Beyond the Hype: The CIO's Guide to Eliminating the Hidden Costs and Complexity of AI at Scale
The promise of Artificial Intelligence (AI) is clear: groundbreaking efficiency, new revenue streams, and a decisive competitive edge. But for you, the IT leader, the reality often looks a little different. You’ve moved past the initial proofs of concept. Now, as you attempt to scale AI across your global enterprise, the conversation shifts from innovation to infrastructure friction. You’re hitting walls built from unpredictable data egress fees, daunting data residency mandates, and the sheer, exhausting complexity of unifying multicloud, on-prem, and edge environments. The network that was fine for basic cloud adoption is now a liability—a bottleneck that drains budget and slows down the very models designed to accelerate your business. I'm Ted, and as an Equinix Expert and Global Principal Technologist here at Equinix, I speak with IT leaders every day who are grappling with these exact challenges. They want to know: What are the hidden costs when training AI across multiple clouds? How do we keep AI training data legally compliant across countries and regions? How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? How to predict and control network spend when running apps across multiple clouds? What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? The short answer is: You need to stop viewing your network as a collection of static, siloed pipes. You need a unified digital infrastructure that eliminates complexity, centralizes control, and makes compliance a feature, not a frantic afterthought. In this deep-dive, we'll unpack the major FAQs of scaling enterprise AI and show you how a platform-centric approach—leveraging the power of Equinix Fabric and Network Edge—can turn your network from an AI impediment into a powerful, elastic enabler of your global strategy. Ready to architect your way to AI success? Let's get started. Q: What are the hidden costs when training AI across multiple clouds? A. The AI landscape is inherently dynamic, with dominant players frequently being surpassed by innovative approaches. This constant evolution necessitates a multicloud strategy that provides flexibility to adopt new technologies and capabilities as they emerge. Organizations must be able to pivot quickly to leverage advancements in AI models, tools, and cloud services without being constrained by rigid infrastructure or high migration costs. However, the rub is, as cloud AI training scales, network-related costs often become the most unpredictable part of the total budget. The main drivers are data egress fees, inefficient routing, and duplicated network infrastructure. Data egress charges grow rapidly when moving petabytes of training data between clouds or regions, especially when traffic traverses the public internet. Unoptimized paths add latency that extends training cycles, while replicating firewalls, load balancers, and SD-WAN devices in every environment creates CapEx-heavy, operationally complex networks. Security infrastructure for network traffic is often duplicated between clouds, leading to cost inefficiencies. The solution lies in re-architecting data movement around private, software-defined interconnection. By replacing internet-based transit with direct, high-bandwidth links between cloud providers, organizations can reduce egress costs, improve throughput, and maintain predictable performance. Deploying virtual network functions (VNFs) in proximity to cloud regions also lowers hardware spend and simplifies management. Beyond addressing hidden cost, this approach gives IT leaders the agility to scale up or down with AI demand. As GPU clusters spin up, bandwidth can be turned up in minutes; when cycles finish, it can scale back just as fast. This elasticity avoids stranded investments while ensuring compliance and security controls remain consistent across clouds and regions. By unifying connectivity and network services on a single digital platform, Equinix helps enterprises eliminate hidden costs, accelerate data movement, and ensure the network is a strategic enabler rather than a bottleneck for AI adoption. Specifically, Equinix Fabric helps customers create private, high-performance connections directly between major cloud providers, enabling data to move securely and predictably without traversing the public internet. By extending this flexibility, Equinix Network Edge allows VNFs such as firewalls, SD-WAN, or load balancers to be deployed as software services near data sources or compute regions. Together, these capabilities form a unified interconnection layer that reduces hidden network costs, accelerates training performance, and simplifies scaling across clouds. Q: How do we keep AI training data legally compliant across countries and regions? A. Data sovereignty and privacy regulations increasingly shape how and where organizations can process AI data. Frameworks such as GDPR and regional residency laws often require that sensitive datasets remain within geographic boundaries while still being accessible for model training and inference. Balancing those requirements with the need for scalable compute across clouds is one of the core architectural challenges in enterprise AI. To address this, many enterprises choose to keep data out of the cloud but near