logz.io 77 B
🛡️ SEO 67 🤖 GEO 84 ⚡ Perf 59 🏗️ Arch 98

logz.io — Global SEODiff Score 77/100

logz.io
📊

logz.io shows strong AI visibility with an ACRI of 80/100, outperforming 94% of indexed domains. Compared to other developer sites (avg score: 57), logz.io performs above the benchmark, suggesting strong competitive positioning in AI search. Its server-rendered architecture ensures AI crawlers receive complete HTML on first request, a key advantage for extractability. With a 2.6× bloat ratio, the page delivers its content without excessive boilerplate, giving AI systems a clean extraction path. Structured data coverage is solid at 2 blocks, covering core entities — expanding to include FAQ or Breadcrumb schemas could strengthen the profile further. Robots.txt grants unrestricted access to the key AI user-agents, which is the strongest starting position for AI visibility.

77
B — Global SEODiff Score
Comprehensive search visibility assessment
Strong foundations, but Performance (59) is your bottleneck.
🎯 Top Fix: Monitor weekly to catch regressions early
🔬 Automated SEODiff Assessment · Snapshot: Mar 21, 2026 · 📋 API
📈 ACRI Trend 28 snapshots
Feb 22 Mar 21
🔔 Recent AI Indexing Activity
No recent changes detected by adaptive crawler.
Does your site score higher than logz.io?
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🧮 Score Transparency — How is this calculated?
🛡️ Traditional SEO (25% weight)67 × 0.25 = 16.8
🤖 AI Readiness / GEO (40% weight)84 × 0.40 = 33.6
⚡ Performance (20% weight)59 × 0.20 = 11.8
🏗️ Architecture & Trust (15% weight)98 × 0.15 = 14.7
Weighted sum = 16.8 + 33.6 + 11.8 + 14.7
Global SEODiff Score = 77 (B)
📊 ACRI Sub-Scores (AI Readiness Detail)
100
Bot Access
avg 92
100
Rendering
avg 93
51
Structure
avg 35
44
Schema
avg 9
85
Tech Stack
avg 63
🔀
Visibility Delta: Google vs AI
Google (Tranco)
Top 2%
Rank #18249
Aligned
Gap
AI (ACRI)
Top 6%
Score 80/100

logz.io has balanced Google and AI visibility — both rank roughly in the same tier. ACRI measures technical crawler readiness. Read the methodology →

Why logz.io ranks here

Tech stackWordPress
Industrydeveloper
RenderingSSR
Schema coverage2 blocks
Token bloat2.6×

Fastest improvements

  • You’re already in decent shape — the next moat is monitoring drift over time.
  • Create an llms.txt file so AI crawlers can discover your content structure without heavy crawling. Generate llms.txt →
  • Run a full entropy audit to find which DOM regions waste the most tokens. Run Entropy Audit →
🧪

JavaScript Rendering Check

We check what AI crawlers miss when they skip JavaScript execution.

Running headless browser to simulate AI extraction…
🛡️

Traditional SEO

67/100 25 % of Global Score 🟢 High Confidence

📝 Title Tag

43 chars
Good length

Optimal range: 30–60 characters for SERP display.

📋 Meta Description

73 chars
Too short

Optimal range: 120–160 characters for snippet control.

🔤 Heading Hierarchy

  • ✓ Exactly 1 <h1> tag — found 1
  • ✓ Has <h2> headings — found 6
  • ✓ <h2> not before <h1>

🔍 Indexability

  • ✓ Canonical tag present → https://logz.io/
  • ✓ No noindex directive
  • ✓ Meta viewport set
  • ✓ HTML lang attribute → en-US
  • ➖ Hreflang tags — N/A (single language site)
  • ✓ Googlebot allowed by robots.txt

🌐 Social / OpenGraph

  • ✓ og:title — Logz.io: Modern Observability Powered by AI
  • ✓ og:description — Stop Chasing Alerts. Get Ahead of Problems with AI-Powered Observability.
  • ✓ og:image — preview
  • ✓ twitter:card — summary_large_image
📐 How the SEO Pillar score is calculated

SEO Pillar = Title (20 pts) + Meta Desc (20 pts) + Heading Hierarchy (20 pts) + Indexability (20 pts) + Social/OG (20 pts)

Each sub-score is derived from the checks above. Canonical tag, lang attribute, og:image, and a single H1 are the highest-impact items.

