> ## Documentation Index
> Fetch the complete documentation index at: https://docs.brightnode.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Concepts

> Key concepts and terminology for understanding Brightnode's platform and products.

## [Brightnode console](https://console.brightnode.cloud)

The web interface for managing inference, compute, storage, API keys, usage, billing, and team resources.

## [Router](/router/overview)

Brightnode's hosted inference entrypoint. Router gives you one OpenAI-compatible base URL for the models Brightnode makes available through the shared catalog.

## [Model](/models/overview)

A model is an inference target you can call through Router or deploy through Beams. Model IDs are passed directly in your API request, and may include slashes such as `meta-llama/Llama-3.3-70B-Instruct`.

## [API key](/get-started/api-keys)

A Brightnode API key is a scoped credential for programmatic access. Current key scopes are centered on `Inference` and `Deployments`.

## [Bnode](/bnodes/overview)

A dedicated GPU or CPU instance for containerized AI/ML workloads, such as training models, running inference, or other compute-intensive tasks.

## [Network volume](/storage/network-volumes)

Persistent storage that exists independently of your other compute resources. Network volumes are designed for durable datasets, checkpoints, and shared assets across Brightnode workflows.

## [Beams](/beams/overview)

The preview deployment workflow for managed inference models. Beams is intended for cases where you need deployment-level control instead of the shared hosted inference router.

## [Usage analytics](/usage/overview)

The console views for request count, token usage, latency, and cost across your inference traffic.

## Container

A Docker-based environment that packages your code, dependencies, and runtime into a portable unit that runs consistently across machines.

## Data center

Physical facilities where Brightnode's GPU and CPU hardware is located. Your choice of data center can affect latency, available GPU types, and pricing.

## Machine

The physical server hardware within a data center that hosts your workloads. Each machine contains CPUs, GPUs, memory, and storage.