Description
Cerebrium delivers serverless GPU infrastructure designed for real-time AI applications, empowering teams to scale globally with ease. The platform facilitates the deployment of diverse AI workloads, including voice agents, video models, and large language models (LLMs), boasting sub-second cold starts and instant autoscaling capabilities. This approach ensures reliability and performance even under sudden bursts of demand, eliminating the complexities typically associated with production-level AI infrastructure.
Built for teams pushing the boundaries of AI, Cerebrium prioritizes production speed without the associated complexity. It offers elastic GPU scaling, allowing workloads to adapt dynamically to changing needs. Users can deploy their code as-is, without requiring rewrites, decorators, or custom SDKs, supporting custom Dockerfiles and private images. The platform provides end-to-end observability for every workload, offering real-time visibility into logs, metrics, scaling events, and system performance, with native OpenTelemetry integration for seamless connection to existing monitoring stacks.
Cerebrium eliminates the need for capacity planning, reservations, or infrastructure management. It provides instant access to thousands of GPUs across multiple clouds and regions, ensuring workloads scale in real time. The infrastructure is built with security and compliance in mind, adhering to standards like SOC 2, HIPAA, and GDPR, and offering data residency options to meet regulatory requirements. Workloads are isolated using gVisor for enhanced security without performance compromise. The platform guarantees 99.999% uptime with multi-region failovers.
Key technologies supported include vLLM, Qwen, and Stable Diffusion XL, with Cerebrium demonstrating significantly faster cold starts compared to traditional Kubernetes solutions like EKS/GKE. The platform supports a wide range of features including WebSocket and REST API endpoints, asynchronous jobs, distributed storage, multi-region deployments, and a variety of GPU types. This comprehensive feature set makes Cerebrium a robust solution for deploying and scaling demanding AI applications.
Cerebrium's Core Features
Serverless GPU infrastructure
Sub-second cold starts
Instant autoscaling
Pay-per-second pricing
No Kubernetes required
Deploy voice agents, video models, LLMs
Bring your own code (no rewrites needed)
End-to-end observability
Native OpenTelemetry integration
SOC 2, HIPAA, GDPR compliance
Data residency options
Isolated container environments (gVisor)
99.999% uptime
Multi-region failovers
Support for vLLM, Qwen, Stable Diffusion XL
How to use Cerebrium?
Deploy: Upload your code or Dockerfile.
Configure: Specify hardware requirements and entry points.
Run: Launch your AI workload on demand.
Monitor: Utilize real-time observability tools.
Scale: Experience automatic scaling based on demand.
Cerebrium's Use Cases
- Real-time Voice Agents
- Video Model Deployment
- LLM Inference
- Generative AI
- AI Tutors
- Digital Avatars
- Embeddings and Reranking
