Free Keywords Clustering Tool

Keywords clustering is an advanced SEO strategy that relies on grouping semantically related words together. It's also referred to as keywords grouping. Before you run a clustering, you need to collect a large quantity of keywords (hundreds or thousands). To gather short‑tail and long‑tail terms, you can use tools like Google Search Console, Ahrefs, SEMrush or Moz.

Enter a list of keywords by either copy‑pasting them in the text field or uploading a text file. In both cases, there should be one keyword per line. (Optional) You can also indicate a topic — it helps organize clusters from most closely related to least closely related.

There are three types of keywords clustering:
1) Lemma‑based: relies on lemmatization (text normalization). Most free grouping tools use it. For example: cluster, clusters, clustering share the same root. If you code, you can prototype with Python — nltk is a good start.
2) SERP‑based: searches each keyword and groups those that co‑occur in the top search results. Results depend on the clustering level (minimum overlaps) and whether you pick a “soft”, “moderate” or “hard” mode.
3) NLP‑based: the newer approach using Natural Language Processing (NLP). Google’s BERT helps search engines understand query meaning; similarly, NLP lets us group queries that reflect the same intent.

Since Google’s 2013 Hummingbird update, focus shifted from single keywords to topic clusters. RankBrain (2015) confirmed Google can infer relationships between related queries.

Clustering helps you structure your site for users and crawlers. Instead of one page per keyword (old school SEO), you target multiple related queries with one strong page. Done right, clustering can boost organic traffic. It’s core to SEO content strategy and planning. Imagine starting a blog with 20 000 keywords from competitor research — clustering breaks them into groups, shaping your architecture and helping Google/Bing/Yahoo understand your site.

Manual grouping is painfully slow. With 5 000 keywords, deciding intent overlap by hand could take hours. Simple Excel formulas won’t cut it. SERP‑based or NLP‑based tools automate the heavy lifting and return results in minutes.

Pick a pillar topic first. That pillar page gives a broad overview (usually a high‑volume term). Example: you sell green tea online. Your pillar page “green tea” touches all subtopics (“green tea benefits”, “types of green tea”, “green tea recipes”…). Each subtopic gets its own in‑depth post. Clustering ties semantically related queries to the right page, capturing intent. Finally, connect pillar ↔ subtopics with internal links to build topical authority.

We use a modern NLP, semantics‑first pipeline (Transformer embeddings, à la Google’s BERT and its successors) plus clustering/graph algorithms to group closely related queries. The exact recipe is proprietary; the idea is simple: understand meaning & intent, then keep only tightly connected keywords together.

LLMs like ChatGPT, Claude, DeepSeek or Mistral are great for ideation and labeling. Ask them to expand a seed list, merge duplicates, or suggest intent labels. But they are stochastic and non‑deterministic — boundaries shift between runs. Our tool handles the quantitative piece (vector similarity, thresholds, optimisation); then you can loop results back into an LLM to polish names, angles or CTAs.

Think of it as a two‑step combo: (1) use an LLM to brainstorm or clean the raw keyword pool; (2) run the list through our clustering engine for clean, size‑controlled groups. Afterward, an LLM can summarise a cluster, infer intent (informational / transactional / navigational, etc.) or draft briefs — while the underlying math that created the cluster stays deterministic.

The free version groups up to 5 000 keywords (English only). Need larger lists or advanced exports? Choose a paid plan.

There’s no universal “right” size. Context matters. Our tool uses NLP to optimise cluster sizes for quality, but you can override with your own minimum/maximum values in Advanced Settings.

By default we sort clusters by size (number of keywords). If you prefer, switch the “Sort by” dropdown to Cluster relevance to reorder them by our relevance score.