Cerebrium
Cerebrium offers a top-tier serverless infrastructure that enables teams to build, test, and deploy AI applications efficiently with minimal latency and high reliability. The platform provides blazing
What is Cerebrium?
Cerebrium is a serverless platform designed to help teams deploy AI models and applications without managing infrastructure. You write your code, push it to their servers, and the platform handles scaling, performance optimisation, and monitoring. It's particularly useful if you're building inference endpoints for machine learning models and want to avoid the complexity of setting up and maintaining your own servers. The platform supports popular frameworks like TensorFlow and PyTorch, offers fast cold starts (meaning your application responds quickly when first called), and includes built-in tools for logging and cost tracking. With 99.999% uptime and options to run on different cloud providers, it targets teams who need reliability without the operational overhead.
Key Features
Serverless deployment
Upload your code and Cerebrium handles provisioning, scaling, and management automatically
Fast cold starts
Applications respond quickly even when idle, reducing latency for end users
TensorRT support
Optimised inference engine for running AI models efficiently
Real-time logging and observability
Monitor application behaviour and performance as it runs
Cost management tools
Track and optimise spending on compute resources
Multi-cloud capacity
Run workloads across different cloud providers based on your needs
Pros & Cons
Advantages
- No infrastructure management required; focus on your application rather than servers
- Generous free tier with $30 credit lets you test without immediate cost
- High reliability with 99.999% uptime guarantee suits production applications
- Automatic scaling handles variable traffic without manual intervention
- Built-in observability tools reduce the need for external monitoring solutions
Limitations
- Serverless platforms typically have cost surprises if you don't monitor usage carefully
- Less control over underlying infrastructure compared to self-managed solutions
- Vendor lock-in risk if you build heavily on platform-specific features
Use Cases
Deploying machine learning inference endpoints that serve predictions to applications
Building chatbot backends powered by large language models
Running batch processing jobs for image recognition or data analysis
Creating API endpoints for real-time model inference without managing servers
Prototyping AI applications quickly before scaling to production infrastructure