Foglift vs Lighthouse: Technical Audit Comparison

Lighthouse diagnoses page performance in depth. Foglift adds a broader Technical Audit for AI Readiness, SEO, security, and accessibility. Here is when to use each.

Short Answer

Use Foglift for the broad baseline: unlimited single-page Technical Audits cover AI Readiness, SEO, performance, security, and accessibility with no signup or credit card. Use Lighthouse for deeper lab-performance diagnostics, trace evidence, and CI automation. Teams that need answer-level proof can add daily monitoring across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overview from $49 per month, with API, CLI, and MCP access on the same Launch plan.

Saved workflow example, July 14, 2026

A saved Foglift Technical Audit result flagged a 247KB HTML document while its performance subscore was 90. That is the handoff: the broad audit identifies the cross-discipline warning, then Lighthouse supplies the lab trace and performance diagnostics. Saved results retain the score contract recorded when the audit ran, so this example is a workflow record rather than a current site benchmark.

What Google Lighthouse Measures

Google describes Lighthouse as an open-source automated tool for improving web-page quality. Its established categories cover performance, accessibility, best practices, and SEO. It can run in Chrome DevTools, from the command line, as a Node module, through PageSpeed Insights, or in continuous integration.

Lighthouse's performance score is a weighted lab score. Google's documented Lighthouse 10 weights use First Contentful Paint, Speed Index, Largest Contentful Paint (LCP), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS). Google also warns that scores can vary with test conditions, device choice, routing, extensions, and page experiments.

Lighthouse's Experimental Agentic Browsing Category

Google added an experimental Agentic Browsing category for Chrome 150 and later. It evaluates whether a site is constructed for machine interaction through deterministic audits. The current checks cover registered WebMCP tools and schema, forms that could expose declarative WebMCP, agent-focused accessibility, layout stability, and llms.txt retrieval.

The category does not return a weighted 0 to 100 score. It reports a pass ratio, audit-level pass or fail states, warnings, and informational counts while the standards are still emerging. WebMCP remains a proposed standard in an origin trial. These constraints make Agentic Browsing a useful developer diagnostic while keeping it distinct from prompt-level AI search measurement.

What Foglift Measures

Foglift's Technical Audit gives a cross-discipline baseline for AI Readiness, SEO, performance, security, and accessibility. Every plan includes unlimited single-page audits. The public Free Technical Audit requires no signup or credit card.

The separate AI Visibility monitoring product measures brand mentions, sentiment, competitors, and provider-returned citations where available across five engines. Gemini does not currently expose citation tracking; Google AI Overview remains the grounded Google source lane. Technical Audit data and answer-level monitoring therefore answer different questions.

Foglift vs Lighthouse: Feature Comparison

Capability	Foglift	Lighthouse
Primary job	Broad website Technical Audit	Deep page-quality, lab-performance, and experimental machine-interaction audits
Free access	Unlimited single-page Technical Audits, no signup or card	Open-source audits in Chrome, PageSpeed Insights, CLI, and Node
AI search readiness	8-dimension AI Readiness analysis	Experimental Agentic Browsing checks in Chrome 150+
Performance	Performance score and issue summary	Weighted lab metrics, diagnostics, and opportunities
Accessibility	Accessibility score and issue checks	Automated accessibility audits
Security	Security headers and HTTPS checks	HTTPS and browser best-practice checks
AI crawler access	Search, training, and user-fetch crawler policy checks	llms.txt retrieval and agent-interaction checks; no dedicated crawler-policy audit
AI visibility outcomes	Five-engine monitoring from $49/mo	Not measured
Crawler and referral evidence	Sensor separates crawler purpose from recognized AI referrals	Not measured
Developer workflow	API, CLI, and MCP access from $49/mo	DevTools, CLI, Node module, and Lighthouse CI

Where the AI Readiness Boundary Sits

Lighthouse's experimental Agentic Browsing category tests whether agents can discover and interact with a page through deterministic technical signals. It does not ask ChatGPT, Perplexity, Gemini, Claude, or Google AI Overview about a brand. It therefore cannot measure mentions, citations, sentiment, competitor presence, or share of voice.

