Cerebrium vs Northflank: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Cerebrium and Northflank. 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
No reviews yet — add yours ↗
Northflank is a developer-focused PaaS platform offering GPU compute (L4, A100 40GB, A100 80GB, H100 80GB, RTX PRO 6000) alongside managed containers, CI/CD pipelines, databases, and cron jobs — all in one platform. GPU instances are available on-demand in US and EU regions. The platform is designed for teams that want to run GPU workloads as part of a broader application stack without managing raw cloud infrastructure. A strong choice for ML engineers who want GPU compute tightly integrated with deployment, scheduling, and application services.
- GPU compute integrated with PaaS — containers, CI/CD, databases in one platform
- H100 80GB and RTX PRO 6000 availability
- US and EU regions
- Developer-friendly platform with managed services
- On-demand billing with no minimum commitment
- PaaS model — less control than raw GPU clouds
- Higher per-GPU cost than bare infrastructure
- Smaller GPU catalog than specialist GPU clouds
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
Northflank
PaaS GPU compute — L4, A100, H100, RTX PRO 6000 with managed containers and CI/CD