GPU Cloud vs Bare Metal: Where to Run AI Inference

You trained your model. It works. Now you need to put it somewhere that can serve a million requests a day without melting your credit card. The choice is essentially: rent a GPU in the cloud, or rent a physical GPU server somewhere.

Both work. Both have very different costs, very different operational headaches, and very different break-even points. Picking the wrong one means overpaying by 5x or being stuck with hardware you cannot move.

This guide is the comparison I wish someone had given me before I spent six months learning it the hard way.

The Two Models, Quickly

GPU cloud (also called “cloud GPUs” or “GPU instances”) is what AWS, Google Cloud, Azure, Lambda Labs, CoreWeave, RunPod, and others sell. You spin up a virtual machine with one or more GPUs attached. You pay by the hour or by the second. You can resize up or down in minutes. When you are done, you tear it down and stop paying.

Bare metal GPU servers are physical machines in a data center. You rent the whole box for a month or a year. The hardware is dedicated to you. You control everything from the kernel up. Performance is predictable. You cannot resize in minutes; changing hardware means a migration.

Both serve the same fundamental purpose. They are optimized for very different use cases.

When GPU Cloud Wins

Spiky or unpredictable traffic. If you run an AI chatbot that gets 100 requests one hour and 10,000 the next, cloud wins handily. Spin up 4 GPUs during the spike, tear them down 30 minutes later. With bare metal you are paying for the worst-case capacity 24/7.

Early-stage products. When you do not yet know what your traffic pattern is, cloud lets you experiment. Try a small instance. Try a big one. Try a different GPU type. All without commitment.

Latest hardware. Cloud providers are first in line for new GPU generations. When an H100 becomes available, the cloud gets it in weeks. Bare metal providers take longer because they have to receive, rack, and test physical hardware.

Geographic distribution. If your users are global, cloud lets you put inference close to them. AWS, GCP, and Azure have dozens of regions. Bare metal usually means one or two data centers.

No ops team. Cloud providers handle hardware failures, network issues, and driver compatibility. You focus on your model. With bare metal, you handle all of that yourself or pay someone to.

The tradeoff: cost predictability. Cloud bills are noisy. Bare metal bills are flat. Finance teams tend to prefer the latter.

When Bare Metal Wins

Steady, high-volume workloads. If you run inference 24/7 at a predictable rate, bare metal is almost always cheaper. Cloud has a premium for the flexibility. Bare metal does not.

Latency-sensitive workloads. Cloud GPUs sit behind virtualization layers that add 5 to 15 percent latency overhead. For most inference this does not matter. For high-frequency trading, real-time video, or anything where every millisecond counts, bare metal wins.

Custom hardware. Need a specific GPU with a specific PCIe layout, paired with specific NICs and storage? Bare metal. Cloud only offers what the provider has configured.

Predictable cost. Cloud bills fluctuate. Bare metal is a fixed monthly invoice. CFOs love this. Engineering teams sometimes love it too because there is no surprise bill.

Data gravity. When your training data is huge and you cannot move it cheaply, the GPU needs to come to the data. Bare metal in the same data center as your storage is the move.

The tradeoff: flexibility. Changing instance types means a migration. Hardware failures take longer to fix because someone has to physically swap a component.

Real Cost Comparison

Numbers, because vague claims are useless.

A single NVIDIA H100 80GB in 2026 rents for roughly:

• Cloud (on-demand): $2.50 to $4.50 per hour
• Cloud (reserved, 1-year): $1.80 to $2.80 per hour (effective)
• Cloud (spot/preemptible): $0.80 to $1.80 per hour (when available)
• Bare metal dedicated: $1,400 to $2,200 per month
• Bare metal reserved (1-year): $1,000 to $1,600 per month

Converting bare metal to hourly: $1,600/month / 730 hours = $2.19/hour.

At full utilization, bare metal is roughly 30 to 40 percent cheaper than on-demand cloud for the same GPU.

At 50 percent utilization (the spike case), cloud spot instances beat bare metal because you only pay for what you use.

The break-even is somewhere around 60 to 70 percent sustained utilization. Below that, cloud wins. Above that, bare metal wins.

