Cerebrium
Serverless GPU infrastructure for real-time AI with sub-second cold starts and instant autoscaling
Paid·Technical·API available
Key strengths
Sub-second cold starts via GPU & memory snapshottingElastic autoscaling across 2500+ GPUs and multiple regionsNo code rewrites — run any Dockerfile or entry point as-isSOC 2, HIPAA, GDPR, ISO compliance with gVisor isolationNative OpenTelemetry observability with real-time metrics and logs
Paid only
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Getting Started with Cerebrium
Installation & Deployment
Install the Cerebrium CLI and deploy a training or inference workload directly from your terminal:
# Install CLI
pip install cerebrium
# Deploy a training script on 8x H100 GPUs
cerebrium run training_script.py::train --hardware HOPPER_100:8
Cerebrium handles containerization, scheduling, and scaling automatically — no Kubernetes manifests or Terraform required.
Key Capabilities
- Endpoints: REST API, streaming, and WebSocket endpoints supported out of the box
- Custom Dockerfiles: Bring your own image; Cerebrium runs it exactly as defined
- Autoscaling: Instant scale-out with concurrency & batching controls; no capacity planning needed
- GPU Types: 12+ options including H100 (Hopper), with multi-GPU job support
- Persistent Storage: Distributed storage available for checkpoints and artifacts
- Observability: Full OpenTelemetry integration for metrics, logs, and scaling events
- CI/CD: Gradual rollouts, versioned deployments, secrets management built-in
- Security: gVisor isolation per workload, SOC 2 / HIPAA / GDPR / ISO certified, data residency controls
Supported Frameworks
vLLM, SGLang, TensorRT-LLM, Triton Inference Server, Pipecat, LiveKit, WandB, Gradio, Stable Diffusion XL, Twilio