Cerebrium review
Cerebrium fits teams that need serverless CPU/GPU inference across regions and endpoint styles.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Cerebrium is a cloud platform for deploying, serving, and scaling AI workloads such as large language models, voice agents, video models, embeddings, and batch jobs. It is aimed at developers and teams running production AI applications that need serverless CPU and GPU infrastructure without managing servers directly. Deployments can use PyTorch, ONNX, TensorRT, or CTranslate2 model formats, along with containers and custom Dockerfiles. The platform is available through web and API interfaces.
Its strongest fit is inference that needs flexible traffic handling and varied client connections. Autoscaling supports configurable replicas and concurrency, while framework-native and custom request batching can improve how inference workloads are served. Cerebrium supports REST, streaming, WebSocket, asynchronous, and OpenAI-compatible endpoints, giving teams several ways to expose models. Multi-region deployment and data residency controls suit applications with regional placement requirements, and private inter-cluster application routing adds an option for internal service communication.
Pricing combines workspace plans with usage-based compute billing for CPU, GPU, memory, and storage. Hobby includes 3 user seats, up to 3 deployed applications, and community and Intercom support. Standard costs $100 per month, includes unlimited applications, custom domains, community support, and a private Slack channel. Enterprise adds volume discounts, unlimited concurrent GPUs, dedicated Slack support, white-glove onboarding, and ML engineering services. Real-time logs, metrics, scaling events, and OpenTelemetry support help teams monitor deployments. Cerebrium suits teams prioritizing serverless multi-region inference and multiple endpoint types; teams wanting fixed all-inclusive infrastructure costs or a smaller deployment surface may prefer a narrower platform.
Cerebrium pros and cons
- Where it wins
- Serverless CPU and GPU deployments with configurable autoscaling
- REST, streaming, WebSocket, async, and OpenAI-compatible endpoints
- Multi-region controls, batching, Docker deployment, and OpenTelemetry support
- Where it doesn't
- Usage-based CPU, GPU, memory, and storage charges require cost monitoring
- Hobby limits teams to 3 seats and up to 3 deployed applications
- Standard adds a $100 monthly fee before metered compute charges
Cerebrium fact sheet, pricing and score →
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