laion.ai

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.

LAION's official site, laion.ai, is the entry point for all of these: the top navigation links to Projects, Team, Blog, Notes, Press, About, and FAQ, and the same menu also includes Donations, Privacy Policy, Dataset Requests, Impressum, and GEP Policy. Use the Projects page to browse released datasets and models, the FAQ for common questions about the organization and its resources, and Dataset Requests to submit a request for dataset access or collaboration.

What each navigation entry is for

The site describes LAION as the Large-scale Artificial Intelligence Open Network, a non-profit that provides datasets, tools, and models to support open machine learning research. The menu reflects that structure:

If your goal is to use LAION resources rather than contact the organization, start with Projects. If your goal is to obtain a dataset that isn't publicly posted, or to ask about one, use Dataset Requests.

Finding datasets and models through Projects

The Projects section is where LAION lists its released resources. According to the site, these include:

• LAION-400M — an open dataset of 400 million English image-text pairs.
• LAION-5B — 5.85 billion multilingual CLIP-filtered image-text pairs.
• CLIP H/14 — described as the largest CLIP vision transformer model.
• LAION-Aesthetics — a subset of LAION-5B filtered by a model trained to score aesthetically pleasing images.
• Re-LAION 5B — a release dated 30.08.2024.

To locate any of these, open the Projects entry from the top menu and look for the matching name. Expect dataset pages to describe what the dataset contains rather than to host a single download button; large datasets are typically distributed through linked hosting rather than served directly from the homepage.

Submitting a dataset request

Dataset Requests is a separate menu item, not part of Projects. Use it when you need a dataset that isn't already published, or when you want to ask the organization about access. Before submitting, check the Projects list and the FAQ — if the dataset is already released, the request may be unnecessary. The site does not publish a form structure or response timeline in the material available here, so treat the request as an inquiry rather than a guaranteed delivery channel.

Checking the FAQ before contacting anyone

The FAQ is the fastest way to resolve questions about what LAION is, what it provides, and how its resources are licensed or used. Read it before sending a dataset request or press inquiry; many basic questions about the organization's non-profit status and open-resource mission are answered there. The site states that LAION is 100% non-profit and 100% free, which is the framing to keep in mind when interpreting what the FAQ says about access.

A practical path through the site

1. Looking for a specific dataset or model → Projects, then match the name (LAION-400M, LAION-5B, CLIP H/14, LAION-Aesthetics, Re-LAION 5B).
2. Have a general question → FAQ.
3. Need a dataset that isn't listed → Dataset Requests.
4. Want to understand the organization → About, then Team.
5. Following announcements → Blog or Notes; media inquiries → Press.
6. Supporting the work → Donations.

One caveat: the menu labels are the reliable map here. The site's available material does not describe individual page layouts, so navigate by these labels rather than expecting a particular button or search box.

LAION provides four main dataset resources: LAION-400M (400 million English image-text pairs), LAION-5B (5.85 billion multilingual CLIP-filtered image-text pairs), LAION-Aesthetics (an aesthetic-quality subset filtered from LAION-5B), and the Re-LAION 5B release dated 30 August 2024. All are offered by LAION as a non-profit organization that describes itself as "100% non-profit" and "100% free," with the stated goal of liberating machine learning research by providing datasets, tools, and models.

Dataset Overview

How the Datasets Relate

LAION-5B is the broad foundation: 5.85 billion multilingual image-text pairs that have passed CLIP filtering. LAION-Aesthetics is not a separate crawl but a subset of LAION-5B, narrowed by an aesthetic scoring model. LAION-400M is the earlier, English-only collection at 400 million pairs. Re-LAION 5B, published on 30 August 2024, is the most recent release listed on the site.

This layering matters when choosing: if you need multilingual coverage at maximum scale, LAION-5B is the base resource; if you need a smaller, quality-filtered pool for image-generation or aesthetics-related work, LAION-Aesthetics is the derived option.

Beyond Datasets

LAION also lists CLIP H/14, described as the largest CLIP vision transformer model, alongside its datasets, tools, and models. The organization frames its work around open public education and reusing existing datasets and models to use resources more environment-friendly.

Practical Notes Before You Start

• Check the release you need. The site lists Re-LAION 5B as a distinct release from the original LAION-5B; confirm which version fits your project before downloading.
• Plan for scale. A 5.85 billion-pair dataset is not a small download; storage and processing capacity should be settled first.
• Use the site's request channels. LAION provides a Dataset Requests entry point and an FAQ, which are the appropriate places to resolve access and usage questions rather than assuming terms.
• Licensing and content review are your responsibility. The site's own pages (including its GEP Policy and Privacy Policy) are the reference points; verify conditions for your specific use case.

For the current status of any dataset, the LAION Projects page and Blog are the authoritative sources, since release details can change after publication.

