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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Yotta Infrastructure is an Indian hyperscale data center and cloud provider offering H100 and A100 GPU instances with Indian data residency and enterprise-grade infrastructure for AI training and HPC workloads. On-demand and reserved billing options are available, making it one of the most capable domestic GPU cloud options for Indian enterprises with data sovereignty requirements. A strong choice for APAC-based organizations needing high-performance GPU compute within India.
- Indian data residency
- Hyperscale infrastructure
- H100 availability
- Enterprise SLAs
- Smaller provider — limited scale vs hyperscalers
- Fewer regions than major cloud providers
- Less mature ecosystem and fewer integrations
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
Yotta
Indian hyperscale GPU cloud