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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Contabo is a German budget cloud provider offering VPS and dedicated servers with unusually high core counts and RAM at very competitive prices, making it popular for CPU-intensive workloads and teams that need large amounts of RAM without paying hyperscaler rates. While not a dedicated GPU cloud, Contabo offers some GPU-enabled VPS configurations for teams that need affordable compute with GPU access at budget pricing.
- Very competitive pricing on VPS and dedicated servers
- High core counts and RAM at budget prices
- German infrastructure with EU data residency
- Long track record since 2003
- Limited GPU catalog — primarily CPU-focused
- Support quality varies
- Less suitable for GPU-intensive AI workloads
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
Contabo
Budget VPS and dedicated servers with high core and RAM counts