Last updated 06-28-2026
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Cerebrium
Cerebrium is a serverless GPU platform for running real-time AI workloads in production. You deploy voice agents, video models, LLMs, and custom inference code without managing servers, and the platform handles cold starts, autoscaling, and regional routing so traffic spikes do not take your app offline. Teams at Tavus, Deepgram, and ResembleAI use it when latency and scale matter more than babysitting infrastructure.
The platform launches containers in 2 to 4 seconds using memory and GPU snapshotting, which cuts startup overhead compared with traditional cloud GPU provisioning. Workloads scale across 2,500+ GPUs in regions including us-east-1, eu-west-2, eu-north-1, and ap-south-1, with no reservations or long-term capacity lock-ins required.
You bring your own code through an entry point or Dockerfile. Cerebrium runs it as-is without custom decorators or proprietary SDKs, and supports inference stacks like vLLM, SGLang, and TensorRT alongside REST, streaming, and WebSocket endpoints. OpenTelemetry integration feeds logs, metrics, and scaling events into your existing monitoring setup.
ML engineers, AI startups, and platform teams use Cerebrium when they need production-grade inference with pay-per-second billing, multi-region data residency, and compliance certifications including SOC 2, HIPAA, GDPR, and ISO.
Cold starts in 2 to 4 seconds with GPU snapshotting
Pay-per-second billing across H100, A100, L4, and 12+ GPU types
Deploy via entry point or Dockerfile with no custom SDK
Autoscale across 2,500+ GPUs in four global regions
OpenTelemetry hooks for logs, metrics, and scaling events
SOC 2, HIPAA, GDPR, and ISO compliance with gVisor isolation
Pay-per-second compute pricing with no GPU reservations or long-term lock-ins.
Cold starts as low as 2 to 4 seconds using memory and GPU snapshotting.
Runs your own entry point or Dockerfile without proprietary SDK requirements.
Multi-region deployment with SOC 2, HIPAA, GDPR, and ISO compliance options.
Supports vLLM, SGLang, TensorRT, and custom inference stacks out of the box.
Standard plan requires a $100 monthly base fee on top of per-second compute charges.
Hobby tier limits you to 3 deployed apps and 5 concurrent GPUs.
AWS and GCP cloud credits cannot be applied to Cerebrium billing.
How does Cerebrium pricing work?
Cerebrium bills compute by the second based on the GPU, CPU, and memory you allocate while workloads run. Workspace plans start with a free Hobby tier, a $100/month Standard plan plus compute, and custom Enterprise pricing. GPU rates range from $0.000164/s for T4 up to $0.00167/s for B200 on the pricing page.
Does Cerebrium have a free plan?
Yes. Cerebrium offers a Hobby plan that is free plus compute usage. It includes 3 user seats, up to 3 deployed apps, 500 container concurrency, 5 concurrent GPUs, and 1 day log retention.
What GPU types does Cerebrium support?
Cerebrium supports more than 12 GPU types including B200, H200, H100, RTX PRO 6000, A100 (40GB and 80GB), L40s, A10, L4, and T4. The LLM use-case page also lists AMD MI300X and Google TPU v5e for production inference workloads.
Can I deploy my own code on Cerebrium?
Yes. Cerebrium runs your application from an entry point or Dockerfile without requiring rewrites, decorators, or a custom SDK. You point the platform at your code and it handles versioning, deployment, and autoscaling.
What compliance certifications does Cerebrium offer?
Cerebrium is built to meet SOC 2, HIPAA, GDPR, and ISO standards. Workloads run in gVisor-isolated containers, and you can deploy to specific regions for data residency requirements.
Can AWS or GCP credits be used on Cerebrium?
No. Cerebrium pricing is separate from AWS and GCP, so cloud credits from those providers cannot be applied to Cerebrium usage according to the pricing FAQ.