docs.coreweave.com

If you're new to Ahrefs and want to run your first SEO audit, the fastest path is: open Site Explorer, enter your target URL, review the Overview for a health snapshot, then dig into Organic Keywords, Top Pages, and Site Audit to find specific problems. From there, build a short prioritized to-do list instead of trying to fix everything at once.

This tutorial walks through that workflow using a realistic starting scenario, explains what the numbers mean, and shows how to turn findings into actions.

Before You Start: Pick a Narrow Scope

A common beginner mistake is auditing an entire large website on day one. The reports become overwhelming, and you can't tell which issues matter.

Instead, choose one of these starting points:

• A single important page (your homepage or a key product/service page)
• A small site (under ~50 pages, e.g., a personal blog or small business site)
• One section of a bigger site (e.g., /blog/)

For this tutorial, assume you're auditing a small business site with about 30 pages. The same steps scale up later.

You'll need an Ahrefs account to follow along. Ahrefs offers paid plans, and pricing and feature limits change over time, so check the current Pricing page for what's included in each tier before committing.

Step 1: Enter Your Target in Site Explorer

Site Explorer is Ahrefs' core tool for analyzing any website or URL.

1. Open Site Explorer from the top navigation.
2. In the search box, paste your domain (e.g., example.com).
3. Choose the Exact URL or Domain mode depending on scope. For a full-site view, use Domain or Prefix; for a single page, use Exact URL.
4. Press Enter.

You'll land on the Overview report. Don't try to absorb everything — focus on four numbers first.

Reading the Overview Snapshot

Important caveat: Ahrefs' traffic and keyword numbers are estimates based on its own data. They won't match Google Search Console or your analytics exactly. Treat them as directional, not absolute.

Step 2: See What You Already Rank For

Go to Organic Keywords in the left sidebar. This shows queries where your site appears in search results.

Sort by Traffic (descending) to see which pages bring the most estimated visitors. Then look for:

• Keywords ranking in positions 4–15 — these are often the easiest wins. A small content or on-page improvement can push them onto page one.
• Keywords with high volume but low position — potential opportunities if the topic is relevant.
• Irrelevant keywords — if you rank for something off-topic, it may signal thin or mismatched content.

Write down 5–10 of the position 4–15 keywords. These become your first optimization targets.

Step 3: Find Your Best and Weakest Pages

Open Top Pages. This ranks your URLs by estimated organic traffic.

Look for two things:

1. Your top performers — understand what topics and formats work. Can you create more content like this?
2. Pages with traffic but poor rankings — these may need on-page fixes (title, headings, internal links).

If a page gets zero traffic and targets a topic you care about, it's a candidate for a rewrite or consolidation.

Step 4: Run a Technical Site Audit

Now move to Site Audit. This crawls your site and flags technical and on-page issues.

1. Click Site Audit → New project.
2. Enter your domain and set crawl settings (default is usually fine for a small site).
3. Start the crawl and wait for it to finish.

Once complete, you'll see a Health Score and a list of issues grouped by category.

Which Issues to Fix First

Not all issues are equal. Prioritize in this order:

Ignore low-impact warnings (like minor meta description length) until the big items are handled.

Step 5: Turn Findings Into a To-Do List

You now have raw data. Convert it into a short, actionable list. Example:

1. Fix 3 broken links found in Site Audit.
2. Rewrite title tags on 5 pages with duplicate titles.
3. Improve 4 pages ranking in positions 6–12 by adding missing subtopics and internal links.
4. Remove or update 2 thin pages with no traffic.

Keep the list to 5–10 items max for your first audit. Finishing a short list beats starting a long one.

Common Beginner Mistakes

• Chasing every red flag. Site Audit flags many minor issues. Fix what affects rankings and users first.
• Trusting estimates as exact numbers. Ahrefs data is modeled, not measured from your analytics.
• Auditing a huge site too early. Start small to learn the interface.
• Ignoring search intent. A page can be technically perfect but still fail if it doesn't match what searchers want.
• Forgetting to re-crawl. After fixes, run Site Audit again to confirm improvements.

