Cerebrium
Serverless CPU and GPU infrastructure for deploying and autoscaling real-time AI workloads.
About Cerebrium
Cerebrium is serverless AI infrastructure for deploying voice agents, large language models, image and video pipelines, and other containerized workloads on CPU and GPU compute. Teams can bring Python applications or custom Dockerfiles, choose frameworks such as vLLM, SGLang, or TensorRT, and scale workloads without operating Kubernetes. The platform is designed for real-time production services: it advertises sub-second cold starts, on-demand autoscaling, global regions, and pay-per-second compute so bursty traffic does not require permanently reserved capacity. Voice teams can co-locate speech-to-text, LLM, and text-to-speech stages to reduce network hops, while media and LLM teams can select modern NVIDIA and AMD accelerators. Adoption still requires engineering ownership of containers, health endpoints, runtime configuration, model behavior, and usage costs. The free Hobby workspace is suitable for limited evaluation, while Standard and custom Enterprise plans expand application, seat, retention, concurrency, support, and compliance options. Cerebrium also documents SOC 2 Type I, HIPAA, GDPR, and ISO compliance practices, with reports available through its trust center.
Best for
Best use cases
Use serverless GPUs when request volume changes quickly and idle reserved capacity would be costly.
Deploy a custom Dockerized service when the application needs a reproducible runtime beyond a standard Python setup.
Place real-time workloads in supported global regions to reduce latency and address data-residency requirements.
Compare before choosing
Pay-per-second compute can reduce idle cost compared with continuously reserved GPU instances.
Custom Dockerfile support offers more runtime control than a narrowly managed model endpoint.
Teams trade infrastructure abstraction for responsibility over containers, endpoints, model behavior, and usage monitoring.
Features
Real-time voice agents
Deploy co-located speech-to-text, LLM, and text-to-speech services for low-latency voice interactions.
Large language model serving
Run vLLM, SGLang, TensorRT, or a custom inference stack with GPU autoscaling across global regions.
Image and video pipelines
Scale bursty generation and processing workloads on on-demand NVIDIA or AMD GPU infrastructure.
Custom container deployment
Ship existing Python services or compiled applications through a custom Dockerfile with explicit health and readiness endpoints.
Pros
- Pay-per-second compute avoids paying for idle capacity and fits workloads with uneven traffic.
- Custom Dockerfiles support portable Python or compiled services instead of limiting teams to a fixed model runtime.
- Global autoscaling and co-located CPU and GPU workloads support latency-sensitive voice, LLM, and media applications.
- Published SOC 2 Type I, HIPAA, GDPR, and ISO compliance practices provide a concrete trust baseline for review.
Cons
- Usage-based compute makes the final bill dependent on workload volume and selected CPU or GPU resources.
- Custom containers require teams to configure ports, startup commands, health checks, dependencies, and application behavior.
- Hobby is limited to three deployed applications, three seats, seven-day logs, and lower CPU and GPU concurrency.
- Standard limits workspaces to ten seats, 30-day log retention, CPU concurrency of 1000, and GPU concurrency of 30 before Enterprise is needed.
Pricing Plans
Hobby
- Unlimited projects
- 3 deployed applications
- 3 seats
- 7-day log retention
- CPU concurrency up to 500
- GPU concurrency up to 5
- Community support
Standard
- Unlimited projects
- Unlimited deployed applications
- 10 seats
- 30-day log retention
- CPU concurrency up to 1000
- GPU concurrency up to 30
- Private Slack channel
Enterprise
- Volume discounts
- Unlimited concurrent GPUs
- Dedicated Slack support
- White-glove onboarding
- ML engineering services
- Unlimited log retention
* Pricing information is for reference only. Please refer to the latest announcement on Cerebrium's official website.