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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Hexabyte is a cloud provider offering VPS and object storage with no egress fees, making it a cost-effective option for teams that need compute and storage without the bandwidth costs that inflate bills on major cloud providers. While primarily a VPS and storage provider, Hexabyte's no-egress-fee model makes it attractive for data-intensive AI workloads that require frequent data transfer between compute and storage.
- No egress fees on VPS and object storage
- Cost-effective for data-intensive workloads
- Simple pricing model
- European infrastructure
- Limited GPU catalog — primarily VPS and storage
- Smaller provider with limited scale
- Less mature ecosystem vs major providers
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
Hexabyte
VPS and object storage with no egress fees