The Cost of Modern Keyword SaaS
Most keyword grouping tools charge premium enterprise subscription rates to group massive exports. By utilizing local sentence transformer AI models, you can run high-quality semantic clustering on your own machine for free.
How It Works
We use Python, the Hugging Face sentence-transformers library, and basic scikit-learn algorithms. We embed the keywords into vector spaces and cluster them using K-Means or DBSCAN. Sentence transformers map thematic intent to mathematically close positions in space.
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(keywords)
kmeans = KMeans(n_clusters=12).fit(embeddings)
Result
You can process lists of 50,000 keywords in minutes, yielding thematic clusters of pure intent without sending any proprietary data to a paid API.