When taken on their own, with no safeguards applied and no additional artificial intelligence (AI) security protocols in place, deploying generative AI (GenAI) models, particularly large language models (LLMs), across the enterprise is a high-risk, high-reward opportunity for any organization.
But exactly how your organization should undertake this big step into the GenAI landscape requires some thoughtful planning. Perhaps it would be better organizationally to gain access to the model through a provider, following the Software as a Service (SaaS) framework, to avoid any configuration or installation issues. Or it might work better to deploy the model on your organization’s private cloud or on your network (on-premise) and enable your organization to control API configuration and management.
This series of three blogs will address the How? question: How should your organization deploy LLMs across the enterprise to achieve maximum return on investment? Each blog will provide information about the benefits and drawbacks of one common deployment framework, enabling you to consider it in light of your company’s organizational and business structure and specific business needs.
An application program interface (API) is, in essence, a digital connection between two devices that enables them to send information back and forth. API software comprises definitions that identify what will be sent between the devices (e.g., the client device making the request and the server device sending the response) and protocols for how that information is to be sent (e.g., the URL/endpoint of the receiving device and the syntax/wording/language that both the request and the response must use). This enables end users to, for instance, log into applications to purchase items on a website, or schedule a rideshare, or, in reference to an LLM, to issue a query and receive a reply, without having to understand how or why the system works. APIs can be monitored and secured, so data on the client’s device is never completely exposed to the server device.
An on-premise system means the application, in this case the LLM, is installed on the organization’s infrastructure (servers) and is available to all users who have access to the organization’s network and the application. A subset of on-premise systems are isolated or “air-gapped” from open access to the Internet, although they can be connected via secure means.
Data Security and Compliance: Hosting LLMs on your own servers ensures that you have complete control over your data and the security protocols protecting it from both internal and external threats. This becomes especially crucial for organizations that are subject to strict data protection regulations, such as healthcare or finance.
Customization and Control: With an on-premise setup, you can customize the LLM(s) to suit specialized organizational tasks, requirements, and other needs, and configure and manage the APIs.
Low Latency: On-premise LLMs often achieve lower latency, which is critical for some real-time applications, such as instant translation, real-time analytics, or customer service operations reliant on chatbots.
Data Integration: Integrating the LLM(s) with existing databases and internal systems can occur without concerns about safety and security when transferring data.
Cost Control and Predictability: The initial setup for an on-premise solution can be pricey, but there are no recurring third-party, subscription, or hosting fees, which are subject to increases, or vendor lock-in issues, making an on-premise solution potentially more economical long term. Automating tasks and fine-tuning the model for other purposes is a more streamlined process with an on-premise model, and can expand the number of tasks in your cost-savings column, as well as in your revenue-generation column.
High Initial Cost: Setting up the necessary local hardware and software, including securing the physical space and power supply for an on-premise LLM can be expensive and time-consuming.
Maintenance Requirements: An on-premise solution requires ongoing maintenance and operational overhead, including hardware repairs, software updates, and security measures, which can be resource-intensive.
Complexity and Scalability: Scaling on-premise LLMs can be complex and expensive, often requiring additional hardware purchases and system downtime for upgrades, as up- or down-scaling is not an on-demand undertaking.
Limited Accessibility: On-premise solutions might not be easily or securely accessible from different locations, and can require virtual private networks (VPNs) or other secure channels for remote access. Local system failures, such as due to weather events or other issues, can compromise connectivity, unless the organization has planned for and maintained critical redundancies.
Whether the benefits of deploying an LLM across your enterprise on-premise outweigh the drawbacks can be determined by serious consideration of your organization’s financial and technical resources, business needs, and security or other operational constraints. Some of our earlier blogs have focused on establishing internal and external systematic safeguards, AI governance programs, and other ways to safely deploy these transformative tools in your organization, and may be of help to you as you traverse this new path through the technosphere.