Cerebrium

Overview

Cerebrium is a serverless GPU infrastructure platform built for real-time AI workloads. Designed to eliminate the operational overhead of managing GPU clusters, it enables teams to deploy, scale, and serve AI models in production with sub-second cold starts and instant autoscaling. The platform supports a broad range of workloads, including voice agents, video models, large language models (LLMs), and custom machine learning pipelines, positioning itself as a flexible compute layer for modern AI applications.

Core to Cerebrium is its bring-your-own-code approach: developers can deploy any Python workload without containerizing or managing underlying infrastructure. The platform offers a choice of leading GPU instances, including L4, L40s, A100, H100, and H200, alongside multi-region deployment options across the US and EU. Billing is metered per second with no idle charges, making it cost-effective for workloads with variable traffic. Cerebrium is also built for enterprise compliance, supporting SOC 2, HIPAA, GDPR, and ISO standards.

Use cases span real-time inference at scale, AI agent deployment, video and image generation, and high-throughput LLM serving. With instant autoscaling, teams can handle traffic spikes without pre-provisioning capacity, while the serverless model ensures resources are only consumed when inference requests are processed. Cerebrium is suitable for startups and enterprises alike, offering both a standard pay-as-you-go tier and an enterprise plan with volume discounts, dedicated support, and white-glove onboarding.

Core capabilities

Sub-second cold starts

Launch GPU-backed inference endpoints in under a second, enabling real-time responsiveness even for spiky or low-latency workloads.

Instant autoscaling

Automatically scale from zero to thousands of concurrent GPU requests without manual capacity planning or queueing.

Pay-per-second billing

Only pay for actual compute time used, with no idle charges or long-term reservation requirements.

Bring-your-own-code

Deploy any Python workload directly, with no need to build or manage custom containers or infrastructure.

Flexible GPU options

Choose from L4, L40s, A100, H100, and H200 instances to match performance and cost requirements for different AI models.

Multi-region deployment

Run inference across US and EU regions to reduce latency and meet data residency needs.

Enterprise-grade compliance

Supports SOC 2, HIPAA, GDPR, and ISO standards, making it suitable for regulated industries.

Real-time AI workload support

Optimized for voice agents, video models, LLMs, and other latency-sensitive AI inference pipelines.

Use cases

Real-time voice agent inference with low latency

LLM serving and prompt processing at scale

Video generation and real-time video model inference

Custom machine learning model deployment without container orchestration

Autoscaling AI workloads with unpredictable traffic patterns

Multi-region AI inference for latency-sensitive global applications