Building an LLM can be a strategic play for many organizations. Yet, even with all the benefits of doing so, third-party models still make a lot of sense for some companies.
Last year, a Forbes article predicted that every business would have its own LLM. Despite what a tremendous undertaking that would have been a few years ago, that prediction now seems pretty normal. With advancements in AI training and more companies making better use of large data volumes, it’s not out of reach for companies to build and deploy a large language model purpose-built for their needs. But even standing on the shoulders of others, this is a significant undertaking. Let’s take a look at what’s making some businesses feel it’s worthwhile.
Why some companies might build
While relying on third-party models can seem like the easier route, more organizations are choosing to build their own. Let’s explore why.
Control over data privacy
While all companies dealing with data have privacy concerns and restrictions, some industries, like healthcare, have even greater regulations because they deal with sensitive data. Using a third-party LLM means jumping through hoops to ensure data privacy and then introducing potential weak spots in security because of that same sharing. Contracts certainly put boundaries in place, but even then, it might be too risky.
Building an in-house LLM lets these companies keep a tight grip on their data. Plus, it’s much easier to comply with strict regulations, like GDPR or HIPAA, when you control exactly where and how your data is handled.
Customizing for the perfect fit
Off-the-shelf LLMs are one-size-fits-all, which is fine if a company doesn’t do anything niche or disruptive. However, in today’s business climate, niche and disruptive are competitive differentiators, so third-party LLMs may not deliver the hoped-for performance.
However, when a company builds its own LLM, it custom-fits it to the company’s needs, making sure it understands the unique context and language of its industry. Additionally, companies can quickly tweak and update their models as market dynamics, customer behaviors, or business goals change—something that’s harder to do when waiting for a third-party provider to release updates.
Saving money
Even with advancements in the field, building an LLM is a significant investment. There are costs associated with research, development, and infrastructure. But if a company needs to use the model a lot or has very specific requirements, this upfront investment can actually save money over time. Licensing a third-party model continuously can get expensive, especially as usage grows.
Companies that build also avoid dreaded vendor lock-in, which can come with surprise fees, rate hikes, restructuring, changes in service, and a host of other risks. Since this is still the realm of startup space, it may invite less trust that the third-party LLM will be around for the long term. A custom LLM is one potential solution to this risk.
Optimizing for business performance
A custom build can really set a business apart from the competition. By training the model on proprietary data, companies are creating something unique—an AI that understands their customers, their industry, and their brand like no other. This can mean better, more relevant responses that boost customer satisfaction and loyalty. Additional bonuses include:
- Intellectual property: LLM ownership opens up new opportunities for licensing, patents, or even creating new products.
- Optimized infrastructure: Think the hardware and cloud environments. This gets the best performance possible with none of the limitations third-party LLMs sometimes carry.
- Full transparency: In industries where decisions have significant implications, businesses need to understand exactly how their AI makes those decisions. Owning the model means full oversight, which builds trust and makes it easier to explain decisions to stakeholders.
Helping future proof
The AI landscape changes fast. Building an LLM could give companies more control so that they can pivot without waiting for third-party providers to catch up. A custom model integrates more easily with existing tools, workflows, and processes. When those change, the company could, in theory, have an easier time integrating the LLM with the change. It’s all about creating an AI product that works with the business and doesn’t demand the business shift components for the LLM.
See also: How to Attract LLM Developers Amidst the AI Boom
Why others may choose not to built their own LLM
While the advantages of building an in-house LLM are clear, this path is not suitable for every organization. Here’s why some companies might choose to stay with third-party solutions:
- Resource Limitations: Developing an LLM demands significant talent, technology, and capital investments. Companies lacking these resources or expertise may find the undertaking impractical. The high upfront costs and ongoing maintenance can be substantial barriers.
- Need for Rapid Deployment: If speed is of the essence, third-party LLMs offer a quicker route to deployment. Companies with immediate needs or short-term projects can benefit from the ready availability of third-party models, avoiding the lengthy development timeline associated with custom models.
- Low or Uncertain Usage: For organizations with low or uncertain demand for AI capabilities, the costs of building and maintaining an in-house model may outweigh the benefits. In such cases, third-party models provide a more flexible, cost-effective option, especially for infrequent or low-volume use.
- Non-Critical Applications: If the AI application is not mission-critical or requires only basic functionalities, third-party solutions may offer sufficient capabilities without the complexities of developing a custom model.
See also: Data Privacy Concerns Deter Enterprises From Commercial LLMs
Weighing the pros and cons
A few years back, building an in-house LLM might have seemed like a moonshot, but today, it could be a strategic play for the right organizations. And the appeal is clear: more control over data, the ability to tailor every output, cost savings over time, and a unique competitive edge that future-proofs your business. Yet, even with all the benefits, this isn’t a one-size-fits-all approach. For some companies, especially those without deep pockets or a burning need for a custom solution, third-party models still make a lot of sense.
