You have full control over your hardware, software, and especially your data. This is crucial when dealing with highly sensitive information or strict regulatory requirements (e.g., GDPR), where you need to know exactly where your data is physically stored and processed. Certain industries, such as banking, healthcare, and the public sector, prefer On-Prem hosting for this reason.
- Customization: You can tailor the hardware and software precisely to your specific AI workloads.
- Potential cost savings (in the long run): With very high and constant utilization, long-term operational costs may be lower than with usage-based cloud models, as initial high investments are amortized over time.
- Independence from internet connection: The core operation is not dependent on external network connectivity (although data feeds, etc., often are).
ONTEC AI provides an AI platform that allows companies to combine specialized modules to set up their own enterprise AI. We also offer hosting consultation, help in developing your On-Prem solution, and gladly provide support and maintenance.
Drawbacks of On-Premises for AI
On-Prem hosting for AI also comes with some challenges that should be considered upfront:
- High initial investments: The purchase of powerful hardware, licenses, and infrastructure setup requires significant upfront investments.
- Scalability: Scaling performance is cumbersome, expensive, and slow. If more computational power is needed for a large training model in the short term, it is hard to implement.
- Maintenance and expertise: Specialized personnel are required for setting up, maintaining, updating, and securing the complex AI infrastructure. This means a regular time investment and that a trained expert must be available at all times.
- Slower access to innovations: Cloud providers often integrate the latest hardware accelerators and AI services faster.
What does Cloud hosting mean for AI?
With the cloud model, you rent the IT resources (computational power, storage, specialized AI services) from a cloud provider. Major cloud providers include AWS, Google Cloud, or Microsoft Azure. Cloud access is via the internet.
Benefits of the Cloud for AI
Hosting an AI system in the cloud offers some interesting advantages:
- High scalability & flexibility: You can scale resources almost unlimitedly, often within minutes, both up and down. Ideal for variable AI workloads such as model training (high load) and inference (often lower but fluctuating load).
- Lower initial costs: No large investments in hardware. Typically, you pay only for what you use (Pay-as-you-go).
- Access to specialized hardware & services: Cloud providers offer easy access to the latest GPUs, TPUs, and optimized AI platforms (e.g., SageMaker, Vertex AI, Azure Machine Learning), which accelerates development and deployment processes.
- Lower maintenance effort: The provider handles the maintenance of the underlying infrastructure, updates, and often security aspects.
- Global reach: Easy deployment of AI applications for users worldwide.
Drawbacks of the Cloud for AI
There are also important factors against hosting an AI system in the cloud:
- Ongoing costs: With consistently high utilization, usage-based costs can become higher over time than an amortized On-Prem solution. Cost control is crucial.
- Data privacy & compliance: Although providers have many certifications, you relinquish direct control over the physical location of your data. This requires careful contract review and configuration, especially with regard to GDPR.
- Vendor lock-in: Using specific cloud services may make it difficult to switch providers later.
- Internet dependency: A stable and fast internet connection is required. Latency can be an issue for some real-time AI applications.
LLM Hosting
A special area of AI hosting is LLM hosting: LLM hosting refers to hosting large language models (LLMs) on servers or cloud platforms to enable their use and access. These models are very resource-intensive and require specialized hardware and software infrastructure to operate efficiently.
LLM hosting is currently very relevant for many companies and can be particularly expensive.
- Hardware is very expensive for hosting cutting-edge LLMs. It only pays off if used sufficiently.
- Technology changes rapidly. If hardware is purchased now, it may be outdated in a few years. Furthermore, smaller LLMs may soon become more efficient and hosted on cheaper hardware.
- In comparison, token-based usage may be cheaper, especially in the PoC phase or at the beginning of implementation.
For data sovereignty, LLM hosting is particularly important. A middle ground could be to use LLM hosting from a European provider or rent GPU hosts.
On-Premises vs. Cloud: Key factors in comparison
In this overview, we summarize the pros and cons of hosting an AI system in the cloud vs. On-Prem:
| Factor | On-Premises | Cloud | Notes for AI |
|---|---|---|---|
| Cost | High (Initial), potentially low (ongoing) | Low (Initial), potentially high (ongoing) | AI often needs expensive specialized hardware (GPUs) → Cloud is often cheaper to start |
| Scalability | Difficult, slow, rather expensive | Easy, fast, flexible | Essential for AI training and variable loads |
| Performance | Dedicated but hardware-limited | Access to high-end hardware, but may be shared | Cloud offers easy access to the latest GPUs/TPUs |
| Security/Data | Full control, own responsibility | Provider-dependent, certifications, data external | Strong consideration for sensitive training data; cloud security is often very high |
| Control/Customization | Maximum | Low over basic infrastructure | On-Prem allows deeper hardware optimization (if needed) |
| Maintenance/Management | High, requires in-house expertise | Low/handled by the provider | Relieves IT teams, focus can shift to AI models |
| Technology Access | Manual, slower | Fast, access to latest services & hardware | A key advantage of the cloud for the fast-moving AI field |
Is there a middle ground? The hybrid approach.
Yes, many companies opt for a hybrid approach. By combining On-Prem and Cloud, companies could:
- Keep sensitive data and core applications On-Prem.
- Perform computationally intensive training phases for AI models flexibly in the cloud.
- Run less critical AI applications or those with highly fluctuating loads entirely in the cloud.
Conclusion: What is the right choice for your AI?
The decision between On-Premises and Cloud (or Hybrid) depends largely on your specific requirements, priorities, and resources:
- Do you prioritize maximum control, data sovereignty, and have constant, high workloads as well as the necessary budget and expertise? Then On-Premises may be the right choice for you.
- Do you need flexibility, quick scalability, access to the latest technology, and want to avoid high initial investments? Then the Cloud is likely the better choice.
- Do you have mixed requirements or want the best of both worlds? Consider a hybrid approach.
Both models have their pros and cons, especially when it comes to the specific needs of AI applications, which are often computationally intensive and process large amounts of data.
Carefully weigh the pros and cons while considering the specific requirements of your AI applications.