What is an On-Premises AI Platform?

An on-premises AI platform is a platform that offers artificial intelligence (AI) services and AI application development capabilities in businesses' physical locations. Often, these platforms are isolated from connection to the internet or any third-party services. But that means, organizations have to maintain these platforms themselves, unless you choose a fully managed service.

On-premises AI platforms are especially important in today’s world, where data protection and security are key drivers for AI adoption. Because protecting sensitive data is the biggest challenge with AI models, especially with API-based solutions, where you have to send your data outside your walls. Without knowing what happens on the other side, it restricts many industries from using AI.

The solution is an on-premises AI platform setup. It allows you to meet internal requirements, regulatory compliance, and government mandates of keeping and processing data locally. Also, it gives you the highest level of control over your infrastructure, data, and assets you want to build.

Benefits of an On-Premises AI Platform

On-premises AI platforms have many advantages over cloud-based solutions. But three of the biggest benefits are control, data protection, and cost savings. Here they are explained, along with other benefits:

• Control and Ownership - On-premises AI platforms integrate with an organization’s internal infrastructure at physical locations, giving you full control over the technology and models. While this means you will need employees or extra help to install and manage the platform, this is the only way you can have total ownership of the innovations and assets you are building.
• Cost-saving - There is a larger upfront investment involved with purchasing the hardware for setting up on-premises AI infrastructure. However, our cost analysis has shown that in the long run, an on-premises setup is 60-70% cheaper than cloud-based AI solutions and up to 100% cheaper than building it yourself.
• Data Protection - Many industries process the personal data of their customers. As a result, they are governed by strict rules and regulations for how they can process this data and are often limited from using AI due to that fact. On-premises AI deployments, on the other hand, allow companies to safely process data while meeting regulatory standards such as HIPAA, GDPR, or government mandates of processing data locally.
• Cloud Dependency - There has been a massive transition from on-premises solutions to cloud in the past years for quicker access to GPUs and reduced overhead from maintenance. But with generative AI, companies are starting to see that it requires a lot of computing power, which is very expensive in the cloud. As a result, there is a transition back to on-prem. Many on-premises platforms are cloud-agnostic, reducing cloud dependencies. This is especially helpful when there are rising costs and you want to migrate to another platform.
• Mitigates Data Leakage - On-premises data should never leave a company’s walls if you want to keep it 100% safe. On-premises AI platforms, running with open-source LLM models, make sure this is not needed. All data processing happens on your premises, which mitigates data leakage into third-party models.
• Easy Integration with Existing Systems - On-premises platforms are built to easily integrate with any large and legacy systems due to their architectural design for flexibility and adaptability. This allows any organization to install them in their existing environment without extensive knowledge.
• Independence from Internet - With on-premises AI, there is no need for internet connectivity. This is beneficial in cases where there is no reliable internet connectivity, which would be crucial for cloud solutions to work. Also, in certain environments, you don’t want to have an internet connection to secure it from vulnerabilities.

5 Industries That Benefit Greatly From On-Premises AI Platforms

Industries that handle sensitive and confidential data, as well as those governed by strict data protection and regulatory compliance, benefit most from on-premises infrastructure. Especially industries such as:

1. ‍Government and Public Sector - Governments and the public sector have highly confidential and sensitive data that must be protected by all means. This data should never leave government borders. Public cloud poses risks due to being in global data centers. While on-premises AI deployment is in local environments, it provides full control and protection for this data.
2. Healthcare - Healthcare companies handle sensitive patient data, which is protected by many laws and regulations. An on-premises platform ensures that the processing of this data remains within the organization's walls to comply with regulatory standards. AI models allow processing of complex and large datasets, turning unstructured data into structured and vice versa. All within healthcare providers’ local environments.‍
3. Financial Services and Banks - Financial service providers handle customers’ data, such as names, personal IDs, and transaction details. As a result, they are also governed by strong regulatory requirements. On-premises AI makes sure that this data is processed within these rules. For financial institutions, AI can help with fraud detection, identifying high-risk individuals, and transferring data to other systems.
4. ‍Manufacturing and Industrial - AI can help manufacturers enhance operations by optimizing supply chains, warehouses, and other internal processes. Often, these systems are organization-wide and outdated, which AI can improve in various ways.‍
5. Telecommunication - Telecom companies often work with on-premises and edge environments to provide low-latency service. An on-premises AI setup is an ideal solution here, allowing telecom companies to process data, structure it, and provide data to their clients with higher quality and speed.

What to Consider Before Integrating an AI Platform On-Premises?

1. Start with the use case

The first step is collecting use cases that AI can potentially enhance or make better. This could be anything from creating generative AI-powered semantic search, automating internal manual/tedious tasks, modernizing legacy systems, or anything else.

This is an important step because on-premises setups cannot be scaled up and down as easily as in the cloud. Therefore, taking some time to prepare and understand your needs leads to better outcomes.

2. Determine your resource needs

Each use case can have different users and model requirements, which should be analyzed before you start the next step.

What to look out for:

1. Number of expected requests per day/hour
2. Concurrent users
3. Model size & type requirements
4. Latency requirements

3. Choose the right hardware

Based on your use case and resource needs, you can roughly calculate your hardware requirements.

For example, the minimum requirement to run the ConfidentialMind AI platform on-premises is a 2 x L40S. While it's hard to provide exact numbers of users that can run with this setup due to user patterns and application-specific factors, it typically supports around 20 users, with 4–5 concurrent users. Below here you can find an image of this setup:

Things you will need for an on-premises AI server:

• GPU Servers
• CPU
• RAM
• Storage
• Networking
• Power Supply
• Cooling System
• Rack or Chassis
• Backup Storage

4. Plan ahead

If you need to order hardware and other supplies, you should make the purchase well ahead of your project timeline. Because it can take weeks, sometimes even months, before you receive them.

Of course, an on premise AI platform is the best option if you already have hardware that is not fully utilized. This makes the process even simpler and provides additional value from underused resources.

5. Test PoCs in the Cloud

According to IBM’s recent survey of 2000 C-suite executives, only 25% of proof of concepts (PoCs) deliver a real return on investment (ROI) and are scaled for organization-wide use. As a result, you should not go all in with the on-prem setup before you do this: test PoCs in the cloud environment.

Once tests show which PoCs provide actual value to your organization, you should start thinking about how to scale them cost-efficiently. Also, by doing that, you'll get an idea of how much computing power you need, so you can set the budget to purchase it.

The same goes while you wait for the hardware. Start your tests in the cloud and move to on-premises once you receive and assemble your server setup.

If you need help getting started, we invite you to book a demo with one of our co-founders, who will show you how you can run the AI platform on-premises and discuss your use cases.

We also provide a cloud environment where you can test your ideas before deciding to fully commit to on-premises AI solutions.