About
Cerebrium is a serverless AI infrastructure platform designed to simplify the deployment and scaling of AI applications, including LLMs, AI agents, and vision models. Built for developers and enterprises alike, it eliminates DevOps complexity by offering a streamlined workflow: initialize a project, select your hardware, and deploy in seconds. The platform offers over 12 GPU types — including T4, A10, A100, H100, H200, Trainium, and Inferentia — enabling teams to match compute to their exact workload needs. Cold starts average just 2 seconds, making it suitable for latency-sensitive real-time applications. Cerebrium supports WebSocket endpoints, native streaming, REST APIs, and asynchronous background jobs, covering the full spectrum of AI serving patterns. Key infrastructure features include automatic scaling from zero to thousands of containers, multi-region deployments for global low-latency access, request batching to maximize GPU utilization, and distributed storage for model weights and artifacts. CI/CD pipeline integration and gradual rollout capabilities ensure zero-downtime updates in production environments. Observability is built in via OpenTelemetry, providing unified metrics, traces, and logs. Security is enterprise-grade with SOC 2 and HIPAA compliance and 99.999% uptime SLA. Secrets management is handled natively through the dashboard. Cerebrium is trusted by companies like Tavus and bitHuman for scaling AI-powered digital human and language model applications. It offers a $30 free credit with no credit card required to get started.
Key Features
- Fast Cold Starts: Applications on Cerebrium start in an average of 2 seconds or less, enabling low-latency real-time AI deployments.
- 12+ GPU Types: Choose from T4, A10, A100 (40GB/80GB), H100, H200, Trainium, Inferentia, and more to match your workload requirements and budget.
- Auto-Scaling & Per-Second Billing: Automatically scale from zero to thousands of containers on demand, paying only for actual compute time used.
- Multiple Endpoint Types: Expose your AI models via REST API, WebSocket, or native streaming endpoints to support synchronous, real-time, and token-streaming use cases.
- Enterprise-Grade Observability & Security: Built-in OpenTelemetry tracing, SOC 2 & HIPAA compliance, secrets management, and 99.999% uptime SLA for production-ready deployments.
Use Cases
- Deploying and serving large language models (LLMs) in production with low-latency REST or streaming API endpoints
- Running real-time AI-powered digital avatar and virtual assistant applications that require low cold starts and WebSocket support
- Executing large-scale batch inference jobs for data processing pipelines without managing dedicated GPU servers
- Building and deploying AI agents that require scalable, serverless compute with automatic scaling under variable load
- Serving multilingual NLP and computer vision models globally across multiple regions to minimize end-user latency
Pros
- Zero DevOps Overhead: Cerebrium abstracts away infrastructure management, letting developers deploy complex AI workloads without configuring servers or orchestration tools.
- Flexible Hardware Selection: Access to 12+ GPU types ensures developers can optimize for cost or performance depending on their specific model and workload needs.
- Broad Workload Support: Handles real-time inference, background batch jobs, and async workloads in a single platform, reducing the need for multiple specialized services.
- Enterprise Compliance Ready: SOC 2 and HIPAA certifications make it viable for regulated industries and enterprise customers with strict data security requirements.
Cons
- Costs Can Escalate at Scale: Per-second billing is efficient for variable workloads, but high-volume or always-on use cases may become expensive compared to reserved instance pricing.
- Limited Free Tier: The $30 free credit is a one-time offer rather than an ongoing free tier, which may limit extended experimentation without incurring costs.
- Vendor Lock-In Risk: Deep integration with Cerebrium's deployment workflow and proprietary tooling may make migrating to another provider more complex over time.
Frequently Asked Questions
What types of AI workloads does Cerebrium support?
Cerebrium supports a wide range of AI workloads including real-time LLM inference, AI agent deployments, vision model serving, large-scale batch processing, and asynchronous background jobs.
How does Cerebrium handle scaling?
Cerebrium automatically scales your application from zero to thousands of containers based on incoming request volume. You only pay for the compute time you actually use, measured per second.
Which GPUs are available on Cerebrium?
Cerebrium offers 12+ GPU types including NVIDIA T4, A10, L4, L40s, A100 (40GB and 80GB), H100, H200, as well as AWS Trainium and Inferentia chips for specialized inference workloads.
Is Cerebrium compliant with data security regulations?
Yes. Cerebrium holds SOC 2 and HIPAA compliance certifications and offers a 99.999% uptime SLA, making it suitable for enterprise and regulated-industry deployments.
How do I get started with Cerebrium?
You can sign up and receive $30 in free credits — no credit card required. You initialize a project, select your hardware, and deploy your app in seconds using Cerebrium's CLI and configuration tooling.