🤖

AI Readiness / GEO

84/100 40 % of Global Score 🟢 High Confidence

This pillar aggregates citation share, hallucination risk, bot access, schema health, and content extractability. The individual diagnostic sections below contribute to this score.

🔗

Citation Alternatives

Research
💡
Insight: In the developer sector, hikkoshizamurai.jp (ACRI: 88) currently has stronger AI extractability. AI models tend to prefer sources with higher semantic structure and schema coverage. Domains with ACRI < 40 see 3.5× more hallucinations. Read the research →
logz.io
67
Your ACRI Score
88
Industry Peer ACRI
AI models prioritize pages with strong semantic structure and schema coverage. hikkoshizamurai.jp has schema coverage of 5 blocks and uses Custom / Proprietary. Improve your score by implementing the remediation patches below.
📊 Side-by-Side Comparison →
🚨

Hallucination Risk

Research

Is AI lying about your brand? This panel measures how likely LLMs are to hallucinate facts when extracting information from your page.

Analyzing hallucination risk…

🤖 Bot Access Matrix

GPTBot (OpenAI)
Allowed
ClaudeBot (Anthropic)
Allowed
CCBot (Common Crawl)
Allowed
Google-Extended
Allowed
Googlebot
Allowed

👻 Rendering (Ghost Ratio) Docs

Ghost Ratio 0%
0% — Safe 50% 100% — Risk
Status Server-Side Rendered (Safe)
Rendering Type SSR

📊 Structure & Information Density Docs

Structure Grade 51/100 — Fair
Structured Elements 93 elements (93 lists, 0 rows, 0 headers)
Total Words1172
Raw Density7.9%

🏷️ Schema Health Docs

Organization Schema ✅ Present
Product / Service Schema ⚠️ Not Found
Total Schema Blocks2 block(s) — Basic (low value for AI)

Schema Coverage Map

2/7 schema types detected
✅ Organization
❌ Product/Service
❌ Breadcrumb
❌ FAQ
❌ Article
✅ WebSite
💡Product / Service schema missing. AI models don't know this is a SaaS product. Add Product or SoftwareApplication schema so AI understands what you offer and can surface pricing/features.
💡BreadcrumbList schema missing. AI cannot understand your site hierarchy or how pages relate to each other.
💡FAQ schema missing. Adding FAQPage schema lets AI models directly extract Q&A pairs for Featured Snippets and chatbot answers.

📐 AI Efficiency Metrics Docs

71
AI Extractability
Medium
Crawl Cost
None
Blocklist Risk
Extractability71/100 — AI models can easily extract answers from this page
Crawl CostMedium (50/100) — moderate for AI crawlers to process
Blocklist RiskNone — 0 of 5 AI crawlers blocked

Token Bloat Research

38%
🗑️ 62%
Useful Content (91.8 KB)Bloat (149.8 KB)
Token Bloat Ratio2.6× — Lean

Multimodal Readiness

Visual Context91% Optimized for Vision
Image Alt Coverage49 / 54 images have alt text

TDM Rights

TDM-Reservation HeaderNot set
X-Robots-Tag: noaiNot set

🔥 Structural Entropy Check Research

60 Entropy
Fair Token Bloat: Medium
Noise Ratio: 62.0% · SNR: 0.61 · Signal: 23493 / Noise: 38352 tokens

🔬 AI-Crawler Simulation

See your website the way AI crawlers do. CSS stripped, structure labeled, content chunked.

🌐
This is what humans see — styled, branded, visual.
Toggle to "AI Agent View" to see what GPTBot, ClaudeBot, and other AI crawlers actually extract from this page.
🤖

AI Answer Preview

NEW

See how AI models summarize your site. Left: your actual content. Right: what the LLM extracts and says about you.

Simulating AI extraction…
🧠

The LLM Interpretation

AI-VERIFIED

SEODiff AI analyzed the extracted content of logz.io and produced this structured business intelligence. Fields marked SEMANTIC VOID indicate information the AI could not find — a critical gap in your site’s machine-readability.