LAION provides at least one model explicitly listed on its site: CLIP H/14, described as the largest CLIP (Contrastive Language-Image Pre-training) vision transformer model. LAION presents models alongside datasets and tools as open resources intended to make machine learning research more open and to encourage reuse of existing datasets and models. The site does not list additional models beyond CLIP H/14, so any claim of a broader model catalog would go beyond the available information.

Where the model fits in LAION's offerings

LAION describes itself as a non-profit organization that provides datasets, tools and models to liberate machine learning research. Its stated goals are open public education and more environment-friendly use of resources through reuse of existing datasets and models. That framing matters if you are deciding whether LAION is a model source or primarily a data source: the site's featured resources are weighted toward datasets, with CLIP H/14 as the named model.

Featured resources on the site

How to decide whether CLIP H/14 is what you need

Use the following conditions to judge fit:

• You need a CLIP-style vision transformer at large scale. CLIP H/14 is the only model LAION names, and the site positions it as the largest CLIP vision transformer model. That is the relevant signal if your task involves image-text contrastive representations.
• You need a model family or multiple checkpoints. The site does not list a model family, variants, or sizes beyond CLIP H/14. Treat the model listing as narrow rather than as a catalog.
• You need datasets rather than a model. LAION's featured resources are datasets: LAION-400M, LAION-5B, LAION-Aesthetics, and the Re-LAION 5B release. If your work is data-centric, these are the entries the site foregrounds.
• You need licensing, weights, or download details. The provided page content does not include license terms, weight formats, or download instructions for CLIP H/14. Check the site's project pages and documentation before planning an implementation around it.

Practical next step

Start from the LAION site's Projects section and the individual resource entries, since the homepage only names CLIP H/14 without technical detail. For a concrete task—for example, building an image-text retrieval baseline—confirm the model's availability and terms on the project page first, then decide whether to pair it with a LAION dataset such as LAION-400M for English image-text pairs or LAION-5B for multilingual pairs. The site's stated emphasis on reuse also means you should check whether an existing dataset or model already covers your need before creating new resources.

LAION describes itself as “TRULY OPEN AI. 100% NON-PROFIT. 100% FREE.” In practice, that means the organization publishes machine learning resources — datasets, tools, and models — for public use rather than selling access to them. Its stated purpose is to “liberate machine learning research” by making these resources broadly available, and to support open public education while encouraging reuse of existing datasets and models to reduce environmental burden. The “truly open” claim is therefore about organizational stance and resource availability, not about a specific license for every asset.

The three commitments behind the slogan

LAION’s homepage frames its identity around three points:

• Non-profit status. LAION presents itself as a non-profit organization. Its work is framed around public benefit — open education and research access — rather than commercial product sales.
• Free availability. The site states its resources are “100% FREE.” This describes LAION’s own positioning; it does not by itself specify the terms attached to each individual dataset or model.
• Openness as a method. LAION says it provides datasets, tools, and models so that machine learning research is not gated behind proprietary access. The emphasis is on releasing resources others can build on.

A useful way to read the slogan: it is a mission statement about who LAION serves and how it distributes work, not a blanket legal guarantee covering every file it hosts.

What “open” looks like in LAION’s actual catalog

The openness claim becomes concrete in the resources LAION lists on its site:

Two things stand out. First, the scale: these are research-grade resources, not samples. Second, the reuse logic — LAION-Aesthetics is derived from LAION-5B, which is exactly the “reusing existing datasets and models” behavior the organization says it wants to encourage.

Why reuse is part of the openness argument

LAION links openness to environmental efficiency. Training large models and assembling large datasets consume significant compute and energy. By publishing datasets and models that others can reuse instead of rebuilding, LAION argues for a “more environment-friendly use of resources.” In this framing, openness is not only about access — it is about avoiding duplicated effort across the research community.

For a reader deciding whether LAION is relevant to their work, the practical implication is: if you need large-scale image-text data or a CLIP-style vision model for research or experimentation, LAION’s catalog is designed to be a starting point rather than a purchased product.

What the slogan does not tell you

The homepage statement is a positioning claim, and it leaves some questions to be answered elsewhere on the site:

• Per-resource terms. “100% free” on the homepage does not replace the specific terms that may apply to a given dataset or model. Check the documentation for the resource you intend to use.
• Support and maintenance. A non-profit release model does not imply ongoing support commitments for every asset.
• Fitness for a specific use. Open availability says nothing about whether a dataset or model suits your particular task, domain, or quality bar.

Where to go next

LAION’s site organizes the rest of the detail under Projects, Team, Blog, Notes, Press, About, FAQ, Donations, Privacy Policy, Dataset Requests, Impressum, and GEP Policy. If your question is about a specific dataset, the Dataset Requests page and the individual project entries are the places to look; if it is about the organization itself, About and FAQ cover that ground.