AI Infrastructure Planning: Data Center, Power, and Network Decisions

AI initiatives often begin with a discussion about models, software, and use cases. The harder question is where those workloads will run and how they will connect to data, users, clouds, and business systems.

That is why AI adoption is also an infrastructure decision.

The right data center selection and network strategy helps an organization deliver reliable performance, control cost, and expand without rebuilding the architecture later. The wrong choice creates latency, capacity constraints, poor resiliency, and unexpected spend.

AI workloads are rarely contained in one place. Data may sit in a private environment, a colocation facility, SaaS platforms, and public cloud. Models may run in a different region. Users and applications still need fast, reliable access.

This changes the role of the network.

Flexential's provider-sponsored 2026 State of AI Infrastructure survey included more than 350 enterprise IT decision-makers. Ninety-six percent reported at least one network-related issue affecting AI workloads during the prior year. Seventy-one percent reported excessive latency. Ninety-one percent said fiber availability, carrier diversity, and low-latency connectivity limited AI deployment site selection.

AI adoption without an infrastructure plan leaves too much to chance.

The right facility is not simply a matter of available rack space and a competitive monthly rate. It shapes the organization's ability to support power-dense equipment, connect to cloud providers, access multiple carriers, and grow as AI initiatives move from pilot to production.

Power availability matters. The same Flexential survey found 89 percent of respondents considered reliable grid power when making AI deployment decisions. Fifty-five percent ranked power-cost differences as the leading factor affecting workload location.

Capacity also needs to be planned early. JLL's North America Data Center Report, published February 17, 2026, reported 1 percent vacancy for the second consecutive year. JLL also reported 92 percent of capacity under construction was precommitted through leases or owner-occupied development.

For IT leaders, this means the ideal location is not always the closest or the least expensive. The right location is one that aligns with the workload's power, latency, data, cloud workload placement, resiliency, and growth requirements.

An ISP connection that works well for traditional office traffic may not be the right fit for AI-enabled applications, distributed data, or agentic workflows.

Cisco's 2026 AI Impact on Wide Area Networks report analyzed AI inference traffic from two service-provider networks. Cisco observed roughly fourfold traffic growth over eight months. In a separate empirical test, one agent task generated 450 percent more total traffic than the same task performed manually. These findings come from limited measurement sets and should guide evaluation rather than serve as universal forecasts.

The implication is clear: bandwidth matters, but it is not the only consideration.

Organizations need to assess:

• Network path and latency between users, data, applications, and AI models
• Carrier diversity and network resilience with physically separate entry paths
• Scalability from today's needs to future throughput requirements
• Direct cloud connectivity and the cost of moving data between environments — see the cloud data-transfer costs guide for a detailed evaluation framework
• Service-level commitments, support, and outage response
• The ability to add locations, providers, or cloud connections without starting over

A low-cost circuit with a weak path, limited diversity, or no room to scale can become expensive when performance suffers.

AI cost is becoming a broader technology-management concern, not just a cloud-billing issue. The FinOps Foundation's 2026 State of FinOps report found 98 percent of respondents manage AI spend, up from 63 percent in 2025. The report also documents expanding FinOps responsibility across private cloud and data center environments.

This is why workload placement deserves the same scrutiny as cloud pricing or software selection.

The best environment for an AI workload depends on the value it creates, the sensitivity and location of its data, latency requirements, power density, required interconnections, and expected growth. Some workloads belong in public cloud. Others benefit from colocation, private infrastructure, or a hybrid design.

Use our infrastructure cost framework to compare AI workload placement costs across public cloud, colocation, and hybrid environments.

The goal is not to force every workload into one model. The goal is to build an architecture that supports business outcomes.

Before committing to a data center or ISP, ask:

• Where will the AI model, enterprise data, and users be located?
• What latency level is acceptable for the application?
• What power density, cooling, and rack growth will the workload require?
• Is the facility close to the clouds, data sources, and users that matter most?
• Are multiple carriers available, with diverse building entry paths?
• Does the network support direct cloud connectivity and predictable data-transfer costs?
• What happens if one carrier, route, or facility has an outage?
• Does the contract leave room to scale or change course as the AI strategy evolves?

