On-Prem vs. Colocation vs. Cloud for AI Workloads

If you’re scaling an AI inference workload, you’ll eventually face a choice that shapes your infrastructure strategy for years: where does your hardware live?

There are three real options. Here’s how to think through each one honestly — including the cases where one is clearly better than the others.

Option 1: Cloud GPUs (AWS, Azure, GCP, CoreWeave, Lambda)

How it works: You rent GPU compute from a provider by the hour or second. Your model runs on their hardware. You don’t own anything.

Best for:

• Variable or unpredictable workloads
• Early-stage companies that don’t want to make capital hardware commitments
• Batch workloads where you can tolerate latency and cold starts

The real costs: An H100 instance on AWS currently runs around $30–35/hr. At 80% utilization running inference, that’s roughly $21,000–$24,000/month per GPU. Eight GPUs would cost $170–$190K/month.

For reference, you can buy an 8x H100 SXM5 server for around $300–350K outright. At $170K/month in cloud spend, you’ve paid for the hardware in under 2 months.

The hidden costs people miss:

• Egress fees when pulling model outputs to your application layer
• Cold starts — your model weights have to be loaded every time a new instance spins up
• GPU availability — during peak demand periods, your instance type may simply not be available
• No control over the underlying hardware configuration

The honest verdict: Cloud GPUs are the right call when you’re starting out, running variable workloads, or don’t have the capital for hardware. Once you’re spending more than $15–20K/month consistently, the math starts to favor owned hardware.

Option 2: Colocation (Your Hardware, Their Facility)

How it works: You purchase the GPU servers and networking hardware. A data center provides the physical space, power, cooling, and network connectivity. You (or a managed service partner) operate the equipment.

Best for:

• Companies running steady, predictable inference workloads
• Teams that want hardware ownership and control without managing a physical facility
• Organizations with GPU spend above ~$15K/month looking to reduce costs

The real costs: A modern Texas colocation facility charges roughly:

• Cabinet space: $500–1,500/month per 42U cabinet
• Power: $150–250/kW/month (a dense GPU rack at 40kW = $6,000–10,000/month in power)
• Cross-connect: $300–600/month for your internet uplink

Total colo overhead for a dense GPU rack: roughly $7,000–12,000/month. Add your hardware amortization (8x H100 server at $320K over 3 years = ~$8,900/month) and you’re looking at $16,000–22,000/month all-in.

Compare that to $170K+/month for the same GPU count in cloud. The colocation model wins by a wide margin once you’re at steady utilization.

What you need to make it work:

• Capital to purchase hardware (or a financing arrangement)
• A partner to handle procurement, deployment, and ongoing management — unless you have an in-house hardware team
• A clear utilization projection — if your utilization drops below ~50%, the economics get closer

The honest verdict: Colocation is the right model for most companies running serious production AI inference at scale. The hardware economics are compelling, you own your infrastructure, and the operational overhead is manageable with the right partner.

Option 3: On-Premises (Your Hardware, Your Building)

How it works: You own the hardware and it lives in your office or a company-owned facility.

Best for:

• Large enterprises with existing data center infrastructure
• Organizations with strict data sovereignty requirements
• Situations where the data literally cannot leave a controlled environment

The real problems: Running GPU infrastructure on-premises is genuinely difficult:

Power: Most office buildings are wired for 50–200A at 208V per circuit. A fully loaded 8x H100 server draws 10–12kW, requiring dedicated, properly conditioned power circuits. Scaling to multiple servers means electrical infrastructure work.

Cooling: Data centers are engineered for high-density heat dissipation. An office is not. Inadequate cooling causes GPU throttling, reduced performance, and shortened hardware life.

Physical security and uptime: When a GPU server fails at 2am, who goes in? What’s the process? Data centers have 24/7 on-site staff, raised floors, fire suppression, and N+1 power redundancy. Most offices don’t.

The honest verdict: On-premises AI infrastructure makes sense for large enterprises that already have proper data center facilities, or for organizations with specific compliance requirements. For most startups and mid-scale companies, the operational overhead isn’t worth it — colocation gives you the same control with professional infrastructure around it.

The Texas Angle

Texas has an unusually strong colocation market. Austin, Dallas, Houston, and San Antonio all have world-class data center options with available power, competitive pricing, and dense fiber connectivity.

The Austin market specifically is in an interesting moment: the AI startup ecosystem is maturing, GPU workloads are growing, but there’s still relatively little competition in the AI infrastructure services space. Companies moving toward colocation now have good facility options and negotiating leverage.

Making the Decision for Your Company

The honest framework:

If you’re in the “evaluate colo seriously” zone and want to model the actual numbers for your specific workloads, get in touch. We’ll run the math with you.