Cerebrium vs TensorWave: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Cerebrium and TensorWave. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
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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TensorWave specializes in AMD Instinct MI300X and MI325X GPU instances — the highest-memory GPU accelerators available in any cloud — offering a compelling NVIDIA alternative for large-model LLM inference and distributed AI training via the ROCm ecosystem. On-demand and reserved billing options are available from US-based data centers, with competitive pricing relative to equivalent NVIDIA H100 configurations. The go-to on-demand GPU cloud for teams exploring AMD ROCm or needing massive VRAM for large-context inference.
- AMD MI300X/MI325X — highest VRAM in any cloud
- NVIDIA alternative via ROCm ecosystem
- Competitive pricing vs equivalent H100 configs
- Large-context inference capability
- AMD ROCm ecosystem less mature than CUDA
- Smaller provider — limited scale vs hyperscalers
- Fewer regions — US-only
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
TensorWave
AMD MI300X and MI325X GPU cloud — highest-VRAM accelerators for large-model inference