For an LLM serving 10 requests per second 24/7, you are well above 70 percent utilization. Bare metal.

For a batch job that runs 4 hours per day, you are at 17 percent utilization. Cloud spot.

Run the math for your actual usage pattern, not your peak.

Real Options in Each Category

GPU cloud providers worth looking at:

• AWS EC2 P5 instances (H100)
• Google Cloud A3 instances (H100)
• Azure ND H100 v5 series
• Lambda Labs (often cheapest, single-region)
• CoreWeave (specialized, fast provisioning)
• RunPod (popular for startups, good pricing)
• Vast.ai (marketplace, variable quality)
• Together AI, Fireworks, Anyscale (managed inference)

Bare metal GPU providers:

• InterServer (yes, including dedicated GPU options on their infrastructure line)
• Equinix Metal
• PhoenixNAP
• Hetzner (limited GPU availability but cheap)
• OVHcloud
• Local colocation facilities

The cloud list is longer because more companies have entered that market. The bare metal list is shorter because it is a more capital-intensive business.

How to Decide for Your Use Case

A few questions that cut through the noise.

What is your sustained GPU utilization? Run it for a week and measure. Under 50 percent, cloud. Over 70 percent, bare metal. In between, it depends on other factors.

How spiky is your traffic? A 10x ratio between peak and off-peak is a strong cloud signal. A 2x ratio can go either way.

What is your latency budget? Under 50 ms p99 means bare metal or specialized inference providers. Above 200 ms means cloud is fine.

What is your team’s operational capacity? No dedicated ops? Cloud. Strong ops team? Bare metal is viable.

How important is hardware flexibility? If you need to test different GPU types frequently, cloud. If you have one well-understood config, bare metal.

What is your data gravity situation? Massive datasets in one place push you toward bare metal in the same facility.

In practice, most teams end up using both: cloud for experimentation and spikes, bare metal for the steady-state production load.

A Common Hybrid Pattern

The most cost-effective pattern looks like this.

Tier 1: Always-on baseline. Bare metal. Handles 80 percent of normal traffic. Predictable cost.

Tier 2: Burst capacity. Cloud spot or on-demand. Handles spikes. Scales out and back automatically.

Tier 3: Edge inference. For global users, lightweight models run close to them via inference providers like Cloudflare Workers AI, Together, or similar. Cuts tail latency and reduces load on the central cluster.

This combination handles 99 percent of real-world workloads. Pure cloud or pure bare metal is rarely optimal.

If you are already hosting with InterServer, their VPS and dedicated server lines can host the inference tier that runs close to your database, while the cloud burst tier handles spikes. Many teams start with InterServer for the steady-state load and add cloud only when they outgrow it.

What You Can Do Today

You do not need to commit to a multi-year strategy. Start here.

Step 1: Profile your traffic. Run for a week. Note peak, average, and the ratio between them.

Step 2: Calculate your break-even. If average utilization is over 70 percent, get a bare metal quote for your preferred GPU. If under 50 percent, run a cloud cost calculator.

Step 3: Pilot the cheaper option. Most cloud providers let you start with $100 in credits. Most bare metal providers let you do a 1-month commitment to test.

Step 4: Measure your actual cost per million inferences. Multiply out. Compare. This is the number that matters, not the hourly rate.

Step 5: Build for portability. Use containers, model serving frameworks with standard interfaces, and infrastructure-as-code so you can move between cloud and bare metal without rewriting.

Conclusion

GPU cloud and bare metal serve different needs. Cloud wins for flexibility and variable load. Bare metal wins for steady, high-volume workloads. Most production teams end up using both.

The trap people fall into is treating this as a religion. “We are a cloud shop.” “We are bare metal only.” Those positions cost real money. The right answer is “we use whichever is cheapest for each workload” and you change it as the workload changes.

Start with cloud if you are still learning your traffic shape. Move to bare metal when the numbers justify it. Add cloud burst on top when spikes happen. Stay flexible because your traffic pattern will change as you grow.

The hardware does not care where it lives. Only your bill does.