What is Cerebrium?
Cerebrium is a serverless GPU infrastructure platform designed for real-time AI workloads, including voice agents, video models, and large language models (LLMs). It eliminates the complexity of managing Kubernetes and infrastructure, enabling teams to deploy and scale AI applications with sub-second cold starts and instant autoscaling. With pay-per-second pricing, you only pay for the compute you use, making it cost-effective for both development and production.
Key features include memory and GPU snapshotting for fast container restores, allowing you to launch containers in seconds. Cerebrium handles sudden traffic bursts and scale-outs automatically, ensuring consistent performance without manual intervention. It provides instant access to thousands of GPUs across multiple clouds and regions, removing the need for capacity planning or reservations.
Deploying your code is straightforward: no rewrites, decorators, or custom SDKs required. Simply point to your entry point or Dockerfile, and Cerebrium runs your application as-is, with versioning and reproducibility built-in. The platform offers full observability with real-time logs, metrics, scaling events, and system performance, plus native OpenTelemetry support for integration with existing monitoring stacks.
Cerebrium meets strict security and compliance standards, including SOC 2, HIPAA, GDPR, and ISO certifications. It supports data residency by allowing deployment in specific regions to meet regulatory requirements. Each workload runs on gVisor in a hardened, isolated environment for strong container isolation without performance trade-offs. The platform boasts 99.999% uptime with multi-region failovers, automatically routing traffic to the best alternative during outages.
Use cases include building voice agents with sub-500ms response times using Pipecat, creating outbound calling agents with Livekit, transcribing hour-long podcasts in under two minutes, and deploying OpenAI's latest models. Cerebrium is ideal for teams that need reliability at scale without the operational overhead, pushing boundaries in real-time AI applications.
Use Cases
Key Features
sub second cold starts instant autoscaling pay per second pricing memory and GPU snapshotting multi cloud GPU access no code rewrites full observability versioning and reproducibilityWho is it for?
AI engineers, ML teams, voice agent developers, video model developers, LLM developers, platform teams, DevOps engineers
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