In-house deployments of generative AI offer a number of key advantages. Here are five to consider.
Although we’re still in the beginning stages of broad generative AI adoption, it’s already shown significant potential to transform businesses and industries. And while examples of early wins abound, organizations are still wrapping their heads around what’s possible and how to make it work for them.
One thing that’s certain: the importance of data in the generative AI era. AI requires lots of high-quality data—and the emergence of generative AI has only reinforced this point. Unlocking generative AI’s full potential lies in an organization’s ability to use it alongside its own proprietary data.
But therein lies a few challenges. For one, growing concern around security and privacy issues stemming from use of public generative AI tools in the workplace. Consequently, many organizations choose to deploy generative AI projects internally, yet face significant challenges in terms of having the right skillsets, budgets, and resources to support them.
To counteract this, some organizations stand up private instances of generative AI large language models (LLMs) in the public cloud. This approach may offer some advantages—speed and ease of deployment as examples—but it’s not right for everyone or every AI workload.
One solution you might not have considered includes bringing an off-the-shelf or open-source model into your own private environment and using it for inferencing or tuning your data. This eases speed and deployment issues without sacrificing privacy and security.
In-house deployments of generative AI offer a number of key advantages. Here are five to consider:
1. You alone control security and data access
First came the stories of company data leaks through public use of ChatGPT. IT leaders faced a familiar challenge: how to provide secure and private internal tools with the features and ease of use that would incentivize users to adopt them over preferred public options. Consequently, some turned to public cloud-hosted private instances of LLMs, but even that approach has been met with some skepticism. To ensure AI models aren’t compromised, some cloud vendors have reserved the right to review prompts and generated content. The reality is the most secure scenario is one where only you control who and when they can access that data —and that’s when your AI model and your data are both located in your own secure environment.
2. You can create more guardrails and reduce reputational risk
Common complaints about public large language models are lack of transparency and explainability. And the truth is, depending on what off-the-shelf LLM you’re using, you might not be clear what data it’s trained on, how it comes up with its answers, or what guardrails it has. Training or retraining a model in-house gives you more control and visibility into exactly how it functions. For example, if you find the model is prone to getting information egregiously wrong about your company or gives responses you deem inappropriate, you can retrain it with high fidelity data or set certain guardrails. This allows you to reduce or mitigate risk.
3. You can capitalize on real-time data
Some of your most interesting data may live outside traditional IT environments—in edge locations such as sensors, points of sale or factory floors. This data is most valuable when you can derive insights and take action quickly, but that means data processing and AI pipelines with high availability and low latency. There’s also the issue of data gravity. Imagine all that’s involved with trying to move a petabyte of data to an AI model for processing. Simply put, there are few scenarios where it makes more sense to move the AI to your data over moving your data to the AI. Worth noting: use cases here are far broader than just generative AI—everything from inventory analysis to predictive maintenance to forecasting can benefit from applying AI at the edge, without sacrificing control over your data.
4. You can create cost efficiencies
For some organizations, with great generative AI power also came great public cloud bills. This has led some to consider whether there were more cost-efficient ways to embrace these technologies—such as running them in their own environments and retraining them with the ability to right-size for specific needs. For targeted use cases domain-specific or enterprise-specific models can deliver more value than their more generic brethren at a fraction of the footprint. This also means more opportunities to create cost efficiencies and embrace OpEx or CapEx models where they make sense. It also means the power to avoid the pitfalls of data egress or high storages fees in the cloud. Infrastructure under your control means costs are more under your control.
5. You can be more energy efficient
It turns out right-sizing AI models isn’t just about cost efficiencies. The size and complexity of LMMs—GPT-4 has a reported 1.7 trillion parameters, for example—make them incredibly compute intensive. Inferencing or retraining a model around domain-specific data offers the opportunity to be more energy efficient. For some smaller models you can even run them locally on a PC like a precision workstation, greatly reducing the amount of energy consumed. This means you control how you adopt generative AI and can do it in a manner that’s more energy efficient while still meeting the needs of your organization.
Where the generative AI journey goes from here
Like most organizations, you’re likely at the beginning of your generative AI journey. Which is why it’s ever more crucial to think through the best architecture for success. Will your best bet be moving large volumes of data closer to AI to derive its benefits, or will it be easier and more advantageous to move the AI closer to the location of your data? These considerations will impact how easily, cost effectively, and securely you can adopt generative AI within your organization.
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