Cerebrium - Serverless infrastructure for real-time AI applications

What is Cerebrium

The complexity of managing GPU infrastructure has become a significant bottleneck for AI development teams. Traditional cloud services require extensive DevOps expertise, unpredictable costs from always-on instances, and suffer from cold start delays that degrade user experience. Cerebrium addresses these challenges by providing a serverless infrastructure platform specifically designed for real-time AI applications, eliminating the operational overhead while delivering sub-second performance.

Cerebrium is a serverless AI infrastructure platform that enables developers to deploy, scale, and manage AI workloads without worrying about underlying infrastructure. The platform handles container orchestration, auto-scaling, cold start optimization, and observability, allowing teams to focus entirely on model development and business logic. With support for over 12 GPU types including NVIDIA T4, L4, A10, A100 (40GB/80GB), L40s, H100, and H200, Cerebrium provides the computational flexibility required for diverse AI workloads from language models to computer vision applications.

The platform's architecture achieves an average cold start time of 2 seconds or less through optimized container initialization processes, while maintaining the ability to scale automatically from zero to thousands of containers based on concurrency, queries per second, or resource utilization. This combination of rapid scaling and minimal latency makes Cerebrium particularly suitable for production AI systems that experience variable traffic patterns.

Trusted by leading AI companies including Tavus (video digital humans), Deepgram (speech AI), Vapi (voice assistants), Lelapa AI, and bitHuman, Cerebrium serves both startups and enterprises seeking to deploy AI applications globally. The platform offers a new user credit of $30 free, requiring no credit card for signup, enabling developers to evaluate the infrastructure without financial commitment.

---

Core Features of Cerebrium

Cerebrium provides a comprehensive suite of features designed to address the unique challenges of deploying AI workloads at scale. The platform's capabilities span from infrastructure optimization to developer experience, creating an end-to-end solution for production AI systems.

Rapid Cold Start technology achieves application startup times averaging 2 seconds or less through optimized container initialization workflows. This performance is critical for real-time AI applications where latency directly impacts user experience. The system pre-warms containers based on predicted demand, ensuring that new instances are ready before traffic arrives.

Multi-Region Deployment enables global distribution of AI applications with data residency compliance. The platform's distributed infrastructure allows deployment across multiple geographic regions, reducing latency for end users while meeting regulatory requirements for data localization. This capability is essential for applications serving international用户 with strict data governance requirements.

Automatic Scaling dynamically adjusts container count based on multiple metrics including concurrent requests, queries per second, and CPU/memory utilization. The system can scale from zero to thousands of containers within seconds, handling traffic spikes without manual intervention. This elastic scaling ensures cost efficiency by only consuming resources when needed.

Request Batching employs dynamic batching algorithms to combine multiple inference requests into batches, minimizing GPU idle time and maximizing throughput. This optimization is particularly valuable for high-volume inference workloads where batch processing can significantly reduce per-request costs.

WebSocket and Streaming Endpoints support real-time interactions with native streaming capabilities. WebSocket endpoints enable bidirectional communication for chat and voice applications, while streaming endpoints support real-time token output for large language models. Both options provide sub-100ms latency for responsive user experiences.

Concurrent Processing allows dynamic scaling of applications to handle thousands of concurrent requests through intelligent container pool management. The platform automatically distributes load across available instances while maintaining consistent performance.

---

Use Cases and Target Users

Cerebrium serves a diverse range of AI deployment scenarios, from startups building their first AI product to enterprises running mission-critical inference at scale. Understanding these use cases helps developers determine whether the platform matches their requirements.

Large Language Model Deployment represents one of Cerebrium's strongest use cases. The platform provides pre-configured vLLM templates that enable deployment from development to production in approximately 5 minutes. Dynamic batching, streaming output, and access to multiple GPU options allow teams to optimize for both cost and performance. Companies like Tavus use Cerebrium to power conversational AI experiences that require real-time response times.

Real-time Voice Applications leverage Cerebrium's WebSocket endpoints and low-latency deployment options. The platform integrates seamlessly with voice AI providers like Vapi, enabling developers to build voice assistants, call center solutions, and real-time translation services. The combination of WebSocket support and streaming processing ensures voice interactions feel natural with minimal perceived latency.

Image and Video Processing benefits from Cerebrium's asynchronous task capabilities and distributed storage. Large media files can be processed in the background without blocking user requests, while automatic scaling handles variable processing volumes. This architecture supports applications ranging from content moderation to video transcoding.

Multi-modal Inference Pipelines utilize Cerebrium's unified serverless abstraction to coordinate multiple model types within a single application. The platform's flexible resource configuration allows teams to allocate appropriate GPU types for different model components, from vision encoders to language heads.

Model Training and Fine-tuning leverage Cerebrium's per-second billing to reduce training costs significantly. Async task support enables long-running training jobs without maintaining persistent connections, while distributed storage preserves model checkpoints and artifacts.

---

Quick Start Guide

Getting started with Cerebrium requires only a few minutes from account creation to your first deployed AI application. The platform supports multiple installation methods ensuring compatibility with different development environments.

Installation proceeds through the Cerebrium CLI tool, available via pip, Homebrew, or direct binary download for Linux and Windows systems. After installation, authenticate with your Cerebrium account credentials to link the CLI to your workspace.

Deployment Workflow follows a streamlined three-step process: create a project, write your application code, and deploy with a single command. The CLI handles containerization, dependency resolution, and infrastructure provisioning automatically. Developers can deploy a basic Python function as an API endpoint in under 5 minutes.

GPU Selection offers flexibility through support for over 12 GPU types optimized for different workload characteristics. Entry-level options like NVIDIA T4 provide cost-effective inference for lower throughput requirements, while high-performance options like H100 and H200 serve demanding real-time applications. The platform's per-second billing means you pay only for actual GPU utilization.

Endpoint Configuration supports multiple API types to match application requirements. REST endpoints handle standard request-response patterns, WebSocket endpoints enable real-time bidirectional communication, and streaming endpoints support progressive output for language models. All endpoint types auto-scale based on demand without additional configuration.

# Minimal example: Deploy a Python function as API endpoint
from cerebrium import deploy

def handler(request):
    return {"message": "Hello from Cerebrium!"}

deploy(handler, name="my-first-endpoint")

Production deployments benefit from custom runtime configurations using Dockerfiles for specialized dependencies, and the platform's key management system for securing API credentials and other sensitive values.

---

Pricing Structure

Cerebrium employs a transparent per-second billing model that eliminates guesswork from cost estimation. All charges are calculated based on actual resource consumption, with no hidden fees or unexpected overages.

Compute Pricing varies by resource type, with CPU-only instances starting at $0.00000655 per vCPU-second. GPU-accelerated instances range from $0.000164 per second for NVIDIA T4 to $0.000917 per second for NVIDIA H200, enabling precise cost matching to workload requirements.

Memory and Storage incur additional charges at $0.00000222 per GB-second for memory and $0.05 per GB-month for persistent storage. The first 100GB of storage is provided free, reducing costs for applications with moderate storage requirements.

Enterprise Benefits include new customer credits up to $1,000 and dedicated engineering support for migration and optimization. The Enterprise plan provides custom configurations, compliance certifications beyond standard SOC 2 and HIPAA, and committed uptime guarantees.

---

Frequently Asked Questions