Cerebrium

Cerebrium Key Insights

What is Cerebrium? 

Cerebrium is a serverless AI infrastructure platform designed to simplify machine learning models' deployment, scaling, and monitoring. It abstracts away complex infrastructure management, allowing developers to focus on building AI applications without worrying about hardware and software intricacies. Cerebrium utilizes a variety of GPUs and implements advanced techniques like quantization, LoRA, and pruning to optimize model performance and cost efficiency.

Cerebrium Key Features

• Effortless Model Deployment: Deploy models using major frameworks like PyTorch, ONNX, and XGBoost with just one line of code. Simplify integration with one-click deployment processes.
• Serverless GPU Infrastructure: Run machine learning models in the cloud with scalability and high performance. Pay only for the resources consumed.
• GPU Variety: Choose from over 8 different GPU types, including H100, A100, and A5000, providing flexibility for various workloads.
• Low Latency: Adds less than 50ms to request latency, ensuring real-time responsiveness.
• Advanced Monitoring: Integrate with observability platforms like Arize and AWS S3 for proactive model performance monitoring and troubleshooting.
• Automatic Scaling: Handle requests from a few to thousands seamlessly without manual intervention.
• SOC 2 Compliance: Ensures data security and privacy for businesses handling sensitive information.

Cerebrum Subscription Plans

Cerebrium Alternatives

1. Vertex AI

Google's comprehensive machine learning platform offers end-to-end model development, deployment, and management capabilities. It provides a unified AI platform with pre-trained and custom tooling options.

2. Union Cloud

A managed orchestration platform by Union.ai that focuses on data and machine learning workflows. It offers features for workflow management, data processing, and model deployment.

3. Amazon SageMaker

AWS's fully managed machine learning platform that covers the entire ML lifecycle. It provides tools for building, training, and deploying machine learning models at scale.