Which AI hosting approach is right for your organization?

Every organization deploying AI eventually hits the same question: where should this actually run? The answer shapes your cost structure and how much control you have when something goes wrong. There are three real options: software-as-a-service (SaaS) AI tools like Copilot and ChatGPT, self-hosted infrastructure, and colocation. Each comes with a fundamentally different set of trade-offs.

Pay-per-token costs have really begun to balloon over the past year, which is exactly why the other two options deserve a serious look.

Tools like Microsoft Copilot, ChatGPT Enterprise, and Google Gemini give your team AI capabilities without standing up any infrastructure. You sign up, the vendor manages everything, and you pay for what you consume, measured in tokens: the units of text and data processed with each query or agent task.

When SaaS AI tools make sense

• Exploratory or low-volume use, where consumption is unpredictable and the priority is speed to value.
• Teams that need access to frontier models without managing model updates or infrastructure.
• Burst workloads, meaning periodic high-intensity tasks where you need scale for a defined window.

Where SaaS AI costs become a problem

Token-based pricing is elastic in both directions. When usage grows, and it does, the bill grows with it. An internal AI assistant that starts at a manageable monthly cost can double or triple as adoption spreads across the organization, with no hard ceiling unless you deliberately build one in.

Uber learned this firsthand. The company rolled out Claude Code to its engineering team in December 2025, encouraged adoption through internal leaderboards, and watched agentic usage jump from 32% of its 5,000-person engineering organization in February 2026 to 84% by March. By April, Uber had burned through its entire planned 2026 AI budget, with three quarters of the year still ahead. Monthly application programming interface (API) costs per engineer ranged from $500 to $2,000, and the chief technology officer (CTO) told The Information the company was “back to the drawing board” on AI budgeting. The tools weren't failing: 70% of committed code was now AI-generated. The pay-per-token pricing model just wasn't built for that scale of daily use.

Uber isn't an outlier. The FinOps Foundation's 2026 State of FinOps Report tracked a dramatic shift: two years ago, 31% of FinOps teams managed AI spend. Today, 98% do. AI cost management has become the top forward-looking priority for practitioners, and governing spend before it's committed now ranks among the discipline's top goals. For always-on workloads like inference pipelines, document processing, or internal assistants used across an entire organization, pay-per-token pricing is structurally mismatched to the workload.

Data sovereignty adds another layer. Sending sensitive business data to a third-party platform introduces compliance questions that some industries can't easily answer.

Running your own graphics processing unit (GPU) infrastructure in your own facility gives you full control over the model, the data, and the economics. You purchase or lease hardware, host an inference model locally, and your monthly cost is fixed regardless of how much the model is used. There are no tokens. Usage doesn't change your bill.

When self-hosted infrastructure makes sense

• Organizations with strict data residency or air-gap requirements.
• High-volume inference workloads where the math clearly favors fixed infrastructure costs over per-token pricing.
• Teams that need deep customization of the underlying model stack.

Why most organizations don't go this route

Self-hosting in your own facility transfers every layer of operational responsibility to your team. Power density for GPU workloads is higher than most facilities are built for, and cooling, physical security, redundant power, and network connectivity all become your problem to solve. Hardware lead times for GPU systems now stretch from several months to a year. Refresh cycles require capital planning. When a GPU node fails at 3 a.m., your team coordinates the repair.

For most organizations, that operational burden is the reason this option stays theoretical. The economics work on paper. The staffing and facility requirements make it impractical.

Colocation sits between the other two options in a way that resolves most of the trade-offs. Your AI workload runs on dedicated hardware in a facility built for it. Power, cooling, physical security, and network connectivity are handled by the colocation provider. You get the predictable, fixed monthly cost of self-hosted infrastructure without building or managing a data center.

For inference workloads specifically, this matters a great deal. Hosting a model in colocation means your monthly cost is the same whether it processes a thousand queries or a million, with no tokens and no surprise invoices when adoption takes off.

When colocation makes sense

• Always-on inference workloads that run at steady-state capacity.
• Latency-sensitive applications, where dedicated GPUs deliver consistent time-to-first-token (TTFT) without contention from shared infrastructure.
• Organizations that need dedicated hardware and data control without the overhead of running their own facility.
• Teams where SaaS AI tool costs have become difficult to forecast or justify to finance.

Owning the stack, whether self-hosted or in colocation, also gives you direct visibility into token consumption and latency. For agentic AI in particular, those two numbers tell you whether an autonomous workflow is actually scalable and cost-effective, instead of finding out when the invoice arrives.

What the planning looks like

Colocation requires more upfront planning than deploying a SaaS AI tool. Hardware lead times are real, and capacity needs to be forecasted ahead of demand. Working with a partner who manages the operational layer removes most of that friction.

The Million Dollar Question

If your team is running AI workloads continuously and the cost question keeps surfacing in finance reviews, the structure of how you're hosting those workloads is worth examining. Pay-per-token pricing doesn't get cheaper as adoption grows. Building your own data center is a commitment most IT organizations aren't positioned to take on.

Colocation offers a third path: the economics of dedicated infrastructure, without the operational burden of running the facility yourself.

Lunavi works through the actual hosting economics with you: your workloads, your utilization, your timeline, so you're making the decision with real numbers. Whatever the right hosting mix looks like for you, we'll help you land on the option your workloads and your budget can both live with.

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