The excitement around launching new AI services and applications is at an all-time high. But with the fast pace of change in the AI market, getting new AI projects off the ground can be complex. AI comes with risks and uncertainty, and organizations want to make sure their AI investments will pay off. As companies navigate AI complexity and prepare for the future, building and connecting to a robust AI ecosystem is crucial.
As the AI market grows, AI ecosystems are expanding quickly to meet the needs of enterprise AI. A plethora of new AI service providers have emerged in recent years, offering everything from GenAI tools to GPU as a Service to agentic AI solutions. Meanwhile, clouds, network service providers and traditional infrastructure providers are evolving their solutions to meet the requirements of AI.
No enterprise can do AI alone. Each one must find the right partners and solution providers for their AI initiatives. Building up and leveraging relationships with data providers, AI model providers, infrastructure providers and other ecosystem participants is crucial for AI success. Deloitte’s State of AI in the Enterprise survey found that 83% of the highest-achieving organizations create a diverse ecosystem of partnerships to execute their AI strategy. In addition, “organizations with more diverse ecosystems were much more likely to have transformative visions for AI and use AI as a strategic differentiator.”[1]
Building up and connecting to an AI ecosystem from scratch can be difficult. Because AI is still maturing, the solutions, services and partners available in the market are continuously changing, as are organizations’ AI needs. Companies therefore need to position themselves so they can pivot quickly and frequently as the AI market evolves. Connecting with growing AI ecosystems in a neutral place will help businesses move faster with AI, while maintaining maximum flexibility for an unpredictable AI future.
Let’s explore the key categories of providers in a comprehensive AI ecosystem and some considerations for choosing the right ecosystem partners to support your AI future.
Gather and protect your data
AI is a data problem first, not a compute problem. Everyone’s talking about GPUs, but the real bottleneck is data readiness. Having the right data, data architecture and data protection strategy is the foundation of AI success. All your other AI decisions will flow from this, so it’s important to give it careful thought and planning. Your data ecosystem addresses where you acquire data as well as how you store it: What datasets will you use for AI? How’s the data going to be treated? Who will have access? Is it secure? Can it go on the public internet?
The data ecosystem can include a variety of providers:
- Cloud service providers like AWS, Microsoft Azure or Google Cloud
- Data brokers
- SaaS platforms
- Other enterprises
As the AI market evolves, storage providers are beginning to offer specialized storage solutions optimized for AI. When choosing AI storage solutions, consider performance, cost efficiency, flexibility and integration with the rest of your AI stack. In addition, think about whether your data needs to live on-premises, in colocation or in the cloud.
AI storage providers include companies such as Dell Technologies, VAST Data, NetApp and Pure Storage.
There are no universals here: The data ecosystem you build will be very specific to your organization.
Choose the right AI model
Once you decide on what data to use and how to make it available for AI, then you can choose your AI model. While very large organizations might train their own model, most enterprises will use their data to fine-tune an existing model or implement a retrieval-augmented generation (RAG) pipeline backed by embeddings in a vector database.
The AI model ecosystem includes proprietary and open-source AI model providers, specialized AI model providers and AI model marketplaces. You need to determine whether you can use a large general-purpose AI model or if you need something more niche or domain-specific for your project. You should also think about how open or closed the model should be, whether it meets your governance and compliance needs, and if it can meet the performance and cost requirements of your AI solution.
As AI matures, more and more organizations are using multiple models from multiple providers because of costs, performance, accuracy, flexibility, availability and privacy. Very likely, AI model selection will be iterative and will change as the AI market keeps evolving.
of the highest-achieving organizations create a diverse ecosystem of partnerships to execute their AI strategy. —Deloitte State of AI in the Enterprise
Design infrastructure to support your AI future
With your data and AI model ecosystem in place, you’re ready to choose AI infrastructure and service providers. In addition to choosing who to work with, you have to consider where that infrastructure will live and how data will be transported.
Whether you use public or private resources for AI depends on how sensitive and regulated your data is. In highly regulated industries, public AI resources, such as the public cloud or the internet, may not be appropriate. If your data isn’t as sensitive and you’re looking to move quickly, you can use public infrastructure. Most enterprises fall somewhere in between and will need a mix of public and private solutions for AI.
Figure 1: An example of an AI ecosystem on Platform Equinix®
Compute
The AI compute ecosystem includes the AI hardware and chip manufacturers who provide CPUs, GPUs and LPUs for AI.
This can include:
- Major hyperscale cloud providers and smaller, tier 2 clouds
- Hardware and chip manufacturers like NVIDIA, AMD, Intel and Groq
- Neoclouds like CoreWeave, Crusoe, Lambda and Nebius that offer AI-specific capabilities
- Full-stack AI infrastructure providers like NVIDIA, Dell Technologies and HPE
Software providers like VMware and Red Hat hosting AI platforms on top of cloud-native technologies such as Kubernetes also fall under the AI compute umbrella.
When choosing AI compute infrastructure, you have to consider whether you want your GPUs to live in an on-premises data center, in a colocation facility or in the public cloud—based on your data and data requirements.
Networking
It’s crucial to be able to connect all the players in a diverse AI ecosystem. In addition to providing connectivity between AI ecosystem participants, networking enables you to move your data for AI training or inference. As with other infrastructure considerations, you need to think about public versus private networks. Can you move your data over the public internet, or do you need a private interconnection solution like Equinix Fabric®?
Network service providers like AT&T, Verizon, Telstra and Orange offer networking solutions to connect your AI infrastructure with remote sites and users.
Services
Finally, if you don’t have AI expertise in house, you may want an AI service provider in your AI ecosystem. This could include global systems integrators, managed service providers and value-added resellers like World Wide Technology (WWT), Accenture and Deloitte.
Future-proof AI on a neutral platform
At Equinix, you can find all the types of AI ecosystem participants you need in one place. As a vendor-neutral platform with global reach, Equinix is a magnet for leading AI providers, offering enterprises the richest, most diverse network of AI providers anywhere. There are thousands of clouds and SaaS providers, network service providers, and other AI solutions vendors on our platform, ranging from large, well-established companies evolving with the AI market to newer, cutting-edge companies that are gaining prominence in the AI space. In addition, our private interconnection service, Equinix Fabric, enables secure, software-defined interconnection between partners and providers.
Putting your AI infrastructure at Equinix de-risks your overall technology investment, since you’ll have the flexibility to make changes whenever you need to. No doubt, the AI landscape will keep evolving as AI matures, so put your AI infrastructure in a place where you can connect to the AI partners you need today and in the future.
Learn more about how to future-proof your AI strategy in The Equinix Indicator.
[1] How to create healthy technology ecosystems, Deloitte. Excerpt from Deloitte’s State of AI in the Enterprise, 4th Edition report.
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