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
No reviews yet — add yours ↗
EcoHash is a GPU cloud offering NVIDIA RTX PRO 6000 Blackwell instances with 96GB GDDR7 VRAM alongside an open-model inference API, making it one of the few providers offering the latest RTX PRO 6000 hardware for large-context AI inference and training. The open-model API enables developers to access popular open-source models without managing infrastructure. A strong choice for teams that need maximum VRAM on RTX-class hardware for large-model inference.
- RTX PRO 6000 Blackwell — 96GB GDDR7 VRAM
- Open-model inference API
- Large-context inference capability
- Competitive RTX PRO 6000 pricing
- Very new provider — limited track record
- Smaller scale vs established providers
- Limited documentation and ecosystem
No reviews yet — add yours ↗
Live GPU Pricing
Region Coverage
Popular Comparisons
Provider pages
Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
EcoHash
RTX PRO 6000 GPU cloud with an open-model inference API