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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Novita has grown into a dual-product platform: a GPU cloud for training and fine-tuning (H100 SXM, A100, RTX 4090) and a high-volume LLM inference API serving 100+ open-source models at some of the lowest per-token rates in the market. In 2025–2026 Novita expanded its model catalog to include Llama 3.1/3.2, Qwen 2.5, DeepSeek V3/R1, and Mistral variants, and introduced batch inference pricing for high-volume workloads at up to 50% discount. The GPU cloud side offers fast provisioning (typically under 3 minutes), pre-built Docker environments for PyTorch and JAX, and persistent storage volumes. A practical choice for cost-conscious teams that want both raw GPU access and a cheap inference API from one provider.
- Among the lowest per-token LLM inference pricing in the market
- Batch inference API at up to 50% discount for high-volume workloads
- 100+ open-source models including Llama 3.2, Qwen 2.5, DeepSeek R1
- Fast GPU provisioning — typically under 3 minutes
- Pre-built PyTorch and JAX environments reduce setup time
- Smaller provider — cluster scale limited vs CoreWeave or Lambda for very large jobs
- Inference API latency trails Fireworks and Cerebras on speed benchmarks
- Less mature enterprise support and SLA options
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Key differences
Cerebrium
Serverless ML deployment — H100, A100, T4 in seconds via custom containers
Novita
GPU cloud and LLM inference API — H100, A100, and 100+ open-source models