Where to Go Next

Once your first audit is done:

• Compare with competitors using Site Explorer's Competing Domains and Content Gap reports.
• Track keyword rankings over time with Rank Tracker.
• Explore backlink opportunities in the Backlinks and Link Intersect reports.
• Set up recurring Site Audit crawls so new issues surface automatically.

Your first audit isn't about perfection — it's about building a repeatable habit: enter a target, read the key reports, pick the highest-impact fixes, and act. Do that once a month and your site's health compounds.

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.

CoreWeave's documentation covers two related but distinct tooling areas: Observability for monitoring infrastructure and workloads (Grafana dashboards, logs, metrics, telemetry, alerts), and agent tooling split across Mission Control (an agent plus MCP server for investigating and changing your environment), Agent Lens (agent observability and improvement), and ARIA (an AI research agent for analyzing experiments and building reports). If you need to watch cluster health, use Observability and Mission Control. If you need to build or improve AI agents and models, look at Forge, Agent Lens, and ARIA.

Observability: monitoring fleet hardware and workloads

The Observability section is described as covering "Grafana dashboards, logs, metrics, telemetry, and alerts for monitoring fleet hardware and workloads." In practice, this is the layer you reach for when you want to answer questions like is a node degraded, is a workload saturating its allocation, or did something change in the last hour?

The documented components break down as:

A concrete use case: you notice inference latency rising on a deployment. You open the relevant Grafana dashboard, confirm the metric trend, cross-check logs for errors, and set or adjust an alert so the next occurrence pages you instead of being discovered after the fact.

Mission Control: investigating and making supported changes

Mission Control is documented as "Mission Control Agent and the Mission Control MCP server for investigating your environment and making supported changes." Two things matter here:

• Mission Control Agent — the agent interface for investigating your environment.
• Mission Control MCP server — an MCP (Model Context Protocol) server, which lets MCP-compatible clients interact with Mission Control programmatically.

The phrase "making supported changes" is the key constraint: Mission Control is not described as an unrestricted control plane. Treat it as a way to investigate and apply changes that the documentation explicitly supports, and verify scope before relying on it for anything destructive.

Agent and model tooling under Forge

Forge is the section for developing and evaluating: "Track experiments, evaluate and improve agents and LLM applications, prototype in notebooks, and manage models across their lifecycle." Three named tools are relevant to agent observability and improvement.

Agent Lens

Documented as "Intelligent agent observability and improvement." This is the piece to look at when your question is specifically about how an agent is behaving — as opposed to how the underlying infrastructure is behaving. Observability tells you whether the GPU fleet is healthy; Agent Lens is aimed at the agent layer itself.

ARIA

ARIA is described as an "AI research agent that analyzes experiments, explains results, and builds visualizations and reports," and is marked Preview. The Preview label is worth noting: expect the interface and behavior to be less settled than generally available tooling, and check the docs for current limitations before building a workflow around it.

A fitting scenario: you've run a set of training or evaluation experiments and want a summary of what changed and why. ARIA is the documented tool for analyzing those experiments, explaining results, and producing visualizations and reports.

Weights & Biases alongside Forge

Forge also documents Weights & Biases: "W&B Models for experiment tracking, hyperparameter sweeps, artifacts, reports, and automations, with W&B Weave alongside for LLM and agent evaluation." If your need is experiment tracking and sweeps, W&B Models is the documented path; if your need is LLM and agent evaluation specifically, that's W&B Weave.

Choosing between them

Where to start

The documentation index groups these under two top-level areas: Infrastructure (Platform, Mission Control, Infra Control, Observability, Workloads, Access Control) and Forge (develop and evaluate). If your goal is monitoring and troubleshooting, start in Infrastructure → Observability, then read Mission Control if you need agent-assisted investigation. If your goal is building or improving agents and models, start in Forge and read Agent Lens, ARIA, and the Weights & Biases entries together, since they cover overlapping parts of the experiment-and-evaluation lifecycle.

CoreWeave's documentation describes a platform for deploying, managing, and observing workloads, organized into two main areas: Infrastructure (the platform layer) and Forge (the development and evaluation layer). If you want to run model training, inference, or isolated compute jobs, the docs point you to specific products depending on whether you need full cluster control, a managed serving endpoint, or a throwaway sandbox.

