Cerebrium
Claim this toolServerless AI infrastructure for deploying, scaling, and operating high-performance AI applications.
From $100/mo (free plan available)Visit WebsiteFreemium Visit Website
Tracked since 2026
The Bottom Line
- Price
- From $100/mo (free plan available). Compare plans
Key facts
- Serverless platform for deploying and scaling AI models and applications.
- Offers automatic scaling, multi-region deployments, and a wide selection of GPUs.
- Simplifies AI infrastructure management, eliminating cold starts and complex orchestration.
Pros
- Significantly reduces infrastructure management overhead for AI applications
- Provides high performance with fast cold starts and efficient GPU utilization
- Offers extensive scalability and multi-region deployment capabilities
- Supports a wide variety of GPU hardware for diverse AI workloads
- Includes robust security and compliance features like SOC 2 and HIPAA
Cons
- Pricing model is consumption-based, which can be complex to estimate for unpredictable workloads
- Requires familiarity with AI model deployment concepts, even with simplified infrastructure
What is Cerebrium?
Cerebrium is a serverless AI infrastructure platform designed to simplify the deployment and scaling of AI workloads. It abstracts away the complexities of cold starts, autoscaling, orchestration, observability, and regional deployment, allowing developers to focus on building AI products without managing servers. The platform supports a wide range of AI applications, from real-time voice bots and multimodal inference pipelines to large-scale batch jobs and fine-tuning models.
Cerebrium is built for both startups and enterprises, offering features like fast cold starts, multi-region deployments for compliance and performance, and automatic scaling from zero to thousands of containers. It provides a robust software layer with capabilities such as request batching, dynamic concurrency, asynchronous job processing, and distributed storage for model weights and artifacts. Users can select from over 12 GPU types, including T4, A10, A100, and H100, and deploy code as REST API, WebSocket, or streaming endpoints with built-in auto-scaling and reliability. The platform also supports custom Dockerfiles, CI/CD pipelines, gradual rollouts, and secure secrets management.
Available on: Web
Louis Corneloup Source cerebrium.ai ↗
Preview
Key Features
- Easy configuration and deployment of AI applications
- Fast cold starts (average 2 seconds or less)
- Multi-region deployments for compliance and performance
- Automatic scaling from zero to thousands of containers
- Request batching for improved GPU throughput
- Dynamic concurrency for simultaneous requests
- Asynchronous jobs for background workloads and training tasks
- Distributed storage for model weights, logs, and artifacts
Pricing Plans
Pricing checked Sep 28, 2026
| Plan | Price | Details |
|---|---|---|
| Hobby | $0 + compute / month |
+3 more Show less
|
| Standard | $100 + compute /month |
+3 more Show less
|
| Enterprise | Custom |
+5 more Show less
|
Included in all plans
- Unlimited projects
- Unlimited secrets
- Unlimited custom images
- Observability (In-app logging & monitoring)
Is Cerebrium worth the price?
Good value
Cerebrium's pricing structure is quite generous, especially with the Hobby tier being $0 + compute.
The jump to the Standard tier at $100 + compute/month is reasonable for increased scale. It's best for individuals and small teams looking for a cost-effective entry into serverless AI deployment.
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Cerebrium FAQ
How does Cerebrium ensure fast cold starts for AI models, especially for large language models?
Cerebrium is engineered to achieve average cold start times of 2 seconds or less. This is accomplished by optimizing the underlying infrastructure and deployment mechanisms specifically for AI workloads, minimizing the delay between a request and the model becoming active.
Can I deploy a custom Docker image with specific dependencies for my AI model on Cerebrium?
Yes, Cerebrium supports bringing your own runtime. You can use custom Dockerfiles to define your application environment, providing absolute control over dependencies and configurations for your AI models.
What types of observability tools are integrated into Cerebrium for monitoring AI application performance?
Cerebrium integrates OpenTelemetry, providing end-to-end observability with unified metrics, traces, and log data. This allows users to track the performance of their AI applications comprehensively within the platform.
How does Cerebrium handle data residency requirements for multi-region AI deployments?
Cerebrium facilitates multi-region deployments, allowing users to deploy their AI applications in various geographical regions. This capability helps address data residency requirements by enabling models and data to be processed and stored closer to end-users, improving compliance and performance.
Beyond standard REST APIs, what other types of endpoints does Cerebrium support for real-time AI interactions?
In addition to REST API endpoints, Cerebrium supports WebSocket endpoints for real-time interactions and low-latency responses, as well as native streaming endpoints that push tokens or data chunks to clients as they are generated, which is ideal for generative AI models.
What specific GPU hardware options are available for deploying models, and how do I choose the right one?
Cerebrium offers a selection of over 12 GPU types, including T4, A10, A100 (40GB/80GB), H100, H200, Trainium, and Inferentia. The choice of GPU depends on the specific use case, model size, and performance requirements, with options catering to both inference and training tasks.
Source: cerebrium.ai