it, placing it in neutral, high-performance locations adjacent to major cloud on-ramps. This approach enables control over where data physically resides while still allowing high-speed, low-latency access to any cloud for processing. It also helps avoid unnecessary egress fees, since data moves into the cloud for analysis or training but not back out again. By establishing deterministic, auditable connections between environments leveraging private, software-defined interconnection keeps data flows under enterprise control, rather than relying on public internet paths. As a result, organizations can enforce consistent encryption, access control, and monitoring across regions while maintaining compliance. This also translates into greater control and auditability of data flows. Workloads can be positioned in compliant locations while still accessing global AI services, GPU clouds, and data partners through secure, private pathways. By combining governance with agility, Equinix makes it possible to pursue your most pressing global AI strategies while still reducing risk. Today, Equinix Fabric can support this approach by enabling private connectivity between enterprise sites, cloud regions, and ecosystem partners, helping data remain local while workloads scale globally. Equinix Network Edge complements this by allowing in-region deployment of virtualized security and networking functions, so policies can be enforced consistently without requiring physical infrastructure in every jurisdiction. Together, these capabilities offer customers a foundation for compliant, globally distributed AI architectures. As a result, customers can create network architectures that not only reduce compliance risk but also turn regulatory constraints into a competitive advantage by delivering trusted, legally compliant AI services, based on the right data at the right time in the right place at global scale. Q: How can I balance on-prem, cloud, and edge when running AI workloads without adding more complexity? A. Determining where AI workloads should run involves balancing control, performance, and scalability. On-premises environments offer data governance and compliance, public clouds deliver elasticity and access to advanced AI tools, and edge locations provide low-latency close to users and devices. Without a unified strategy, this mix can lead to fragmented systems, inconsistent security, and rising operational complexity. One effective approach is a hybrid multicloud architecture that standardizes connectivity and governance across all environments. Equinix defines hybrid multicloud architecture as a flexible and cost-effective infrastructure that combines the best aspects of public and private clouds to optimize performance, capabilities, cost, and agility. This design allows workloads to move seamlessly between on-prem, cloud, and edge based on performance, regulatory, or cost needs without rearchitecting each time. As a result, organizations can employ a hybrid multicloud architecture where policies, security, and connectivity are consistent across all environments. AI training can happen in the cloud with high-bandwidth interconnects, inference can run at the edge with low-latency access to devices, and sensitive datasets can remain on-premises to maintain regulatory compliance. This architecture enables seamless interconnection across clouds, users, and ecosystems, supporting evolving business needs.If customers utilize Network Edge VNFs they can access a control plane to manage traffic flows seamlessly across these environments, ensuring workloads are placed where they deliver the most business value with a predictable cost. It also enables the deployment of virtual network functions such as firewalls, load balancers, and SD-WAN as software services, reducing hardware overhead and improving consistency. Together, they create a common network fabric that simplifies operations, supports workload mobility, and maintains governance across diverse environments. As a result, customers can minimize complexity by centralizing management, turning what used to be a fragmented sprawl into a unified, agile, and compliant AI operating model. Q: How to predict and control network spend when running apps across multiple clouds? A. As AI and multicloud workloads scale, network costs often become the least predictable element of total spend. Massive east-west data movement between training clusters, storage systems, and clouds can trigger unexpected egress and transit fees, while variable routing across the public internet adds latency and complicates cost forecasting. These factors can make it difficult for IT and finance teams to align budgets with actual workload behavior. A more sustainable approach is to build predictability and efficiency into the interconnection layer. By replacing public internet paths with dedicated, software-defined connections, organizations can achieve elastic bandwidth scaling while having predictable billing. This model not only ensures stable and reliable network performance but also enhances cost transparency, enabling businesses to optimize their connectivity expenses while supporting evolving operational demands. Equinix Fabric supports this model by enabling private, high-performance connections to multiple clouds and ecosystem partners from a single port, fostering predictability