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Cerebrium

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Serverless AI infrastructure for deploying, scaling, and operating high-performance AI applications.

Cloud & InfrastructureHosting & DeploymentAI Model Deployment
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Tracked since2026
0 reviews tracked

The 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?

Editorial review
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

how we evaluateSourcecerebrium.ai ↗

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 Aug 18, 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)
Calculate your costView full pricing

Is Cerebrium worth the price?

85/100

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.

Hidden Costs & Gotchas

Compute costs are additional to base price

Higher GPU concurrency means higher compute

Log retention limited on lower tiers

How Cerebrium Compares to Competitors

Compared to platforms like Replicate, which often charges per-model inference, Cerebrium's fixed monthly fee plus compute for dedicated resources can be more predictable for consistent usage. While AWS SageMaker offers immense flexibility, its cost structure can be more complex and potentially higher for similar dedicated GPU access, making Cerebrium a more streamlined and potentially cheaper alternative for focused AI deployment.

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Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

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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

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