GPU rental or LLM API: the math that decides

The framing error

"Renting a GPU is cheaper than API per-token pricing" is the most common miscalculation in AI infrastructure. The per-token comparison is real. The hidden costs are not: instance management, idle capacity, model serving, monitoring, failover, and the engineering hours to build it all.

Break-even math

A single H100-class instance rents around $2–$4/hour on-demand in 2026. A month of 24/7 usage is roughly $1,500–$3,000. If your workload actually saturates that GPU, self-hosting can beat per-token API pricing. If it does not — and most workloads do not — you pay for idle capacity.

Plugsky's break-even calculator computes this for your exact workload.

When the API wins

• Variable or bursty traffic — the API absorbs spikes.
• Multiple models needed — one API key, 30+ models.
• Small team — no infra engineers to run GPU clusters.
• Fast iteration — deploy new models without re-provisioning.

When renting GPUs wins

• Stable, high saturation — 24/7 heavy inference.
• Strict data control — everything must stay in your perimeter.
• Fine-tuning and training — not just inference.

And note: Plugsky supports both sides of this — on-prem for control, API for convenience, and the GPU Share Network if you own GPUs that sit idle.

FAQ

Is an API ever cheaper than self-hosting?

For most usage patterns, yes — because you pay only for tokens, not idle GPUs. The calculator shows your exact crossover point.

What is the latency penalty of an API?

Measured medians on the public Plugsky API are 0.24–0.9s depending on model — comparable to self-hosted for most applications.