Supports multiple AI workload types
Run voice agents, video models, LLMs, embeddings, reranking, image generation, and training workloads on the same serverless GPU platform.
Cerebrium
RivendicaCerebrium is serverless GPU infrastructure for real-time AI applications. It lets teams deploy voice agents, video models, and LLM workloads with per-second billing and fast autoscaling.
Overview
Cerebrium is serverless GPU infrastructure for real-time AI workloads. It is designed for teams that want to deploy voice agents, video models, LLMs, and other GPU-backed applications without managing Kubernetes or idle infrastructure.
The platform emphasizes fast startup behavior and elastic scaling. The home page highlights sub-second cold starts, instant autoscaling, and access to thousands of GPUs across multiple clouds and regions, while the pricing page shows pay-per-second billing based on actual compute time.
Cerebrium also supports a straightforward deployment model: bring your own code or Dockerfile, and run it as-is. The site lists observability, multi-region deployments, private Docker images, concurrency and batching, secrets management, asynchronous jobs, and CI/CD with gradual rollouts as part of the platform.
The product is aimed at production AI systems that need low latency and the ability to absorb traffic spikes. The site examples include voice agents, transcription, LLM serving, image generation, and training workflows.
Core capabilities
Sub-second cold-start optimization
Launch containers in seconds and use memory and GPU snapshotting to restore workloads faster, reducing the impact of cold starts.
Instant autoscaling and multi-region routing
Scale workloads automatically without capacity planning, with support for bursts and scale-outs across multiple clouds and regions.
Runs your existing code
Bring your own entry point or Dockerfile, use private Docker images, and run your code without rewrites or a custom SDK.
API and job orchestration options
Expose workloads through REST, WebSocket, and streaming endpoints, with asynchronous jobs and support for concurrency and batching.
Built-in observability
Track logs, metrics, scaling events, and system performance in real time, with native OpenTelemetry support for existing monitoring stacks.
Common use cases
Voice agents
Deploy conversational or calling agents that need low latency, such as voice assistants and outbound calling workflows.
Model serving
Serve LLMs, embeddings, reranking, and other inference endpoints with autoscaling and request-level observability.
Generative media
Run video and image generation workloads that need elastic GPU capacity and multi-region deployment.
Bursty background workloads
Process bursty jobs like transcription or asynchronous inference without keeping idle GPU instances running.
Training and experiments
Launch training or hyperparameter sweep jobs on high-end GPUs using the same deployment model as inference workloads.
Pros and Cons
Pros
- Pay-per-second pricing ties cost to actual compute time instead of hourly instance billing.
- Supports a wide range of AI workloads, including voice, LLMs, video, embeddings, and training.
- Built for fast startup and autoscaling, with published work on reducing cold starts and node boot times.
- Offers multi-region deployment and routing across multiple clouds and regions.
- Lets teams deploy from existing code or Dockerfiles without rewriting the application.
Cons
- The public site does not provide a complete integration list, so compatibility details are limited.
- The pricing page is detailed on metered compute, but the public page does not fully specify all limits or usage thresholds for every plan.
FAQ
What does Cerebrium do?
Cerebrium is a serverless GPU platform that runs AI workloads on demand. The source shows it supports bringing your own code or Dockerfile and running workloads such as voice agents, LLMs, video models, transcription, image generation, and training jobs.
How does Cerebrium pricing work?
The pricing page shows usage-based billing measured by actual compute time in seconds, rather than hourly instance billing. It also lists separate compute, memory, and storage charges, with plan tiers for Hobby, Standard, and Enterprise.
How do you deploy a workload on Cerebrium?
The source indicates you can point Cerebrium to an entry point or Dockerfile and run the application as-is, without rewrites, decorators, or a custom SDK. The platform also supports custom Dockerfiles and private Docker images.
What kinds of workloads is Cerebrium suited for?
The home page highlights sub-second cold starts, instant autoscaling, multi-region deployments, and thousands of GPUs across multiple clouds and regions. The pricing page also notes guaranteed capacity for bursty workloads without traditional reservations.
What integrations are documented on the site?
The source does not provide a complete public integration list. It does mention OpenTelemetry support, private Docker images, custom Dockerfiles, WebSocket and REST endpoints, streaming endpoints, and CI/CD with gradual rollouts.
Quick Facts
- Category
- Serverless GPU infrastructure
- Primary users
- Teams building real-time AI applications
- Deployment model
- Bring your own code or Dockerfile
- Billing model
- Per-second compute pricing
- Platform capabilities
- REST, WebSocket, streaming endpoints, autoscaling, observability
- Website
- cerebrium.ai