# Cerebrium Serverless AI infrastructure

## About

A serverless cloud infrastructure platform that makes it easy to build and deploy AI applications scalably and performantly. Run serverless GPUs with low cold starts, choose from over 10 GPU types, run large scale batch jobs and run realtime applications.

- Verified: Yes

## Services

### AI & ML Services
- [AI Application Deployment & Management](https://bilarna.com/ai/ai-and-machine-learning-services/ai-application-deployment-and-management)

### Cloud Computing and Infrastructure
- [Serverless AI Infrastructure](https://bilarna.com/ai/cloud-computing-and-infrastructure/serverless-ai-infrastructure)

## Pricing

- Model: subscription

## Trust & Credentials

### Certifications
- SOC 2 (SOC2)
### Compliance
- ISO, SOC2
### Data Security
- ISO 27001, SOC 2

## Frequently Asked Questions

**Q: How can serverless AI infrastructure improve the scalability and performance of AI applications?**
A: Serverless AI infrastructure enhances scalability and performance by allowing applications to dynamically scale based on demand without the need for manual server management. It supports running serverless GPUs with low cold start times, enabling quick response to workload changes. Features like batching combine multiple requests to minimize GPU idle time and improve throughput, while concurrency management allows handling thousands of simultaneous requests efficiently. Auto-scaling ensures resources are allocated only when needed, optimizing cost and performance. Additionally, support for multiple GPU types and asynchronous job processing enables tailored and efficient execution of various AI workloads.

**Q: What features support real-time AI application deployment in serverless cloud platforms?**
A: Real-time AI application deployment in serverless cloud platforms is supported by several key features. WebSocket endpoints enable low-latency, bidirectional communication, which is essential for interactive AI applications. Streaming endpoints allow native streaming of tokens or data chunks to clients as they are generated, facilitating real-time data flow. Auto-scaling ensures that the infrastructure can handle sudden spikes in traffic by automatically adjusting resources. Additionally, multi-region deployments provide users with fast, local access regardless of their geographic location, reducing latency. These features combined enable developers to build responsive and scalable real-time AI applications without managing underlying servers.

**Q: How does serverless AI infrastructure handle secure management of sensitive information like API keys?**
A: Serverless AI infrastructure handles the secure management of sensitive information such as API keys through integrated secrets management systems. These systems allow users to store and manage secrets securely via a centralized dashboard, ensuring that sensitive data remains hidden and protected from unauthorized access. By abstracting secret handling away from application code, the risk of accidental exposure is minimized. Additionally, secure storage mechanisms and access controls enforce strict policies on who can view or use these secrets. This approach simplifies the process of managing credentials and enhances overall security in AI application deployments.

## Links

- Profile: https://bilarna.com/provider/cerebrium
- Structured data: https://bilarna.com/provider/cerebrium/agent.json
- API schema: https://bilarna.com/provider/cerebrium/openapi.yaml