Cerebrium
Serverless GPU infrastructure for deploying and scaling ML models with one command.
About
Cerebrium is a serverless AI infrastructure platform that lets developers deploy machine learning models, including LLMs served through engines like vLLM, to serverless CPUs and GPUs without managing infrastructure. You define your project with a cerebrium.toml and deploy with a single cerebrium deploy command, getting back a REST endpoint for inference.
The platform offers sub-5-second cold starts, auto-scaling, pay-per-use pricing down to the second, GPU selection from A5000s up to H100s, built-in monitoring and logging integrations, and SOC 2 compliance (all vendor-reported). It is positioned for real-time inference workloads such as LLM serving, speech transcription, and video processing, with cost savings claimed versus traditional cloud providers.
What it can do
- One-command serverless GPU deployment
- Sub-5-second cold starts with auto-scaling
- GPU selection from A5000 to H100
- Pay-per-second billing with no idle charges
- Built-in monitoring, logging, and alerts
- Support for vLLM and major ML frameworks
Setup
Sign up at cerebrium.ai, then pip install cerebrium, run cerebrium login and cerebrium deploy from your project.
Prerequisites
A Python ML project, a Cerebrium account, and cloud GPU usage billed per second of compute.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 113; Trending score 70/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.