What Is CoreWeave Documentation (docs.coreweave.com)?

Updated

CoreWeave Documentation is the official documentation site for CoreWeave, a cloud platform for deploying, managing, and observing workloads. It is aimed at users who run workloads on CoreWeave infrastructure, and it organizes its content into two main areas: Infrastructure and Forge. If you need to understand how CoreWeave is structured, find setup instructions, or learn how to operate workloads on the platform, this is the reference site to start from.

What the documentation covers

The site is divided into a documentation index with two top-level sections.

Infrastructure

This section covers the platform layer — the resources your workloads run on and the controls around them:

  • Platform — getting started, choosing instances and capacity, regions and Availability Zones, billing, and policies.
  • Mission Control — the Mission Control Agent and the Mission Control MCP server, used for investigating your environment and making supported changes.
  • Infra Control — CoreWeave Kubernetes Service (CKS) plus the storage and networking products your clusters use.
  • Observability — Grafana dashboards, logs, metrics, telemetry, and alerts for monitoring fleet hardware and workloads.
  • Workloads — SUNK for Slurm on Kubernetes, CoreWeave Inference for serving models, and CoreWeave Sandbox for isolated environments.
  • Access Control — accounts and authentication, IAM access policies, automated user provisioning, and trust and compliance.

Forge

Forge is the developer-facing area for building and evaluating AI work:

  • Weights & Biases — W&B Models for experiment tracking, hyperparameter sweeps, artifacts, reports, and automations, with W&B Weave alongside for LLM and agent evaluation.
  • ARIA — an AI research agent that analyzes experiments, explains results, and builds visualizations and reports. Marked as Preview.
  • Agent Lens — intelligent agent observability and improvement.
  • Notebooks — reactive Python notebooks for exploring data, prototyping model workflows, and analyzing runs and artifacts.
  • Registry — a versioned hub for models and datasets with lineage and deployment handoff.
  • Serverless services — serving, fine-tuning, and running models without managing infrastructure:
    • Post-Training — post-train and fine-tune LLMs with Serverless RL and SFT, and distill specialized models from real application traffic.
    • Serverless Inference — open-source foundation models through an OpenAI-compatible API, integrated with Weave.
    • Serverless Sandbox — on-demand, isolated compute environments you create and discard from Python.

Who it is for

The documentation is written for people who deploy, manage, and observe workloads on CoreWeave. That includes:

  • Platform and infrastructure engineers setting up clusters, storage, and networking.
  • ML and AI practitioners running experiments, fine-tuning, or serving models.
  • Operations staff who need observability, access control, and policy information.

How to use it

Start with the Documentation Index to pick the area that matches your task. If you are new to the platform, begin with Platform under Infrastructure to get started and understand instances, capacity, and regions. If your work is model- and experiment-focused, go directly to Forge. Each section is self-contained, so you can move between Infrastructure and Forge depending on whether you are managing the environment or building on top of it.

One note on scope: the documentation describes capabilities and workflows, but the page evidence does not include pricing details or login requirements. Treat those as things to confirm separately rather than assuming free or unrestricted access.