Cerebrium is a serverless GPU platform for running AI workloads in production. Teams deploy voice agents, video models, language models and any custom code, and it scales containers up and down automatically with cold starts of two to four seconds using memory and GPU snapshots. Billing is per second of compute, for example an H100 at $0.000944 per second. The Hobby plan is free plus compute, while Standard costs $100 per month plus compute and adds unlimited apps, 1,000 containers, 30 concurrent GPUs and custom domains.
Machine learning engineers, AI startups and platform teams that want inference infrastructure without running Kubernetes are the core users. Developers can point to an entry point or Dockerfile and ship without rewrites, custom decorators or special SDKs. Hobby suits individuals testing a first deployment, Standard fits teams with ML apps live in production, and Enterprise adds volume discounts, unlimited concurrent GPUs, dedicated Slack support and ML engineering services. Regulated companies benefit from SOC 2, HIPAA, GDPR and ISO compliance plus region-specific data residency.
Documented examples include a voice agent responding in 500 milliseconds, a Twilio phone agent built with Pipecat, an outbound calling agent on LiveKit, and transcribing a one-hour podcast in under two minutes. Others cover serving open models with vLLM, deploying a vision-language model with SGLang, running Triton with TensorRT-LLM, hyperparameter sweeps with WandB, generating images with SDXL and serving embeddings and rerankers at high throughput. Capacity spans more than 2,500 GPUs across several clouds and regions, with logs, metrics and scaling events visible in real time.
