Habibi vs Weights & Biases

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Habibi Evaluation	Weights & Biases Evaluation
Tagline	Self-hosted generative engine optimization for tracking brand mentions across ChatGPT, Perplexity, and Gemini	The ML experiment tracker, now with LLM eval features.
Category	Evaluation	Evaluation
Pricing	Freemium· Self-host free under the Sustainable Use License; deploy via Opsily starting at $20/month for the underlying server (no separate Habibi seat fees). Users bring their own OpenAI, Perplexity, and Google API keys, which are billed separately by those providers.	Freemium· Free personal; team from $50/mo per seat
Model	GPT-4o, Perplexity Sonar, Gemini (bring-your-own API keys)	Platform (any LLM)
Editorial score	—	8.4 / 10
Use cases	AI answer engine visibility trackingChatGPT brand mention monitoringPerplexity citation trackingGemini share-of-voice reportingCompetitor GEO benchmarkingPage-level citation mappingClient-facing GEO reporting for agenciesGDPR-compliant AI visibility monitoringPrompt-library A/B testing for content changes	ML experimentsLLM evalWeave
Pros	Self-hosted with data kept in a local SQLite database, so prompt libraries and competitor lists never leave your infrastructure Flat server-based pricing (from about $20/month via Opsily) instead of per-seat SaaS fees that typically run $99-$579/month Unlimited team seats at no extra cost, which suits agencies managing many client workspaces Multi-sample runs per prompt smooth out LLM non-determinism and give more trustworthy mention-rate numbers Page-level citation tracking shows which specific URLs answer engines actually cite, not just brand-name mentions Covers the three highest-traffic answer engines (ChatGPT, Perplexity, Gemini) in one dashboard EU (Germany) hosting option and bring-your-own-key model make GDPR and data-sovereignty stories straightforward	Industry-standard for ML tracking Weave adds LLM-native eval Mature, reliable Strong enterprise features
Cons	You pay separately for OpenAI, Perplexity, and Gemini API usage, and heavy sampling can make those bills non-trivial Requires running and maintaining a server (or paying Opsily to do it), which is more setup than a pure SaaS signup Sustainable Use License is source-available but not true OSI open source, so commercial resale as a service is restricted Coverage is limited to ChatGPT, Perplexity, and Gemini today; Copilot, Claude, and Grok visibility are not first-class GEO is a young discipline and the underlying prompt-sampling methodology can still misrepresent volatile answer surfaces	Heavier UX than LLM-native tools LLM features still catching up
Website	opsily.com	wandb.ai

Pick Habibi if

✅ Self-hosted with data kept in a local SQLite database, so prompt libraries and competitor lists never leave your infrastructure
✅ Flat server-based pricing (from about $20/month via Opsily) instead of per-seat SaaS fees that typically run $99-$579/month
✅ Unlimited team seats at no extra cost, which suits agencies managing many client workspaces
✅ Multi-sample runs per prompt smooth out LLM non-determinism and give more trustworthy mention-rate numbers

Pick Weights & Biases if

✅ Industry-standard for ML tracking
✅ Weave adds LLM-native eval
✅ Mature, reliable
✅ Strong enterprise features

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