Cerebrium is a cutting-edge AI tool that empowers developers to deploy and manage their AI models with ease. Built by engineers for engineers, Cerebrium offers unparalleled flexibility, scalability, and observability, enabling you to iterate quickly, monitor performance in real time, and ensure your models are always up and running.
Developer-Friendly: Designed by engineers for engineers, Cerebrium prioritizes flexibility and iteration, enabling rapid development cycles.
GPU Variety: Offers a wide range of GPUs, including H100’s, A100’s, and A5000’s, to cater to diverse model requirements.
Infrastructure as Code: Allows you to define your environments in code, eliminating the need to manage infrastructure manually.
Volume Storage: Provides direct mounting of file or model weights to your code, removing the hassle of managing S3 buckets.
Secure Credentials: Integrates frameworks and platforms using your secure credentials, ensuring data protection.
Hot Reload: Facilitates rapid iteration by allowing you to change code and see the results live on a GPU container.
Streaming Endpoints: Enables real-time streaming of output to users, eliminating waiting time for results.
Real-time Logging: Offers real-time logs across builds and requests for quick debugging.
Increased Efficiency: Streamlines AI model deployment and management, saving time and resources.
Improved Scalability: Ensures seamless scaling of AI models to meet changing demands without downtime.
Enhanced Observability: Provides comprehensive monitoring and logging capabilities for proactive issue identification and resolution.
Rapid Iteration: Facilitates rapid development cycles by enabling quick code changes and real-time updates.
Cost Optimization: Offers detailed cost breakdowns to help you optimize resource allocation and manage expenses effectively.
Deploying and managing AI models for image generation, natural language processing, speech recognition, and more.
Scaling AI models to handle increased traffic or usage spikes without compromising performance.
Monitoring model performance in real time to identify and address issues promptly.
Iterating on AI models quickly and efficiently by leveraging hot reloading and real-time logging.
Streaming output from AI models to users in real time, enabling interactive applications and experiences.
