Cerebrium
Claim this toolServerless AI infrastructure for deploying, scaling, and operating high-performance AI applications.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Significantly reduces infrastructure management overhead for AI applications
Biggest con
Pricing model is consumption-based, which can be complex to estimate for unpredictable workloads
TL;DR - Cerebrium
- 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.
Pricing: Free plan available
Best for: Growing teams
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
Pros & Cons
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
Preview
Key Features
Easy configuration and deployment of AI applicationsFast cold starts (average 2 seconds or less)Multi-region deployments for compliance and performanceAutomatic scaling from zero to thousands of containersRequest batching for improved GPU throughputDynamic concurrency for simultaneous requestsAsynchronous jobs for background workloads and training tasksDistributed storage for model weights, logs, and artifacts
Pricing Plans
Pricing checked Jun 29, 2026
Hobby
$0 + compute / month
Everything in all plans, plus:
- 3 user seats
- Up to 3 deployed apps
- 5 Concurrent GPUs
- Slack & intercom support
- 1 day log retention
- 1000 CPU concurrency
Standard
$100 + compute /month
Everything in all plans, plus:
- Everything in Hobby plan
- 10 user seats
- 10 deployed apps
- 30 Concurrent GPUs
- 30 day log retention
- 1000 CPU concurrency
Enterprise
Custom
Everything in all plans, plus:
- Everything in Standard plan
- Unlimited deployed apps
- Unlimited Concurrent GPUs
- Dedicated Slack support
- Unlimited log retention
- Unlimited seats
- SOC2 compliance
- Unlimited CPU concurrency
Included in all plans
- Unlimited projects
- Unlimited secrets
- Unlimited custom images
- Observability (In-app logging & monitoring)
Reviews
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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