Foglift's Technical Audit adds eight dimensions that help a publisher inspect whether a page gives retrieval systems clear, verifiable material:

Structured data richness: whether machine-readable markup adds useful entity detail.
Heading clarity: whether the page hierarchy exposes its main questions and answers.
FAQ quality: whether common questions receive direct, visible answers.
Entity identity: whether the organization, product, and category are stated consistently.
Content depth: whether the page has enough evidence to answer its target query.
Citation formatting: whether claims include named, dated, reachable sources.
Topical authority: whether supporting content covers the surrounding subject.
AI crawler access: whether relevant search and user-fetch agents can retrieve the page.

These checks measure technical and content readiness. They do not establish a ranking formula or guarantee selection in an AI answer.

Where Lighthouse Goes Deeper

Lighthouse gives developers more detail when the problem is page performance. Its report connects weighted lab metrics to diagnostics and opportunities such as main-thread work, render-blocking resources, image delivery, request cost, and document size. Its CLI, Node module, and Lighthouse CI also fit automated engineering workflows.

Treat a Lighthouse score as one controlled run. Google's own documentation explains why performance scores fluctuate and recommends interpreting performance as a distribution rather than relying on one perfect number.

Recommended Workflow

Run Foglift's Free Technical Audit to establish the broad baseline.
Prioritize severe issues across AI Readiness, SEO, performance, security, and accessibility.
Open Lighthouse when performance needs metric-level diagnosis.
Fix the issue, rerun both tools under comparable conditions, and record the change.
Use AI Visibility monitoring for mention, sentiment, competitor, and available citation outcomes. Use Sensor to separate crawler fetches from recognized human referrals.

Reviewed September 15, 2026 by the Foglift team against current Google Chrome documentation and current product contracts. See our company and correction details.

Frequently Asked Questions

What does Google Lighthouse audit?

Google documents Lighthouse as an open-source automated tool for performance, accessibility, best practices, and SEO audits. It also has an experimental Agentic Browsing category in Chrome 150 or later. That category runs deterministic checks for WebMCP integration, agent-focused accessibility, layout stability, and llms.txt discoverability.

Does Foglift replace Google Lighthouse?

The tools are complementary. Foglift gives unlimited single-page Technical Audits across AI Readiness, SEO, performance, security, and accessibility. Lighthouse provides deeper lab-performance diagnostics and developer workflows. Use Foglift to find cross-discipline gaps, then use Lighthouse when a performance issue needs trace-level investigation.

What does Foglift's AI Readiness analysis measure?

Foglift's Technical Audit evaluates eight answer-readiness dimensions: structured data richness, heading clarity, FAQ quality, entity identity, content depth, citation formatting, topical authority, and AI crawler access. These page signals describe technical readiness. They do not prove that an AI engine mentions or cites a brand, which requires prompt-level monitoring.

Does Lighthouse measure AI search visibility?

No. Lighthouse's experimental Agentic Browsing category evaluates technical readiness for machine interaction. It does not query AI engines or measure brand mentions, sentiment, competitors, or prompt-level visibility. Foglift tracks those answer outcomes across five engines from $49 per month and records provider-returned citations where available. Gemini does not currently expose citation tracking.

Which audit should I run first?

Start with Foglift when you need a broad, no-install baseline across five audit dimensions. The single-page Technical Audit is unlimited and requires no signup or credit card. Follow with Lighthouse when performance needs metric-level diagnosis, trace evidence, or CI automation.

Sources

Google Chrome for Developers, Introduction to Lighthouse. Current overview of audit categories and execution methods; last updated June 2, 2025.
Google Chrome for Developers, Lighthouse performance scoring. Documents metric weights, scoring distributions, and run variability.
Google Chrome for Developers, Lighthouse agentic browsing scoring. Documents the experimental Chrome 150+ category, deterministic audit set, score format, and WebMCP origin-trial requirement; last updated May 5, 2026.
Google Chrome for Developers, Registered WebMCP tools. Documents the informational registered-tools audit; last updated July 1, 2026.
Google Chrome for Developers, WebMCP. Explains the proposed standard, origin trial, declarative and imperative APIs, and human-in-the-loop boundary; last updated August 7, 2026.
Foglift, Technical Audit of foglift.io, July 14, 2026. Saved workflow example with a performance subscore of 90 and one large-HTML warning.

Start with the broad baseline

Audit any public URL for AI Readiness, SEO, performance, security, and accessibility.

Run Free Technical Audit

Fundamentals: Learn about GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) (the two frameworks for optimizing your content for AI search engines).