Overview
Strengths & Best For
Cerebrium is a serverless ML infrastructure platform that deploys H100, A100, and T4 GPU workloads in seconds using custom containers, enabling real-time LLM inference and fine-tuned model serving without managing any infrastructure. Per-second billing and fast cold starts (typically under 5 seconds) make it highly cost-efficient for bursty AI inference APIs and model deployment pipelines. Supports any Python framework — PyTorch, TensorFlow, JAX, vLLM, TGI. Free tier available. A top choice for ML teams that want to ship production inference endpoints quickly with minimal DevOps overhead.
- Cold starts under 5 seconds — fast serverless GPU spin-up
- Custom containers — any Python framework supported
- Per-second billing with free tier
- H100 and A100 availability
- Supports vLLM, TGI, and custom inference servers
- Smaller provider — limited cluster scale
- Fewer regions than major cloud providers
- Not suitable for multi-node distributed training
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CloudGPU rents on-demand GPUs billed per minute, with root SSH access and templates for Ollama, JupyterLab, ComfyUI and LLaMA-Factory. Prices come from CloudGPU's public price feed.
- Per-minute billing — pay only for what you use
- Root SSH access on every instance
- Ready-to-use templates: Ollama, JupyterLab, ComfyUI, LLaMA-Factory
- Wide consumer and data-center GPU selection
- Smaller provider — limited scale vs hyperscalers
- No enterprise SLAs or compliance certifications
- Region information not published per instance
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Live GPU Pricing
Green = lower price. On-demand hourly rates. Updated every 15 minutes.
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
CloudGPU
On-demand GPUs billed per minute — RTX 3090 to A100, root SSH, Ollama and ComfyUI templates