To Thrive in the AI Era, Enterprises Need Model Choice

Getting the right AI outcomes starts with choosing the right models. But how do you decide which are the “right” models for you? With so many different options available—from open to closed, from frontier LLMs to domain-specific—how can you possibly be sure you’re making the right choices for all your different AI workloads?

The short answer is that you can’t. But what you can do is build an enterprise architecture that’s optimized for flexibility. Choosing models isn’t a one-time decision anyway, because your exact needs will always change over time. Making the right choices today is less important than being able to easily change your mind in the future.

Many enterprises are shifting from LLM-only AI strategies to multi-model, multimodal strategies. As a recent IDC blog post reported, “What is replacing LLM-only AI strategies? The shift to ‘any-to-any’ models. LLMs, largely built on transformer architectures, still play a central role. But they are no longer sufficient on their own.”[1]

Optimizing model choice allows you to respond to changing conditions in the AI landscape and balance tradeoffs around the “three Ps”: pricing, performance and privacy. In addition, it allows you to future-proof your AI strategy, changing your approach as your needs inevitably change.

Pricing: Don’t pay for what you don’t need

As enterprise leaders choose AI models, they must consider the tradeoffs between performance and cost-efficiency. For instance, it doesn’t make sense to pay a premium for a “better” model that doesn’t actually drive greater business value. In some cases, the open model that’s “good enough” may truly be good enough. In fact, indiscriminately routing prompts to models that aren’t fit for purpose is one of the main ways that enterprises lose control over their AI costs.

Ensuring AI cost-efficiency is all about control. You need to control where tokens are routed; otherwise, your applications may consistently send prompts to models that are much more powerful—and more expensive—than those prompts require. One Equinix customer, a life sciences company that uses AI to accelerate drug discovery, cut costs by 44% after moving from a frontier LLM to a neocloud-hosted model. They achieved this despite the fact that the new model still provides acceptable results.

The answer to the question “which model should we use?” will sometimes be “none of them.” There will be cases where enterprises can stop requests from ever becoming tokens in the first place. For instance, they can use private infrastructure to store answers to common questions, a concept called semantic caching. When users ask those same questions again in the future, the application can generate a response without having to consult a model at all. Since these questions are asked so frequently, they present a big cost-savings opportunity for enterprises that can avoid unnecessary model calls.

Performance: Pick the right model for the job and optimize for lower latency

Getting the right models in the right places can determine how accurate and responsive AI applications are. In this case, the “right” models are those that are fit for purpose. Sometimes, a powerful LLM may be the only valid choice, but this shouldn’t be the default for every use case.

Some use cases require the deep industry context that only a domain-specific language model (DSLM) can provide. There will also be times when more affordable open-source models are good enough to get the job done, and continuing to pay more for frontier models would be unnecessary. It’s all about continuously striking the right balance between pricing and business requirements.

To get the choice and flexibility they need, enterprises need access to a wide variety of different ecosystem partners. This includes the model providers themselves, but also the cloud hyperscalers and neoclouds that offer infrastructure and tools to support those models.

Where enterprises host their models also has a big impact on performance. Cloud-based models could be physically hosted just about anywhere, so they’re not a good choice for applications that are especially sensitive to latency.

When enterprises architect for model choice, they may be able to find a different model that offers comparable accuracy and better proximity to data sources and end users. This can shave milliseconds of delay from each individual transaction. It may not sound like much, but when you consider the thousands of transactions required to build an application and ensure a great user experience, it adds up to a big impact.

Privacy: Align AI operations with data governance requirements

As new regulations governing data privacy and sovereignty continue to proliferate in different jurisdictions throughout the world, enterprises need to ensure that their AI workloads are aligning with their data governance requirements. Of course, their choice of models plays a role here as well.

Enterprises need to place sensitive or regulated datasets into environments that meet their governance requirements. For instance, if they need to meet specific data residency requirements in the European Union, they’ll need access to models hosted exclusively in the EU. If they simply default to the most powerful or most affordable model available, with no concern for where that model’s hosted or who controls it, they’ll put themselves at risk of non-compliance.

For sensitive workloads and datasets, enterprises may need to deploy models on private infrastructure. This helps meet their sovereignty requirements because they know exactly where those models are hosted, and they know their data never has to cross borders to reach those models. Deploying models on private infrastructure also empowers them to apply the appropriate security measures to protect sensitive data, instead of leaving it up to a cloud provider to do it on their behalf.

With all this said, the ideal way to balance the tradeoffs between performance, pricing and privacy is with a hybrid infrastructure approach. While some workloads will require the security and control that private infrastructure offers, others will benefit more from the scalability and convenience of cloud-based models. It’s all about striking the right balance of different models for different purposes.

How Equinix enables AI model choice

Enterprise AI can’t be a one-size-fits-all approach. You need to run different models, but you also need to run those models in different places. What you need is an AI framework that allows you to balance your private and public infrastructure environments so that you’re always able to choose the right models, from the right partners, in the right places.

At Equinix, we built that exact framework for our customers, and we call it the Equinix Distributed AI™ Hub. It brings together different kinds of models across public and private infrastructure on one neutral foundation.

Once you build an architecture at Equinix, you can use gateways and Equinix Fabric connectivity to access the right models for the right workloads, without having to rearchitect. You can start dialing in the options that work best for your needs, and you can continue to optimize those options over time. You’re never tied to a certain way of doing things. That’s important because we know that the AI landscape will continue to change, so you need to be able to change with it.

As the diagram below shows, the Equinix Distributed AI Hub sits at the heart of your choice-optimized AI infrastructure:

The left side of the diagram illustrates the private infrastructure you can use to maintain control over datasets and workloads, meet sovereignty and residency requirements, and pursue cost-savings opportunities like semantic caching.

The right side illustrates your partner ecosystem. You can connect on demand with all the different AI providers that matter to your business. In addition to model providers, this includes cloud hyperscalers and neoclouds.

Crucially, you can reach these partners using private interconnection solutions like Equinix Fabric® instead of the public internet. This helps ensure that your network won’t prevent you from optimizing performance, keeping costs low, and protecting and governing data.

To put it simply: Deploying at Equinix means you can access any model, from any provider, wherever you need it. Whatever the ideal mix of pricing, performance and privacy looks like for your business, you’ll be able to achieve it thanks to Equinix’s vendor-neutral colocation data centers, private interconnection solutions, and dense ecosystem of AI service providers.

Learn more about how enterprises are positioning workloads for optimal performance, security and cost. Read the 451 Research Pathfinder Report, commissioned by Equinix: “Modern IT pressures rewrite workload placement decisions.”[2]

[1] IDC Blog Post, Beyond LLMs: Why AI Strategy Now Requires Multi-Model, Multimodal, and Multi-Agent Architectures, April 13, 2026.

[2] 451 Research Vanguard Report commissioned by Equinix, Modern IT pressures rewrite workload placement decisions, April 2026.