Core Offering
Logz.io provides a comprehensive OpenSearch solution for DevOps organizations seeking robust search, visualization, and analytics capabilities, enabling them to gain
Target Audience
DevOps engineers, IT managers, data analysts, and security professionals involved in managing and analyzing infrastructure and application data.
Pricing Model
Logz.io offers a range of pricing plans, including a freemium model with limited features and paid tiers based on usage and features, starting from $10/user/month.
🔗 Integration Partners
AWSAiven.ioCapitalOneCoralogixInstaclustrLogz.ioOpsterOracle
🏆 Competitive Moat
Logz.io’s competitive advantage lies in its community-driven approach to OpenSearch, combined with AI-powered sprint planning and real-time resource optimization, offering a streamlined and user-friendly experience for observability.
📊 Content Depth
8/10
🔄 Programmatic SEO Signals
Integration directory pagesTemplate comparison pages
⚡ Key Pain Points
• No structured FAQ schema
• Thin landing pages for features
Analyzed by SEODiff AI · 2026-03-01

🔧 Tech Stack

FrameworkWordPress
AI-Readiness Score85/100
Servernginx
CDN
HTTP Status200
Load Time637 ms
Raw HTML Size241.6 KB
Visible Text Size91.8 KB

Performance & Speed

59/100 20 % of Global Score 🟢 High Confidence

⏱️ Time to First Byte

637 ms
Slow — bots may time out or deprioritise

Google considers <200 ms "good". AI crawlers may have even shorter timeouts.

📦 Page Weight

1159
DOM nodes
242 KB
HTML payload
Moderate weight — acceptable for most scenarios

🗄️ Cache & CDN

  • ✓ Cache-Control header → public, max-age=2000000
  • ✗ CDN cache status
  • ✗ CDN detected

🔬 Tracker Tax

0
tracker scripts
0
third-party domains
0.0%
token overhead
Minimal tracker load — clean signal for bots
📐 How the Performance Pillar score is calculated

Perf Pillar = TTFB (35 pts) + Page Weight (25 pts) + Cache/CDN (20 pts) + Tracker Tax (20 pts)

TTFB <200 ms = full marks. DOM >3000 or payload >300 KB incurs heavy penalties. Tracker scripts beyond 5 reduce score.

🏗️

Architecture & Trust

98/100 15 % of Global Score 🟢 High Confidence

🗺️ Sitemap & Robots

  • ✓ Sitemap declared in robots.txt → https://logz.io/sitemap_index.xml
  • ✓ Googlebot allowed
  • ✓ GPTBot allowed
  • ✓ ClaudeBot allowed

🔗 Linking

99
internal links
8
external links
Good internal linking — helps crawlers discover content

🔒 Security & Trust

  • ✓ HSTS header (Strict-Transport-Security)
  • ✓ Content-Security-Policy header
  • ✓ HTTP status 200 OK (got 200)

♿ Accessibility Signals

  • ✓ HTML lang attribute → en-US
  • ✓ Meta viewport for mobile
  • ✓ Single H1 for screen readers
📐 How the Architecture Pillar score is calculated

Arch Pillar = Sitemap & Robots (30 pts) + Linking (25 pts) + Security (25 pts) + Accessibility (20 pts)

Having a valid sitemap, allowing AI bots, HSTS, and a good internal link count are the highest-impact items.

🏅 AI-Verified Trust Badge

Your site scores 67/100. Reach 80+ to unlock the green "AI-Verified" badge. Fix the issues below to improve your score.

AI-Verified badge for logz.io
Pending Audit — score below 80 threshold
<a href="https://seodiff.io/radar/domains/logz.io" rel="noopener"><img src="https://seodiff.io/api/v1/badge?domain=logz.io" alt="AI-Verified by SEODiff" width="280" height="52"></a>

💡 Paste in your site footer, GitHub README, or email signature. Badge updates automatically as your score changes.

� Deep Crawl Analysis 122 pages · Deep-10

Homepage ACRI
67
Single-page score
0
Consistent readability
Δ delta
Site-Wide ACRI
67
Avg across 122 pages · Range 0–80
Topical Cohesion
17%
Topical Drift
TF-IDF cosine similarity
Total Words
136397
Avg Bloat
39.8×
RAG Fractures [?]
117
⚠️
117 RAG-Chunking Fractures Detected

Poorly formatted tables or pricing grids on 117 pages will be split incorrectly during RAG chunking, causing AI models to hallucinate prices and features.