in network performance. Equinix Network Edge complements this by allowing network functions such as firewalls, SD-WAN, and load balancers to be deployed virtually, reducing CapEx and aligning spend with actual utilization. Together, they deliver a unified network architecture that stabilizes performance, enhances cost transparency, and enables organizations to scale bandwidth effectively while managing costs in alignment with their AI and multicloud workloads. Q: What’s the best way to ensure my AI workloads don’t go down if one cloud region fails? A. AI workloads are highly distributed, and regional outages can disrupt training, inference, or data synchronization across clouds. Relying on a single provider or static internet-based paths introduces latency and failure risks that can cascade across operations. Building resilience into the interconnection layer ensures continuity even when one region or cloud becomes unavailable. The key is to design for multi-region redundancy with pre-established, high-performance failover paths. By maintaining secondary connections across clouds and geographies, organizations can automatically reroute workloads and traffic without interruption or loss of performance. Equinix Fabric enables this design by providing software-defined, private connectivity to multiple cloud providers and regions. Equinix Network Edge complements it by supporting virtualized global load balancers, SD-WAN, and firewalls that dynamically redirect traffic and enforce security policies during failover. Together, they create a resilient, globally consistent architecture that maintains availability and performance even when individual cloud regions experience disruption.Tech Note: Using BGP Local-AS with Equinix Internet Access over Fabric (EIAoF) and Network Edge
Welcome to the Equinix Community! We know you’re always looking for ways to maximize your connectivity, and sometimes technical limitations can be a hurdle. This post dives into a handy BGP feature called Local-AS that helps our Equinix Internet Access over Fabric (EIAoF) customers navigate a current setup requirement. We’ll provide a brief description of BGP Local-AS, a high-level overview of how it works in practice, and how it enables you to maintain your public Autonomous System Number (ASN) while at the same time using an Equinix-assigned private ASN currently required by EIAoF. What is BGP Local-AS? BGP Local-AS is a feature supported by most major network vendors that lets a BGP-speaking device appear to belong to an ASN different from its globally configured one. While it’s not part of the official BGP standard, it’s a powerful feature typically used during major network events like merging autonomous systems or transitioning to a new ASN. For EIAoF customers, it provides a clean, effective method to accommodate the current requirement to use a private ASN for your BGP session. The best part? Once EIAoF is updated to fully support public ASNs, you can simply remove the Local-AS configuration, or even leave it in place until you’re ready for a future transition! How BGP Local-AS Works with EIAoF The picture below provides only the relevant configuration snippets needed to convey the concept of using BGP Local-AS. It's presented using classic, non Address Family Cisco configuration syntax. An explanation of the relevant configuration variables and commands are explained below the picture. This short post is only intended to help readers understand how local-as can be used with EIAoF and is not intended to represent a complete BGP configuration nor an in-depth overview of local-as capabilities. Figure 1 – BGP Local-AS Example Dynamic Configuration Variables Equinix Assigned Primary IPv4 Peering Subnet: 192.0.2.0/30 Equinix Assigned Secondary IPv4 Peering Subnet: 192.0.2.4/30 Equinix Assigned Private ASN: 65000 Customer Public Autonomous System Number (ASN): 64500 Customer Public IPv4 Prefix: 203.0.113.0/24 A Side Key Configuration Command References router bgp 64500 ⬅️ Customer’s public ASN Customer router BGP ASN. This is the ASN BGP speakers use for peering (when not using local-as.) neighbor 192.0.2.1 remote-as 15830 ⬅️ EIA public ASN Defines the BGP connection to the EIA edge gateway router. neighbor 192.0.2.1 local-as 65000 ⬅️ Equinix Assigned Private ASN This makes the EIA edge gateway see this peer as belonging to the private AS 65000 instead of 64500. This router will also prepend AS 65000 to all updates sent to the EIA edge gateway. EIA Edge Gateway Router A *> 203.0.113.0 192.0.2.2 0 0 65000 64500 i The output above is an excerpt from the BGP table on the example EIA router A edge gateway. The fact this prefix appears in the BGP table with the associated ASNs confirms successful peering between EIA and the customer router using AS 65000. You can also see the AS-PATH of the prefix received lists the customer’s real AS, 64500, as the origination AS with the private ASN, 65000, prepended to it. When EIA advertises this prefix to external peers it will strip the private ASN, 65000, and prepend 15830 in its place. This will result in external peers seeing the 203.0.113.0/24 prefix with an AS-PATH of 15830 64500. Important Routing Security Requirement To ensure successful service provisioning with EIA, you must have the necessary Route Objects (RO) defined. Route Object (RO) When using an Equinix-assigned private ASN, you are required to create, or have created, a Route Object (RO) that matches your advertised prefix with the Equinix ASN (15830). If this RO does not exist your EIA service order will fail. Best Practice Recommendations It is recommended to also create a Route Origin Authorization (ROA) using RPKI for improved security and validation. We also strongly recommend that you ensure there is an RO that matches your public ASN to the prefix in addition to the one for ASN 15830. If you have any questions, please share in the comments below! 👇3 Multicloud Network Designs for Simplified Multicloud Connectivity
