Deploy real-time AI applications with Cerebrium’s autoscaling compute, streaming endpoints and workload observability.
Cerebrium provides serverless infrastructure for AI applications, including voice agents, video models and language-model inference. Developers deploy their own code or container configuration while the platform manages execution and scaling. It is a compute service rather than a consumer chatbot or a subscription that grants unlimited use of a fixed set of models.
The platform accepts an application entry point or Dockerfile and supports CPU and GPU workloads. Memory and GPU snapshotting can reduce startup work for supported deployments. Autoscaling adjusts capacity to demand, while asynchronous jobs and concurrency controls support workloads that do not fit a single synchronous request.
REST, streaming and WebSocket endpoints provide different ways to connect an application. The official examples include voice-agent frameworks, transcription, image generation and model serving. These demonstrate possible integrations, but the developer still supplies the application logic and verifies the behaviour of the selected model.
Cerebrium exposes logs, resource metrics and scaling events, with OpenTelemetry support for an existing monitoring stack. Multi-region deployment can help meet latency or data-location requirements. Secrets management, private images and gradual rollout capabilities address parts of the production workflow, while workload isolation supplies an execution boundary.
Compute is charged according to allocated resources and active time. CPU, GPU and memory contribute separately, and storage has its own allowance and rate. The Hobby tier has no monthly platform fee but is not free compute. Standard adds a monthly subscription, and Enterprise terms depend on the deployment. The official pricing page states that AWS or GCP credits cannot be applied to Cerebrium usage.
A production evaluation should measure a representative request, including startup, memory and concurrency, and check the resulting cost. Some plan details on the public comparison page differ between sections, so teams should confirm exact seat and retention limits before purchase. Low-latency examples are useful benchmarks for their configurations, not guarantees for every model or traffic pattern.
