SEO Keyword Clustering Tool

Overview

SEO Keyword Clustering Tool is a locally run Python and Streamlit application for organizing SEO keyword lists. It is best suited to users who want SERP-based grouping and can manage a DataForSEO account and its usage costs. Its strongest case is combining keyword metrics, adjustable clustering, and exportable reports in a self-hosted workflow.

Key features

SERP clustering and metrics

The core method groups terms according to overlapping URLs in search results. Default, Strict, and Balanced Strict algorithms, plus Search Volume and CPC strategies, give users several ways to shape the grouping; that flexibility is useful when targeting precise SERP opportunities, but the results depend on paid DataForSEO calls. Those calls retrieve SERP results, search volume, CPC, keyword difficulty, and search intent.

Batch uploads suit larger keyword lists, though API expense is the practical ceiling: the project says SERP clustering costs $0.50+ per keyword and that the number of keywords is limited by API costs. DataForSEO's Sandbox and Live environments can be configured, which gives users a way to choose between those API modes.

Cache, analysis, and exports

A local SQLite cache checks for saved API responses before making calls, and users can set its duration. That can reduce repeat requests, but it does not remove the cost of fetching new data. The interactive workbench supports filtering and summarizing clusters, and reports can be exported as CSV or multi-sheet Excel files, making the output practical to carry into other analysis workflows.

Semantic clustering

The project describes local embedding-based semantic clustering as unlimited and free of API costs, and says this mode suits large lists and semantic grouping. However, semantic clustering also appears in the roadmap as a planned feature. That conflicting status makes it difficult to recommend this mode as a dependable reason to choose the tool; SERP clustering is the clearer use case.

Pricing

The tool is free, with no paid product plan described. Free access to the application does not make SERP analysis cost-free: DataForSEO API usage is priced at $0.50+ per keyword, and API costs determine how many keywords users can process. There is no seat or quota structure described for the application itself, so the meaningful budget constraint is the external API usage.

There is no lower application tier that trades away features, but users who need frequent or very large SERP runs should account for the per-keyword API charge before adopting it. The local semantic mode could avoid those API costs if it is usable, though its status is uncertain.

Platforms

The application is described as a Python and Streamlit desktop tool that runs locally on Windows, macOS, or Linux. It is also categorized for web and self-hosted use, but the documented installation path is local setup with Python and Streamlit rather than a hosted service. DataForSEO credentials are stored in a local .streamlit/secrets.toml file. The roadmap includes secure multi-user authentication as future work, so this is better suited to individual or controlled local use than to a team needing built-in account management.

Who it's for

SEO practitioners who need precise SERP-oriented clustering, batch keyword processing, and spreadsheet-ready reporting are the natural audience, provided they are comfortable setting up a local Python application and paying DataForSEO charges. Users primarily seeking semantic grouping for large lists should wait for clearer confirmation of that feature, while teams that require secure multi-user access should look elsewhere for now.

Pros and cons

• Pros: Multiple SERP algorithms and prioritization strategies let users adapt grouping to their targeting approach.
• Pros: The local cache, interactive filtering, and CSV and Excel exports support a repeatable analysis workflow.
• Pros: The application is free and MIT-licensed, and the project welcomes contributions through GitHub issues and pull requests.
• Cons: SERP calls cost $0.50+ per keyword, making large runs potentially expensive despite the free application.
• Cons: Semantic clustering's simultaneous description as a feature and roadmap item leaves its availability uncertain.
• Cons: Secure multi-user authentication remains planned, limiting its fit for teams that need built-in account controls.

Alternatives

Keyword Clustering Tools is the broader category directory for comparing options. Consider Topvisor instead if you want a freemium service with a free XS plan for unlimited projects and keywords, or a paid S plan at 29.00 USD per month. Absolute Cluster is another freemium option, with a free plan capped at one project and 1,000 keywords that also includes content briefs, an internal linking map, exports, and one article draft generation.

ContentGecko Keyword Clustering may fit users who want a free web plan for up to 200 keywords using its standard algorithm, with a paid plan at 19.90 EUR per month for 50,000 keyword credits. For a free web tool with a stated monthly allowance, NeedMyLink Keyword Clustering Tool offers 500 keywords per month, or 1,000 total after email confirmation.

SEO Algorithm Keyword Clustering is worth considering if a free, no-signup stem-based option for up to 200 keywords is a better fit; its SERP-based PRO and AI Semantic plans use credits. 100 SEO Tools Keyword Clustering Tool is a free browser-based alternative described as having no signup, limits, or hidden costs. Optiwing is another freemium option, with a Starter subscription at 20.00 USD per month for 5,000 credits and one-time credit packages. Pro SERP Cluster is a free web alternative with CSV import and export and a limit of 500 keywords per clustering run.

Verdict

Choose SEO Keyword Clustering Tool if you want flexible, SERP-overlap clustering in a locally run workflow and are prepared to manage Python setup and DataForSEO costs. Its combination of clustering controls, cached responses, analysis tools, and spreadsheet exports makes it a credible fit for SERP-focused work. Look elsewhere if you need predictable high-volume costs, confirmed semantic clustering, or secure multi-user access today.