Workload products

The Workloads section of the documentation lists three named products:

These cover the core execution patterns: batch/HPC-style scheduling (SUNK), model serving (Inference), and isolation (Sandbox).

Serverless services

If you don't want to manage infrastructure yourself, the docs describe serverless options:

• Serverless Inference — open-source foundation models through an OpenAI-compatible API, integrated with Weave.
• Post-Training — post-train and fine-tune LLMs with Serverless RL and SFT, and distill specialized models from real application traffic.
• Serverless Sandbox — on-demand, isolated compute environments you create and discard from Python.

The distinction matters: Serverless Inference is for consuming models behind a familiar API, Post-Training is for adapting models to your own data, and Serverless Sandbox is for short-lived isolated execution driven from Python code.

Managing and observing workloads

Two infrastructure products sit alongside the workload layer:

• Mission Control — an agent and an MCP server for investigating your environment and making supported changes.
• Infra Control — CoreWeave Kubernetes Service (CKS) plus the storage and networking products your clusters use.

For monitoring, Observability provides Grafana dashboards, logs, metrics, telemetry, and alerts for fleet hardware and workloads. Access Control covers accounts and authentication, IAM access policies, automated user provisioning, and trust and compliance.

In practice these work together: you define who can act (Access Control), run and adjust workloads (Infra Control, Mission Control), and watch what happens (Observability).

Forge: developing and evaluating

Forge is the development and evaluation side of the platform:

• Weights & Biases — W&B Models for experiment tracking, hyperparameter sweeps, artifacts, reports, and automations, with W&B Weave for LLM and agent evaluation.
• ARIA — an AI research agent that analyzes experiments, explains results, and builds visualizations and reports (marked 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.

Where to start

The documentation index splits into Infrastructure (Platform, Mission Control, Infra Control, Observability, Workloads, Access Control) and Forge (the development and evaluation tools above). If your goal is to run a job, start with Workloads or the serverless services; if your goal is to experiment and track results, start with Forge.

Forge is the section of the CoreWeave documentation that covers developing and evaluating AI workloads, rather than running the underlying infrastructure. It is the place to look if you want to track experiments, evaluate and improve agents and LLM applications, prototype in notebooks, or manage models across their lifecycle. If your question is about provisioning GPUs, clusters, storage, or networking, that lives under Infrastructure instead.

What Forge covers

The documentation groups Forge around a single purpose: "Develop and evaluate." Within that, it lists four broad capabilities:

• Track experiments — record runs, parameters, and results.
• Evaluate and improve agents and LLM applications — measure quality and iterate.
• Prototype in notebooks — explore data and model workflows interactively.
• Manage models across their lifecycle — version, store, and hand off models and datasets.

The tools inside Forge

The relationship between these is layered rather than parallel. W&B Models handles the general experiment-tracking layer, and Weave sits alongside it specifically for LLM and agent evaluation. ARIA and Agent Lens build on top of that recorded data — ARIA to interpret experiments and produce visualizations and reports, Agent Lens to observe and improve agents. Notebooks and Registry are the working surfaces: you prototype in notebooks, and you version and hand off what you produce through the registry.

How Serverless services fit in

Forge also lists Serverless services, described as a way to "serve, fine-tune, and run models on CoreWeave infrastructure without managing it." Three are named:

• 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.

These sit in Forge because they are part of the model development and evaluation loop rather than the infrastructure layer. Serverless Inference being integrated with Weave is the clearest link: you can serve a model and evaluate it in the same workflow.

Where to start

If you are new to Forge, the order implied by the documentation is:

1. Notebooks — prototype and explore data or model workflows.
2. W&B Models and Weave — track experiments and evaluate LLM or agent behavior.
3. ARIA and Agent Lens — once you have runs recorded, use these to analyze results and improve agents. Note that ARIA is labeled Preview.
4. Registry — version what you want to keep and hand off for deployment.
5. Serverless services — serve, fine-tune, or run the model without managing infrastructure.

If you instead need to set up the environment these run on — instances, capacity, regions, billing, or policies — start from the Infrastructure section of the docs. Forge assumes that layer is already in place.