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What is an On-Premise AI Platform?

What is an On-Premise AI Platform?

An on-premise AI platform is a platform that runs AI services and applications within the organization’s physical environment, rather than being hosted on the cloud. As a result, it is maintained and operated by the organization’s employees, rather than by the external cloud provider.

On-premise AI platforms often allow enhanced security and privacy, as well as more customization and control. Regulated industries are often required to ensure on-premise AI deployment to meet compliance regulations. When choosing an on-premise AI platform approach, it’s important to ensure the organization has the internal resources and knowledge to support such platforms.

What are the Benefits of an AI On-Premise Platform?

An on-premise AI solution offers advantages to Heads of ML, Data Engineers and other data professionals. Among these are security, control and performance benefits. Here’s a detailed deep dive:

  • Enhanced Security and Data Privacy – On-premise AI solutions often handle data more securely, because the data and AI models reside within the organization’s own physical infrastructure. This minimizes the risk of data breaches since organizations have more control over data protection policies, data is less externally exposed and the organization is not vulnerable to cyber attacks to the cloud.
  • Customization and Autonomy – An on-premise setup provides total control over the hardware and software environment. This allows for deep customization to meet specific organizational requirements across performance, storage, or network configuration requirements. It also reduces dependence on the public cloud, whether it’s technological capabilities that might not be right for your use case, or rising costs.
  • Compliance with Regulatory Standards – Certain industries are subject to stringent regulatory requirements regarding data handling and processing. On-premise AI can be tailored to comply with these regulations, providing a framework that adheres to legal standards like GDPR, HIPAA, etc.
  • Reduced Latency for Critical Applications – On-premises data doesn’t need to be transmitted over the internet to a cloud server. As a result, on-premise AI can offer significantly lower latency. This is particularly beneficial for real-time applications where even a small delay can be critical, like gaming.
  • Independence from Internet Connectivity and Bandwidth – On-premise AI does not rely on continuous internet connectivity. This can be advantageous in scenarios where internet access is unreliable or where bandwidth constraints limit cloud computing effectiveness.
  • Easier Integration with Legacy Systems – Due to their customization and flexibility, on-premise solutions can be more easily integrated with existing legacy systems within the organization.

Which Industries Benefit From an On-Premise AI Platform?

Industries that need to prioritize data security, regulatory compliance, and high-performance computing can benefit from on-premises AI deployments. Example industries include:

  • Healthcare – Handling sensitive patient data requires adherence to strict privacy laws and regulations like HIPAA. On-premises AI ensures better control over this data. In addition, high-performance computing capabilities for processing large datasets and running complex simulations, which is supported by on-premises AI, can assist in research and drug discovery.
  • Financial Services and Banking – Financial institutions handle sensitive financial and customer data, making them subject to stringent regulatory requirements. An on-premise AI platform can help meet these requirements. In addition, AI can be used for real-time fraud detection and risk assessment, since low latency and high data throughput are crucial for them.
  • Government and Public Sector – Government data often includes confidential information that must be protected and kept within national borders. The public cloud is global, but you control where an on-premise AI platform resides. In addition, AI can help in optimizing public services, from traffic management to public safety, where data might not be permissible to store or process off-premises.
  • Manufacturing and Industrial – AI can optimize supply chains and manufacturing processes, often requiring integration with sensitive and legacy internal systems.
  • Gaming – Gaming requires real-time, high-performance computing and the need to protect intellectual property. For example, during real-time strategy games, multiplayer online battles, and virtual reality environments. On-premise AI ensures minimal latency in game development and player experience.

Why is an On-Premise AI Platform Important for Gen AI?

An on-premise AI platform can be important for general AI applications due to several key reasons:

  • Data Security and Privacy: On-premise solutions allow for better control over data security and privacy. Sensitive information, such as personal data or proprietary business information, can be more securely managed on-site, reducing the risk of data breaches that can occur through third-party cloud services.
  • Compliance and Regulatory Requirements Industries like finance and healthcare, that regularly handle highly sensitive personal data, are subject to strict regulatory requirements regarding data handling and processing. In many cases, private data cannot be uploaded to the cloud and must be kept on-premise per compliance requirements.
  • Reduced Long-Term Costs: While the initial investment for on-premise infrastructure can be higher, the long-term costs of running AI applications in production can be lower compared to cloud-based services, especially for gen AI applications that leverage massive workloads. By leveraging an on-premise platform, the high fees associated with gen AI–subscription fees, expensive API calls, and so on—can be mitigated over the long term.

However, it’s important to note that on-premise AI platforms also come with their own set of challenges and costs, such as the need for significant upfront investment in (potentially scarce) hardware and infrastructure, higher maintenance requirements, and the need for in-house expertise to manage and operate the AI systems. It’s important to find a solution for the automatic orchestration of resource allocation and control, automatic scale up/scale to zero, and team-wide GPU management in order to make the most of this AI investment.

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What are Some Best Practices of Working with an On-Premise AI Platform?

Implementing an on-premise AI platform requires careful thought and planning. Based on our experience working with global companies on operating and deploying these platforms, here are some best practices to follow.

1. Define the Use Case

The first step is determining the use case that the platform will serve. This could range from data analysis to automating specific tasks to many more possibilities. Understanding the use case at this stage is critical; on-premises deployment is less flexible for changes and scale than cloud-based solutions. Therefore, understanding what you need to achieve and whether the project will be a classic ML project, a deep learning one, generative AI, or a different type will deeply impact your next steps.

2. Choose the Right Hardware

Based on your use case, determine resource procurement needs. To ensure you are choosing the right hardware, here are some example questions to ask:

  • What type of hardware do you need? E.g, will you need CPUs, GPUs, memory, and storage solutions? How many?
  • Do you need real-time, low latency support?
  • How many users are you expecting?
  • What’s the forecasted computational load?

3. Plan Ahead for Procurement

Ensure hardware is shipped and installed on time for your project’s timelines, since your operations rely on them. There are components that could take even a year to arrive. It’s also recommended to come up with a plan B if components aren’t ready on time.

4. Run Your POC in the Cloud

While your servers are on-prem, your Proof of Concept doesn’t have to be. Use synthetic data and the cloud for more efficient operations, before developing your actual project on-premises. This will help you speed up the process and get to production deployment faster.

Ready to get started with on-premises AI? Book a consultation here.