cerebrium

cerebrium

Cerebrium is a serverless, real-time AI infrastructure platform for deploying and scaling AI workloads — including LLMs, voice agents, video models, and multimodal pipelines — with fast cold starts, instant autoscaling, and multi-region GPU access for teams and production applications.

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#524 in AI Agents (524 tools)
Just launched
Data reviewed Aug 19, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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Quick Overview

Best for: AI Agents

What it does

AI Agents software for decision-makers comparing workflow fit and alternatives.

Best fit

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cerebrium

Cerebrium provides serverless GPU infrastructure optimized for real-time AI workloads, enabling teams to deploy voice agents, video models, large language models, and other AI applications with low-latency cold starts and automatic autoscaling. The platform emphasizes production readiness: fast container startup using memory and GPU snapshotting, autoscaling across thousands of GPUs without reservations or lock-ins, and multi-region deployments to meet data residency requirements. Cerebrium targets teams and organizations that need reliable, observable, and secure infrastructure for production-grade AI.

Serverless AI infrastructure platform for building, deploying, and scaling AI applications with cost savings.

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Key Features

Fast cold starts (snapshots)

Memory and GPU snapshotting to launch containers in seconds and reduce cold-start latencies.

Instant autoscaling

Automatic, real-time scaling of workloads to handle bursts and scale-outs without manual capacity planning.

Multi-cloud, multi-region GPU access

Instant access to thousands of GPUs across multiple clouds and regions with no reservations or lock-ins.

Bring-your-own-code

Run user code or Dockerfiles unchanged — point to an entry point or Dockerfile and Cerebrium runs it versioned and reproducibly.

Observability

End-to-end observability with logs, metrics, scaling events, system performance, and native OpenTelemetry support.

Security & Compliance

Security foundations including SOC 2, HIPAA, GDPR, ISO compliance, gVisor-based workload isolation, and data residency controls.

Multiple endpoint types

Support for WebSocket endpoints, streaming endpoints, and REST API endpoints for serving models and real-time agents.

Deployment tooling

Support for custom Dockerfiles, CI/CD workflows, secrets management, concurrency & batching, asynchronous jobs, and distributed storage.

12+ GPU types

Support for a variety of GPU hardware types to match workload requirements.

Pricing

Current pricing details are not available from the vendor source.

Use Cases

Low-latency voice agents

Deploy voice agents that respond with low latency (examples show 500ms voice agents and Twilio/LiveKit integrations).

Transcription at scale

Transcribe long-form audio quickly (example: transcribe a 1 hour podcast in under 2 minutes).

Serving open-source LLMs

Deploy and serve open-source models with vLLM or other inference backends.

Video and image models

Run generative and VLM workloads such as Stable Diffusion XL and other image/video models.

High-throughput embeddings & reranking

Provide a low-latency, high-throughput REST API for embeddings and reranking models.

Inference performance tuning

Deploy Triton Inference Server with TensorRT-LLM for high-throughput inference and specialized optimizations.

Model training and hyperparameter sweeps

Run training jobs and hyperparameter sweeps (example: Llama 3.2 with WandB).

Model-backed web apps

Deploy chat interfaces and web UIs using FastAPI + Gradio or similar frameworks.

Ad analysis and multimodal pipelines

Deploy VLMs and pipelines (example: building intelligent ad analysis systems with SGLang).

Integrations

OpenTelemetry

Native OpenTelemetry support to connect Cerebrium observability to existing monitoring stacks.

Twilio

Example integration for building voice agents that handle telephony interactions.

LiveKit

Used in examples to build outbound calling agents and real-time audio workflows.

WandB

Example integration for running hyperparameter sweeps and tracking training experiments.

Gradio / FastAPI

Examples show deploying interactive chat interfaces using FastAPI and Gradio on Cerebrium.

Triton + TensorRT-LLM

Example deployment path for achieving high-throughput inference with Triton and TensorRT-LLM.

vLLM

Support for serving open-source LLMs using vLLM as an inference backend.

Stable Diffusion XL

Examples demonstrating image generation workflows using SDXL and refiners.

Benefits

Low-latency serving from the first request via fast snapshot-based cold starts.
Automatic, real-time autoscaling across thousands of GPUs without reservations or capacity planning.
Production-grade observability (logs, metrics, scaling events) with native OpenTelemetry support.
Compliance-ready foundations (SOC 2, HIPAA, GDPR, ISO) and data residency controls.
Bring-your-own-code deployment model for reproducible, versioned runs without SDK lock-in.

Limitations

No verified limitations are available.

Frequently Asked Questions

No verified FAQs are available.

Getting Started

  1. 1 Step 1: Visit the site and sign up or click 'Try it now' / 'Book a demo' to start an evaluation.
  2. 2 Step 2: Point Cerebrium to your application entry point or provide a Dockerfile (bring-your-own-code).
  3. 3 Step 3: Launch runs via the CLI (example shown: 'cerebrium run ...') or dashboard, then monitor logs, metrics, and scaling events through the observability stack.

Support

docs

Documentation and tutorials are linked from the site (Docs link present in site navigation).

status

Status page referenced on the site for uptime and incident information.

contact / sales

Contact Us and Book a demo links for sales and pre-sales support.

blog

Product updates, tutorials, and case studies available via the site's blog.

API

Available: Yes

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