Taken on their own, with no safeguards applied and no extra 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 system 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.
The Software-as-a-Service (SaaS), or on-demand software, framework is arguably the easiest method to deploy LLMs from the point of view of your IT team because the software is made available to users via the Internet. The provider handles the heavy lifting and maintains the hardware, the model, the code that runs the model, and the data used by the model. While deploying an LLM using the SaaS framework can seem like an all-around great idea, it isn’t the best option for every organization. Here are five key benefits and five key drawbacks to take into account when considering a SaaS deployment approach for your organization.
Quick Deployment with Reduced Time to Benefit: SaaS solutions can be set up and operationalized quickly and easily because the application is already installed, configured, and available in the cloud. Deploying an LLM as a SaaS solution means the model can be integrated into an organization’s operations without requiring any new or altered infrastructure.
Financial and Operational Efficiencies: Typically, LLMs don’t require extensive up-front capital expenditure because they are subscription-based and don’t require any special hardware or software, which allows for streamlined resource management. System updates and some elements of security are handled by the service provider.
Easily Scalable and Integrated: LLMs offered as SaaS solutions allow organizations to adapt and adjust to user demand without significant additional investment. Flexible subscription options are common and the variety of plug-ins available to integrate into existing organizational capabilities continues to expand.
Supports Accessibility and Collaboration: Users can access the LLM from any web browser on any Internet-enabled device in any location with an Internet connection, which allows continuous collaboration across teams and timezones. Some LLMs provide users limited or full access to their search histories for easy reference.
Easy to Use with Little/No Training: Most LLMs have a user interface that resembles a search engine and is, therefore, very familiar to users. Some providers offer users an option of selecting the version of the LLM they wish to use based on what they need to do. Also, some LLMs are “conversational” and can provide content linked to the previous query, while some treat each query as a stand-alone prompt.
Data Privacy and Security: Using LLMs for sensitive tasks could put your organization at risk for data loss and privacy issues. The content sent to LLMs in prompts may be processed and kept by the provider, with no assurances regarding security against misuse or further dissemination.
Limited Customization and Controls: LLMs are emerging technology and, as such, have been designed to meet the needs of a very wide range of users. This means their capacity to provide customized user interactions based on organizational needs or preferences is still in development. Security controls or acceptable-use limitations that organizations might want to implement, such as prohibiting specific content, i.e., project names or source code in prompts, are not yet available, which increases the organization’s risk exposure.
Cost Increases: While the SaaS framework doesn’t typically require a large initial investment, subscription fees can increase over time due to price hikes or additional users added to the system, which reduces long-term cost-effectiveness.
Latency and Performance: It is not unusual for SaaS applications to run more slowly than local applications, which means that latency must be a critical consideration, especially for real-time LLM usage, such as conversational customer service or language translation applications.
Connectivity: This issue is as basic as it is critical. As with any Internet-based solution, dependable LLM performance can only be realized when the service provider’s connectivity is reliable and reinforced against slowdowns or failure.
Whether the benefits of deploying an LLM across your enterprise using a SaaS framework 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.