About a year has passed since generative AI burst into the mainstream, helping send enterprise AI growth into overdrive. According to a recent IDC report, annual spending on AI—including generative AI—will go from $175.9 billion in 2023 to $509.1 billion in 2027, a compound annual growth rate (CAGR) of 30.4%.[1]
Enterprises are investing heavily in their AI strategies, both because they see the opportunity and because they don’t want to fall behind their competitors. However, as they accelerate their AI adoption, many enterprises also recognize they must be careful to ensure their AI strategy is sustainable and responsible.
Another growing area of concern is data management and protection. After all, data is the heart of AI. To fulfill the promise of AI, enterprises must capture data from the right sources and feed it into the right models. Herein lies one of the biggest challenges that enterprises face as they seek to activate their AI strategies: How can they maximize the value of their AI models without exposing their data or putting it at risk? To achieve this, many enterprises are beginning to turn to private AI. Private AI refers to an AI environment built by or for a specific organization, to be used exclusively by that organization.
What makes private AI different from public AI?
Before the proliferation of generative AI and large foundation models, AI models were generally private by default. This is because each model had to be trained on private data for each enterprise’s specific use case. It is only in the era of foundation models (such as the large language models (LLMs) used by ChatGPT) that public AI has become viable. Foundation models can be fine-tuned to cater to various use cases, allowing different users or enterprises to use and share the same models.
The table below summarizes the characteristics that differentiate private AI from public AI:
| Private AI | Public AI | |
|---|---|---|
| Purpose | Designed to be used by a specific entity. This is typically an enterprise that wants to execute its AI strategy while also maintaining control and custody over its data. | Designed to be used in the open public domain by multiple tenants in a shared environment. These users can be consumers, individual employees within corporations, or businesses more broadly. |
| Models | Developed by a third party or internally. Either way, the models are hosted in a private, protected environment, typically behind a firewall. | Developed by a third party and hosted in a public environment. All user interactions with the model can be used to expand the model. |
| Data (for training) | Uses proprietary data sets only—often including an enterprise’s most sensitive and valuable business data. | Often uses publicly available data, such as data packs that can be purchased on public platforms. Users can also add proprietary data for fine-tuning. Service providers for the model may access and store training data. |
| Data (for inference) | Typically uses an entity’s proprietary data. Only the entity has access to the data. | Can use either proprietary or publicly available data. Service providers for the model may access and store inference data. |
| Workload residence | Can be hosted in any private environment, such as on-premises, in a colocation data center or in a Bare Metal as a Service environment. | Hosted in a multitenant environment—often a public cloud. |
| Networking | Data only travels via private, dedicated network connections. | Data may cross the public internet. |
Private AI is emerging now because organizations have begun to recognize the limitations of building their AI strategy exclusively around public AI. They see the need for private AI, even if they’re not familiar with the term yet.
Top 3 ways private AI is right for enterprises
Using private AI can provide many opportunities for enterprises to optimize their AI strategies, as shown below:
Protect your proprietary data
When you feed your business’s proprietary data into public AI models, you agree to make that data public, whether you realize it or not. For one thing, you’re trusting a third party with your sensitive data, and you can’t be certain the third party will take the appropriate precautions to protect that data. You’d also be taking the business insights found in your data and building them into the public AI models. This means that your competitors could directly benefit from those insights.
With a private data architecture, you can keep your data where it belongs: in your own hands. This means you can feel confident that your data is protected, and that your data is used solely for the benefit of your company.
Reduce your regulatory risk
We live in an era of increasing regulatory complexity, with ruling bodies around the world setting new requirements for how enterprises collect, store, transfer and process data. These requirements can be particularly onerous for global companies that need to comply with data sovereignty requirements and specific rules around data life cycle management. How can enterprises stay on the right side of the law while still accessing the massive volumes of data they need for their AI models?
A private AI approach can help. Enterprises can design their models and data architectures to give themselves end-to-end control over their data. This includes specifying exactly what equipment is used to store and move the data, what physical locations the data is stored in, who has access to the data and for what purposes. In short, you won’t have to outsource your compliance responsibilities to a third party, as you would if you were using public AI models.
Optimize performance and cost-efficiency
When enterprises feed their own proprietary data into public AI models, the data and the models typically reside in different environments. For instance, public AI models are often hosted in a public cloud environment. Every time the enterprise has to move data between their own environment and the public cloud, it can cause delay and egress charges—especially if they don’t have an interconnection partner to help optimize performance.
Enterprises can design their private AI environments to minimize these issues. This could mean building their data architecture so that AI models and data warehouses are adjacent to one another. Doing so will ensure a consistent, low-latency flow of data. Also, since the data never leaves the internal data architecture, the enterprise will never have to pay a third party for the privilege of moving their own data.
When you feed your business’s proprietary data into public AI models, you agree to make that data public, whether you realize it or not."Ruth Faller, VP Corporate Development and Strategy, Equinix
What are the infrastructure requirements for private AI?
AI is a groundbreaking technology, and it has unique infrastructure requirements. Enterprises can’t achieve the benefits of AI while continuing to rely on conventional IT infrastructure. In fact, this is part of the appeal of public AI: Enterprises see it as a quick and easy way to get started, without having to build an AI infrastructure of their own.
As we’ve established in this blog post, there are important reasons that enterprises should build their own private infrastructure once they’re ready to scale their AI strategies. In this section, we’ll talk about what that infrastructure should look like.
Cloud adjacent
Just because your AI environment is private doesn’t mean you should be cut off from public clouds altogether. There are many reasons you might want to tap into public cloud resources, such as connecting with AI Model as a Service vendors that are hosted there. The key is being able to connect to public clouds on your own terms. This means building a cloud adjacent data architecture, where you maintain custody over your data while also being able to move it into the cloud on demand via dedicated, private network connections.
Ecosystem access
Even when enterprises build their own private AI infrastructure, that doesn’t mean they have to build it alone. They can connect to a wide variety of partners and service providers to get the agility and flexibility they need from their AI infrastructure. With access to the right digital ecosystem partners in the right places, enterprises can deploy the network, cloud and SaaS services they need to scale their AI infrastructure quickly and continue to evolve it over time to keep up with the changing needs of the business.
Examples of how partners can help enterprises with their private AI infrastructure include:
- Helping them deploy liquid cooling technology to keep up with the density requirements of AI workloads
- Helping them get the on-demand compute capacity they need in strategic locations via single-tenant Bare Metal as a Service
Global reach
Building a private AI environment requires the flexibility to capture valuable data wherever it’s generated, worldwide. You also need to position AI workloads in the locations that best meet their density and latency requirements—not just the best locations where you have infrastructure available.
The thought of building this global reach for yourself can feel very intimidating. The good thing is that you don’t have to. Working with a global colocation partner like Equinix can help you stand up the AI infrastructure you need, in all the locations where you need it, without the high cost and complexity of doing it yourself. Equinix also offers a dense partner ecosystem that includes many key players in the global AI market, and low-latency on-ramps to top cloud providers worldwide.
To learn more about how Equinix customers are thriving in the modern digital economy with distributed, interconnected digital infrastructure, read our vision paper The future of digital leadership.
[1] Rick Villars, Karen Massey, Mike Glennon, Eileen Smith, Rasmus Andsbjerg, Peter Rutten, Ritu Jyoti, Jason Bremner, David Schubmehl, GenAI Implementation Market Outlook: Worldwide Core IT Spending for GenAI Forecast, 2023–2027, IDC Market Note, Doc # US51294223, October 2023.
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