Why We Built a Cheaper, Faster GPU Cloud for AI Model Hosting

Most AI teams are overpaying for GPU compute. Not because the rates are bad — but because the billing model is wrong.

You rent a dedicated GPU at $2–7/hour. Your model handles a burst of requests, then sits idle. You're paying the same rate whether the GPU is doing inference or doing nothing. For most production workloads, utilization hovers around 15–30%.

That's the problem Cumulus Labs was built to solve.

The GPU Idle Problem

Here's what a typical day looks like for an AI team running inference on a dedicated GPU:

• Morning: Traffic ramps up. GPU runs at 60–80% utilization.
• Midday: Steady state. Maybe 30–40% utilization.
• Evening: Traffic drops. GPU runs at 5–10% utilization.
• Night: Near zero traffic. GPU still running. Still billing.

If you're paying $5/hour for an H100 and your actual GPU utilization averages 25%, you're effectively paying $20/hour for the compute you actually use. That's $3,600/month in waste on a single GPU.

Multiply that by the number of models you're serving, and infrastructure costs become the biggest line item after salaries.

Serverless GPU Inference: Pay Only for Compute

Cumulus takes a different approach. Instead of renting a dedicated GPU, you deploy your model and pay only for the GPU seconds consumed during actual inference.

from cumulus import deploy

# Deploy any model — LLMs, diffusion, VLMs, custom
model = deploy("./my-model")
print(f"Endpoint: {model.endpoint}")

When requests come in, Cumulus provisions GPU compute instantly. When traffic stops, you scale to zero. No idle costs.

The same workload that costs $3,600/month on a dedicated GPU might cost $400–800/month on Cumulus — because you're only paying for the 25% of time the GPU is actually working.

Fast Cold Starts Make This Practical

The catch with serverless GPU has always been cold starts. If it takes 60 seconds to spin up a GPU, scale-to-zero doesn't work for real-time applications.

Cumulus achieves cold starts as fast as 12.5 seconds — fast enough that most users never notice. For latency-sensitive workloads, we keep models warm in memory across a shared GPU pool, so the first request after idle gets sub-second response times.

Any Model, Not Just Popular Ones

Most inference APIs only support a fixed list of popular models. That works until you need your finetuned LLM, your custom LoRA, or the open-source model that shipped yesterday.

Cumulus supports any containerized model. Bring your own weights, your own framework, your own inference code. We handle the GPU scheduling, autoscaling, and failover.

What We're Building Next

This blog is where we'll share the engineering behind Cumulus — how we optimize GPU utilization, reduce cold starts, and build infrastructure that makes AI deployment cheaper and faster.

Coming up:

• Deep dives into our inference engine, ionattention
• Benchmarks comparing GPU cloud providers on real workloads
• Guides on deploying models with the Cumulus SDK

If you're running AI models in production and want to cut your GPU costs, get in touch.