In the early days of cloud, shadow IT proliferated. Different teams acquired different services, with no centralized oversight. Over time, leaders saw the drawbacks of this approach, including unpredictable costs, compromised performance, and data privacy issues. IT teams began to take back control, and a centralized cloud function became the norm.
Today, this pattern is repeating itself with AI. Until recently, most enterprises took an ad hoc approach, where different teams pursued AI as they saw fit. This led to many of the same problems mentioned above and made it difficult for businesses to move AI from proof of concept to production-grade deployment.
Now, enterprises are adopting the concept of the AI Center of Excellence. This refers to an AI factory solution combined with a centralized governance model overseen by IT. (Members from functional application teams are also part of the governance team.) It’s important to note that the governance is centralized, but the physical infrastructure itself can be distributed across different locations. This centralized approach is essential for any organization that wants to deploy production-grade AI solutions to improve customer experience and employee productivity.
What are the benefits of an AI Center of Excellence?
There are two main reasons enterprises are implementing AI Centers of Excellence: governance and costs.
Governance
As they pursue AI, every organization will inevitably face data privacy and data sovereignty challenges. Overcoming these challenges is hard enough, but a lack of centralized governance makes it essentially impossible. An AI Center of Excellence sets standards for how teams are allowed to access and use AI models and datasets.
Good governance becomes even more important as organizations start procuring data and models from external sources like AI model marketplaces. When using AI assets from partners and service providers, you must establish the source of truth and audit the full lineage of those assets. A centralized governance structure also helps different teams share best practices. An AI Center of Excellence can establish processes to make sure this happens.
Costs
When different teams choose their own approach to AI infrastructure, they won’t all make the most cost-efficient choices. In many instances, systems will remain under-utilized. Organizations may end up deploying duplicate cloud environments, overprovisioning capacity, or paying high egress fees. Centralizing AI enables better strategic planning and informed decision-making to avoid these outcomes.
What are the infrastructure requirements for an AI Center of Excellence?
AI-ready data centers
An AI Center of Excellence will provide support for different groups. That requires serious compute power. Many companies are using next-gen hardware from leading providers to get the compute they need.
In turn, that hardware must be deployed in an environment that can help it run to its full potential. This means a high-performance data center that provides AI-ready capabilities like advanced liquid cooling and power density of 130 kW/rack or more. These capabilities aren’t available in on-premises data centers or commodity colocation.
Privacy
For many organizations, ensuring data privacy and control over model lineage is paramount. Their proprietary data is among their most valuable assets, and they’re often worried that public cloud providers will train insights from that data into their global models. They need private infrastructure to address these concerns.
Managed services
Managing next-gen hardware can be very difficult and time-consuming, especially in the era of liquid cooling. It requires specialized knowledge and experience that most businesses won’t have in-house. Working with a leading colocation provider that also offers managed infrastructure services makes it easy for these companies to access the expertise they need.
Distributed networking
AI infrastructure is distributed by nature, and organizations need high-performance networking to keep it all connected. With distributed networking, enterprises can:
- Access data from distributed service providers, data brokers and business partners
- Connect developer teams across the world to the AI Center of Excellence
- Burst into the cloud for additional compute capacity on demand
- Connect their infrastructure footprint across geographic locations to enable local inference and federated AI
Flexibility
Organizations often pivot between service providers to access cutting-edge AI models and acquire newer AI hardware. They may also use both public and private infrastructure, for cost and privacy reasons. To achieve this flexibility, organizations need a centralized data hub where they can keep at least one copy of their data in a neutral, cloud adjacent location. This lets them ingest data into different public or private clouds to access innovative AI models and technologies.
If data is generated outside a particular cloud, they can store a copy of it on private infrastructure while keeping a second copy in the cloud where the application runs. This architecture helps enterprises avoid vendor lock-in due to high data egress costs. Also, the emergence of Model Context Protocol (MCP) server technology in agentic AI gives organizations flexibility to avoid lock-in at the API level and easily pivot between service providers.
Governance and security
As mentioned previously, the need for centralized governance is one of the main reasons companies are pursuing AI Centers of Excellence. They must deploy the right tools to make sure this happens. For instance, automated AI tools can help audit the lineage of data and models. Also, enterprises need the right tools and infrastructure to enable a distributed approach to security.
Costs
Businesses want a predictable cost model for their AI Center of Excellence. They want the flexibility to provision for base infrastructure capacity and burst into the cloud during periods of peak demand. They want the flexibility to pursue both OPEX-based and CAPEX-based solutions as their AI capabilities mature. Finally, they want a data architecture that allows them to store authoritative data copies to minimize their cloud data egress costs.
Multitenancy
AI Centers of Excellence are shared among various teams, so they need shared infrastructure that works well for all of them. Furthermore, they need infrastructure that can be easily split to support different types of workloads within an AI Center of Excellence. This could include:
- Training proprietary AI models
- Fine-tuning existing AI models
- Performing inference (for certain workloads that aren’t latency-sensitive)
Rather than developing foundation models from scratch, many organizations are leveraging AI models developed elsewhere via retrieval-augmented generation (RAG) and agentic AI frameworks. Also, some will deploy the latest AI clusters for training and repurpose older hardware for inference. For these reasons and more, organizations need to be able to support different varieties of hardware on the same interconnected platform.
Why deploy an AI Center of Excellence at Equinix?
Choosing the right place to host an AI Center of Excellence is becoming a competitive differentiator. In an Equinix colocation data center, organizations can meet all the requirements outlined above.
The benefits of deploying an AI Center of Excellence at Equinix can be summed up as “the 3 P’s”: price, performance and privacy.
- Price: Enterprises can move toward a predictable, fixed cost model. If their utilization rates are high, they can reduce their overall costs compared to the clouds. Also, they can avoid egress fees to ensure a cost-efficient approach for workloads they do run in the cloud.
- Performance: Customers can get more predictable performance on dedicated infrastructure. They can ensure proximity to the data sources to enable low latency for AI inference. They can also use Equinix Fabric® virtual interconnection for proven predictable performance, unlike the public internet.
- Privacy: Customers can bring models to their sensitive data, instead of uploading their sensitive data into the clouds. Furthermore, they can better control the lineage and choice of AI models used as part of their solutions.
To learn more about how Equinix is helping customers meet AI infrastructure requirements, read the ESG analyst report Architecting a Data Center Optimized for the AI Era.[1]
[1] Enterprise Strategy Group, Architecting a Data Center Optimized for the AI Era, Scott Sinclair and Monya Keane, May 2025.
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