Cerebrium
FreemiumServerless GPU infrastructure for real-time AI workloads with sub-second cold starts, autoscaling, and per-second billing.
Cerebrium is a serverless GPU platform for deploying, scaling, and operating real-time and high-performance AI applications. It lets teams launch containers on CPUs or GPUs across multiple clouds and regions through a simple CLI or Dockerfile, with automatic scaling and per-second billing. Cerebrium is designed for workloads such as LLM inference, voice agents, video generation, and digital avatars that need low cold starts, global low latency, and no infrastructure management.
What is it
Cerebrium is a serverless GPU infrastructure platform for real-time AI workloads.
What it can do
It deploys custom AI apps and models as auto-scaling REST, WebSocket, streaming, or ASGI endpoints across global regions, billing only for actual compute time.
Who is it for
Machine-learning engineers, AI developers, and platform teams who need fast, scalable inference for voice, video, LLMs, and multimodal applications without managing Kubernetes or GPU clusters.
Key Features
Serverless GPU Deployment — Run AI apps without infrastructure management
Deploys Python functions or container images via CLI and turns them into production endpoints without requiring Kubernetes, decorators, or custom SDKs.
Sub-Second Cold Starts — Memory and GPU snapshotting
Restores workloads in seconds through content-aware storage and memory snapshotting, reducing the delay before a model can serve its first request.
Elastic Autoscaling — From zero to thousands of instances
Handles sudden traffic bursts by scaling containers up and down automatically, avoiding over-provisioning and idle GPU costs.
Multi-Region Deployment — Global low latency and data residency
Deploys workloads across US, EU, and Asia regions to meet latency requirements and data sovereignty regulations.
Use Cases
Deploy LLM inference endpoints
Serve open-source or proprietary large language models with vLLM, SGLang, TensorRT, or custom stacks behind scalable, OpenAI-compatible APIs.
Run real-time voice agents
Build low-latency conversational AI pipelines with automatic scaling to handle call-volume spikes without pre-warming capacity.
Scale image and video generation workloads
Run diffusion and generative media models on demand, paying only for the seconds each generation job actively uses GPU compute.
Build multimodal AI applications
Combine ASR, LLM, and TTS components in a single deployment that scales each piece independently based on traffic.
Pricing plans
Frequently Asked Questions
Discussion
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