Cerebrium

What it does

Cerebrium runs Python code, a Dockerfile, or a container image on cloud CPUs or GPUs. After a CLI deploy, the code becomes a REST endpoint that accepts JSON input and returns JSON output. It can also serve streaming endpoints, WebSockets, or any ASGI-compatible app.

Typical uses include serving LLMs, voice agents, video and image models, and embedding or reranking services. Instances are added and removed automatically as traffic changes. Cold-start speed relies on memory and GPU snapshotting, apps can be deployed across multiple regions, and compute is billed by the second. The CLI also runs one-off scripts remotely before they become persistent apps.

The platform suits developers and ML teams that need production inference for bursty traffic or low-latency workloads. Getting started means creating an account and installing the CLI. Pricing is usage-based with a free Hobby plan that caps apps, seats, and concurrency, so buyers should check current compliance claims such as the SOC 2 type directly with Cerebrium.

What it can do

• Deploy Python apps or Dockerfiles as serverless REST endpoints that scale automatically
• Serve streaming endpoints, WebSockets, and ASGI-compatible apps
• Run CPU and GPU workloads with memory and GPU snapshotting for fast restarts
• Deploy the same app across multiple regions with multi-region failover
• Use persistent storage for model weights and files
• Export logs and metrics through native OpenTelemetry support

Works with

• OpenTelemetry

Before you choose

• GPU, CPU and memory are billed per second; storage is $0.05 per GB per month, with the first 100 GB free. Plan fees apply separately.
• Hobby limits apps, seats and concurrency; its log retention is inconsistent on the pricing page (1 day versus 7 days). Standard includes unlimited apps, but its seat allowance also needs confirmation: the plan card says unlimited while the comparison table says 10.
• Regioned API hostnames add latency compared with the global api.cerebrium.ai endpoint.

Listed under

• Coding
• Sandboxes and infrastructure
• Developer tools
• GPU
• Inference
• LLM deployment
• MLOps
• Serverless

Get started

Create an account on the dashboard, install the cerebrium CLI, log in, and deploy a first app.

1. Create an account at the Cerebrium dashboard and install the CLI with pip, Homebrew, or a binary download.
2. Run cerebrium login and set the active project with cerebrium project set if your account has multiple projects.
3. Initialize a project with cerebrium init, test a function with cerebrium run, then deploy with cerebrium deploy to get a REST endpoint.

GPU, CPU and memory rates are per second; storage is $0.05/GB/month with the first 100 GB free. Monthly plan fees apply separately. Confirm the contradictory Standard seat and Hobby retention allowances on the official pricing page.

Source: cerebrium.ai/pricing, checked 1 Nov 2026. Prices can change; check before you buy.

Sources for this overview

Product pages, documentation, and pricing checked 1 Nov 2026. Each source supports part of the overview; availability and prices can change.

• Introduction to Cerebrium for real-time AI workloads - Cerebrium ↗docs.cerebrium.ai
• Serverless GPU Infrastructure for Real-Time AI | Cerebrium ↗cerebrium.ai
• REST API - Cerebrium ↗docs.cerebrium.ai
• Security & Data Privacy - Cerebrium ↗docs.cerebrium.ai
• Pay-Per-Second Pricing for Serverless AI | Cerebrium ↗cerebrium.ai

Company website

cerebrium.ai: product, pricing and policy pages

Hacker News

Launch posts and discussion

YC directory mirror

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Social accounts

Accounts the company links, and its posts on X

Human review

Checked by a person

manual_review

Last checked 1 Nov 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.