What does Cerebrium do?

Cerebrium is a serverless AI infrastructure platform that makes it easy to build, deploy, and scale machine learning and AI applications, including serverless GPUs with low cold starts, support for major frameworks, and options for real-time voice, video, and batch jobs.

Who is Cerebrium best for?

Cerebrium is best for machine learning engineers, data scientists, and developers building and scaling AI workloads like voice agents, video processing, LLMs, and real-time applications without managing infrastructure.

Do I need to install anything to use Cerebrium?

No, you do not need to install anything to use Cerebrium. It is a serverless platform accessed via code deployment.

Is Cerebrium free to use?

No, Cerebrium is not free to use. It offers pay-per-use pricing down to the millisecond.

Does Cerebrium offer an API or integrations?

Yes, Cerebrium offers an API for deploying and managing models, plus integrations with major ML frameworks, observability platforms, and libraries like daily-python.

Cerebrium: Reviews, Pricing, Alternatives & Features (2026)

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Cerebrium is a serverless AI infrastructure platform for machine learning engineers and teams building and deploying GPU-backed AI applications, including real-time voice, video, and LLM inference.

Key capabilities include:

Serverless GPUs with 2–4 second cold starts and 12+ GPU types (including A100 and H100) for matching hardware to workload needs
Autoscaling from 1 to 10,000+ concurrent requests, plus support for batching and asynchronous jobs for background workloads
Multiple serving options, including REST, WebSocket, and streaming endpoints; designed for low-latency execution (under 35 ms overhead)
Developer workflow via a Python library and CLI (one-line deploy), plus a web dashboard; includes orchestration, versioning, and observability support such as OpenTelemetry

Pricing: Pay-per-use (billed per second or millisecond of inference time); includes a free account with GPU credits (amount varies by offer).

Notable for combining low cold starts, broad GPU selection, and multi-region deployments (5 regions) for teams that want to ship model endpoints without managing Kubernetes or long-running GPU instances. It also lists compliance support (including SOC 2, HIPAA, and GDPR) and is available on .