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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Koyeb is joining Mistral AI to build the future of AI infrastructure. The platform offers serverless GPU compute with automatic scaling to zero and per-second billing. GPU catalog: B200, H200, H100, A100 SXM, RTX PRO 6000, A100, L40S, RTX A6000, L4, RTX 4000 SFF Ada. N300s (NVIDIA Rubin) available via request. 7 regions in US, EU, and Asia. Saving plans up to 50% off. Kubernetes deployment in 120 seconds. Check koyeb.com/pricing for current rates.
- B200, H200, H100 — competitive serverless pricing
- Joining Mistral AI — strong AI infrastructure future
- Automatic scaling to zero — no idle costs
- Per-second billing, Kubernetes in 120 seconds
- 7 regions in US, EU, and Asia
- N300s (NVIDIA Rubin) available via request
- Acquisition by Mistral AI may change product direction
- Fewer regions than major cloud providers
- Less mature ecosystem vs CoreWeave or Lambda
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
Koyeb
Joining Mistral AI — B200, H200, H100, serverless GPU with scale-to-zero