Elastic compute and snapshot restores
Scale CPU and GPU containers with demand. Memory and GPU snapshotting accelerate workload restores and reduce startup overhead; startup time varies by workload and configuration.
Serverless CPU and GPU infrastructure for teams deploying real-time AI workloads.
Cerebrium is a serverless infrastructure platform for deploying and running AI applications on CPUs and GPUs. It supports voice pipelines, large language models, image generation, video workloads, and training jobs, with compute billed by the second.
Teams supply their application code or Dockerfile, while Cerebrium handles container orchestration and scaling across clouds and regions. The platform combines deployment, monitoring, and regional routing to help developers operate production AI services without managing Kubernetes or planning fixed compute capacity.
Scale CPU and GPU containers with demand. Memory and GPU snapshotting accelerate workload restores and reduce startup overhead; startup time varies by workload and configuration.
Deploy from an application entry point or Dockerfile without rewriting code or adding platform-specific decorators. Private Docker images, secrets management, CI/CD, and gradual rollouts support production delivery.
Expose REST, WebSocket, and streaming endpoints, or run asynchronous jobs. Concurrency controls and batching support workloads with different throughput and response requirements.
Inspect request logs, metrics, scaling events, and system performance in real time. Native OpenTelemetry support connects workloads to an existing monitoring stack.
Select deployment regions for data residency requirements. Workloads run in gVisor-based isolated environments, with multi-region failover routing constrained by deployment requirements.
Run speech-to-text, language models, and text-to-speech on co-located CPU and GPU infrastructure to reduce network latency between pipeline components.
Build inbound or outbound voice applications with LiveKit or Pipecat. Published examples include Twilio integration and agents combining transcription, language generation, and synthesized speech.
Serve language models with vLLM or TensorRT-LLM, and deploy embedding and reranking APIs for search workflows. Examples also cover vision-language models using SGLang.
Run Stable Diffusion XL image generation, digital avatars, and video pipelines with on-demand resources suited to variable request volumes.
Execute GPU training scripts and hyperparameter sweeps, including a published Llama 3.2 workflow with Weights & Biases.
Compute is billed by the second, with separate rates for GPU, CPU, and memory. Storage costs $0.05 per GB per month, with the first 100 GB free. Listed GPU rates include $0.000222 per second for L4 and $0.000944 per second for H100.
Hobby has no monthly platform fee, with compute charged separately, and allows three deployed apps and five concurrent GPUs. Standard costs $100 per month plus compute and includes unlimited apps and 30 concurrent GPUs. Enterprise uses custom pricing and offers volume discounts and additional support.
No. Cerebrium bills separately from AWS and Google Cloud, so credits from those providers cannot be applied.
Cerebrium offers guaranteed capacity for bursty workloads through a minimum monthly spend commitment, without requiring continuously paid infrastructure reservations. Terms depend on the deployment and requested compute.
Os dados de tráfego são apenas para referência.
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