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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

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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

Ready to try Cerebrium?

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Pricing

Free

Free

Includes $30 credit to get started; suitable for testing and small workloads

Get started with Cerebrium

Click through to Cerebrium and start using it now.

Get Cerebrium
  • Always free
  • No credit card