If your organization is juggling multiple clouds, there’s a good chance network complexity is clogging progress. What if there were structured, strategic configurations that simplify multicloud connectivity—so you could scale with confidence and clarity?New Term-Based Discounts for Equinix Fabric Are Here
We're excited to introduce Equinix Fabric Term-Based Discounts for inter-metro i.e. remote virtual connections (VCs) between your own assets and to your service providers including hyperscalers such as AWS, Azure, Google Cloud and Oracle. This new pricing option is designed to help you save more while enjoying the high-performance connectivity you rely on. What's New? You now have the option to select 12, 24, or 36-month contracts for inter-metro VCs from your Fabric ports, Network Edge virtual devices and Fabric Cloud Router instances. Here's how you'll benefit: Lower Monthly Rates: Save between 15% and 50% compared to on-demand pricing. For example, a 1 Gbps inter-metro virtual connection between London to New York drops from $1005/month to just $503/month with a 36-month term-based plan. See how much you can save using the Fabric pricing calculator, accessible via the Fabric portal. Simple Provisioning: No approvals required. Just select your term in the self-service portal or Fabric API and enjoy the savings. Broad Capability Support: Applicable across Point-to-Point (EPL & EVPL), Multipoint-to-Multipoint (EP-LAN & EVP-LAN) and IP-WAN services supported by Fabric Cloud Router. Also supported for Z-side service tokens. Predictable Cost Structure: Term based contracts provided set monthly rates, making it easier for you to manage your annual budget. Things to Note Discounts are available only for inter-metro VCs (intra-metro i.e. local VCs are not eligible. Discounts are currently not supported on Network Edge virtual devices to AWS, but are coming soon. Term-based discounts cannot be added to existing VCs, so you’ll need to create a new VC with your chosen term. Why This Matters By locking in discounted rates, you can optimize costs and achieve predictable spending without sacrificing performance, reliability, or flexibility. This is the perfect opportunity to create cost-efficient connectivity solutions tailored to the demands of your business. We'd Love to Hear From You Tap into these savings today by selecting a term-based discount during your next VC provisioning. We’d love to hear how this pricing option benefits your operations. Share your feedback with the team!Need DDI In the Cloud? Infoblox is Now on Network Edge
Hey everyone—just popping in with some exciting news for those of you building out your virtual networking environments! Infoblox NIOS DDI is now available on Equinix Network Edge 🙌 That means you now have another core piece of your networking stack—DNS, DHCP, and IP address management (IPAM)—available as a virtual networking function alongside firewalls, routers, SD-WAN, and more. What this unlocks for you? Whether you’re working across hybrid or multi-cloud environments, having your full stack available virtually means you can deploy faster, scale smarter, and keep everything centralized—all without touching hardware. Already using Infoblox on-prem? You can now extend those services to the cloud and manage everything through Network Edge. Need better automation? Infoblox helps eliminate manual IPAM tasks and streamlines network provisioning. Focused on security? Built-in Protective DNS helps block threats before they reach your network. Building for scale? Combine Infoblox with other Network Edge VNFs for a complete, cloud-ready networking solution. And yes—it’s available to deploy via the portal, APIs, or Terraform, so you can integrate it however you work best. 👉 Check out the blog post here if you want a deeper dive or to explore use cases. Would love to hear how you’re thinking about virtualizing more of your network stack—or if you’ve already started using Network Edge and want to add Infoblox into the mix. Drop your thoughts or questions below!Boost Your Multicloud Strategy with High-Performance Connectivity
Experts from Equinix and Oracle are going to show you how to boost your multicloud strategy with high-performance connectivity. Discover how Oracle Cloud Infrastructure (OCI) via FastConnect enables essential private, dedicated, high-bandwidth connectivity. Key Topics: Predictions and trends for multicloud Oracle Cloud Infrastructure (OCI) distributed cloud Multicloud use cases and challenges How to connect to multiple clouds High-performance cloud-to-cloud connectivity solution Multicloud data integration architecture Fabric Cloud Router Learn more about Equinix and Oracle solution
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