AI workloads typically require more power density, lower latency, and higher bandwidth than traditional enterprise applications. GPU-based training and inference workloads can draw significantly more power per rack than standard compute. The specific requirements depend on the model size, inference frequency, batch size, and whether the workload is training, fine-tuning, or serving predictions.

Beyond compute, AI workloads depend on fast, reliable access to data. That means evaluating where training data and operational data are stored, how quickly the workload needs to read and write that data, and whether the network path between data and compute introduces latency that affects performance or throughput.

Resilience requirements also vary. A batch training job may tolerate a brief outage differently than a real-time inference API serving customer-facing applications. Defining the availability and recovery requirements for each workload before selecting infrastructure helps avoid over-provisioning in some areas and under-provisioning in others.

Data center location affects latency between the AI workload and the data, users, and systems it depends on. A facility that is physically distant from the primary data source or the end users consuming AI outputs introduces round-trip latency that compounds across every inference call or data fetch. For latency-sensitive applications — real-time recommendations, conversational AI, or time-critical analytics — location can directly affect whether the application performs acceptably.

Location also affects connectivity options. Facilities in major data center markets typically have access to more carriers, more cloud on-ramps, and more diverse network paths than secondary markets. That diversity matters for both performance and resilience. A facility with a single carrier or a single building entry path creates a single point of failure regardless of how redundant the internal infrastructure is.

Power availability and cost vary by geography as well. Some markets have more reliable grid infrastructure, lower power costs, or better access to renewable energy. For power-dense AI workloads, the difference in power cost between markets can be a meaningful factor in total cost of ownership over a multi-year deployment.

The most important network factors for AI workloads are latency, bandwidth, carrier diversity, and path diversity. Latency affects how quickly the workload can access data and return results. Bandwidth determines how much data can move between the workload and its dependencies within a given time window. Both need to be evaluated against the specific throughput and response-time requirements of the application.

Carrier diversity means having access to multiple independent network providers. A single carrier creates a dependency that can affect availability if that carrier experiences an outage or degraded performance. Path diversity means those carriers enter the facility through physically separate routes — separate conduits, separate building entrances, separate upstream infrastructure. Two carriers sharing a common conduit or building entrance may not provide meaningful protection against a physical event.

Direct cloud connectivity is also worth evaluating for workloads that interact with public cloud services. Dedicated private connections to cloud providers — rather than routing traffic over the public internet — can reduce latency variability and data-transfer costs for workloads with significant cloud dependencies.

There is no universal answer. The right placement depends on the workload's data sensitivity, latency requirements, cost profile, scale, and operational constraints. Public cloud offers fast provisioning, broad service availability, and flexibility for workloads with variable demand or early-stage requirements. It also introduces data-transfer costs and potential latency for workloads that move large volumes of data between environments.

Colocation gives organizations control over the physical infrastructure while offloading facility management. It can be a good fit for workloads with predictable capacity requirements, sensitive data that cannot leave a controlled environment, or applications that need direct connectivity to specific carriers or cloud on-ramps. The tradeoff is longer provisioning timelines and capital or contractual commitment.

Private infrastructure — owned or leased compute in a controlled environment — offers the most control but also the most operational responsibility. It may be appropriate for workloads with strict data residency requirements, very high utilization rates that make cloud economics unfavorable, or security requirements that preclude shared infrastructure. Hybrid designs that combine public cloud, colocation, and private infrastructure are common for organizations with diverse workload portfolios.

CorePath Network Group helps organizations evaluate colocation, cloud placement, carrier connectivity, data center capacity, and infrastructure contracts against AI workload requirements. Our advice stays independent and vendor-agnostic.

Our standard sourcing advisory has no direct client advisory fee. CorePath Network Group receives compensation from the selected provider or distribution partner after deployment.

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