Cerebrium is a production-oriented real-time AI serverless infrastructure platform, whose main purpose is to help teams deploy, run, and scale AI workloads, covering voice agents, video models, LLMs, and other AI tasks.
In terms of main capabilities, the platform emphasizes low latency and elastic scaling: it is officially stated to provide sub-second cold starts, while the page highlights also note cold starts of 2–4 seconds, and fast recovery is achieved through memory and GPU snapshots; Cerebrium can automatically handle burst traffic and horizontal scaling without capacity planning, reservations, or infrastructure management. Users can use their own code, and the platform does not require rewriting, decorators, or a custom SDK; it is enough to point to an entry point or Dockerfile to run as-is, while maintaining versioning and reproducibility.
For observability, the platform provides real-time visibility into per-request logs, metrics, scaling events, and system performance, and natively supports OpenTelemetry, making it easy to integrate with existing monitoring stacks. In terms of security and compliance, Cerebrium has a foundation of SOC 2, HIPAA, GDPR, and ISO compliance, supports data residency (workloads can be deployed to specific regions to meet regulatory or contractual requirements), runs each workload on gVisor to provide container isolation, and claims 99.999% uptime, routing traffic under constraints to a suboptimal alternative region through multi-region failover.
In terms of usage, the platform supports multiple endpoints and deployment forms, including WebSocket endpoints, REST API endpoints, streaming endpoints, asynchronous jobs, concurrency and batch processing, distributed storage, multi-region deployment, custom Dockerfiles, CI/CD and progressive releases, secret management, and private Docker images. Official examples cover: a 500ms low-latency voice agent, a voice agent based on Twilio and Pipecat, an outbound calling agent based on LiveKit, fast transcription of a 1-hour podcast, deploying GPT-OSS with vLLM, deploying a VLM with SGLang, achieving high throughput with Triton Inference Server and TensorRT-LLM, hyperparameter sweep training of Llama 3.2 with WandB, deploying an LLM chat interface with FastAPI and Gradio, generating images with SDXL and refiner, a high-throughput low-latency REST API for embedding and reranking models, etc.
Applicable scenarios include real-time voice agents, video models and digital humans/virtual assistants, LLM and vision-language model inference, embedding and reranking services, image generation, model training and hyperparameter sweeps, etc. The officially listed application directions involve digital avatars, virtual assistants, AI tutoring, AI-native web search, real-time deepfake detection, etc.
In terms of limitations, the materials do not provide specific pricing figures (only stating per-second billing), a list of regions, upper limits for models and hardware specifications, free quotas, or detailed SLA terms; statements about 99.999% uptime and a 50% cost reduction come from official or customer case descriptions, and actual performance may vary depending on workloads and configurations.