Page Type ACRI Token Bloat Words Status
https://logz.io/blog/docker-swarm-monitoring-logging-using-elk-stack/
Docker Swarm Monitoring and Logging Using the ELK Stack | Logz.io
pricing 80 14.5× 2465 ⚠️ RAG Fracture
https://logz.io/blog/elasticsearch-mapping/
Elasticsearch Mapping: Basics, Updates, and a Few Examples
pricing 80 14.0× 2690 ⚠️ RAG Fracture
https://logz.io/blog/key-considerations-for-a-cross-organizational-observability-strategy/
A Guide to Enterprise Observability Strategy | Logz.io
pricing 80 11.1× 3564 ⚠️ RAG Fracture
https://logz.io/learn/opensearch-guide/
Definitive Guide to OpenSearch for Observability | Logz.io
pricing 80 14.2× 2817 ⚠️ RAG Fracture
https://logz.io/blog/how-to-build-a-pci-dss-dashboard-with-elk-and-wazuh/
How to Build a PCI-DSS Dashboard with ELK and Wazuh | Logz.io
pricing 80 13.4× 2851 ⚠️ RAG Fracture
https://logz.io/blog/elasticsearch-queries/
Elasticsearch Query: A Guide to Query DSL | Logz.io
pricing 80 12.1× 3032 ⚠️ RAG Fracture
https://logz.io/blog/mongodb-monitoring-prometheus-best-practices/
Best Practices for MongoDB Monitoring with Prometheus | Logz.io
pricing 77 19.7× 1828 ⚠️ RAG Fracture
https://logz.io/blog/how-to-monitor-redis-with-prometheus/
How to Monitor Redis with Prometheus | Logz.io
pricing 77 18.8× 1908 ⚠️ RAG Fracture
https://logz.io/blog/aws-reinvent-2016-sessions/
AWS re:Invent 2016: 10 Sessions You Must Not Miss | Logz.io
pricing 77 14.6× 2386 ⚠️ RAG Fracture
https://logz.io/blog/redis-performance-monitoring-elk-stack/
Redis Performance Monitoring with the ELK Stack | LOgz.io
pricing 77 18.2× 1970 ⚠️ RAG Fracture
https://logz.io/blog/logstash-plugins/
A Guide to Logstash Plugins | Logz.io
pricing 77 19.1× 1771 ⚠️ RAG Fracture
https://logz.io/blog/how-to-tackle-spiraling-observability-costs/
How to Tackle Spiraling Observability Costs | Logz.io
pricing 77 19.5× 1854 ⚠️ RAG Fracture
https://logz.io/blog/open-source-observability-adoption-migration-curve/
The Open Source Observability Adoption and Migration Curve | Logz.io
pricing 77 18.9× 1923 ⚠️ RAG Fracture
https://logz.io/blog/influxql-to-promql-grafana-dashboard-converter-open-source/
Auto-convert Grafana dashboards from influxQL to PromQL | Logz.io
pricing 77 17.1× 2247 ⚠️ RAG Fracture
https://logz.io/blog/log-analysis-technical-seo/
How to Use Log Analysis in Technical SEO | Logz.io
pricing 77 18.4× 1901 ⚠️ RAG Fracture
https://logz.io/blog/devops-in-2016/
DevOps in 2016: The Year of Implementation | Logz.io
pricing 72 31.3× 1040 ⚠️ RAG Fracture
https://logz.io/blog/comparing-opensearch-managed-services/
Comparing OpenSearch Managed Services | Logz.io
pricing 72 21.3× 1617 ⚠️ RAG Fracture
https://logz.io/blog/the-devops-pulse-2016-results/
The DevOps Pulse 2016 Results | Logz.io
pricing 72 26.1× 1326 ⚠️ RAG Fracture
https://logz.io/blog/logging-as-a-service-laas/
Logging as a Service (Laas) | Accelerating Log Management
pricing 72 21.0× 1584 ⚠️ RAG Fracture
https://logz.io/blog/how-to-write-end-to-end-tests-like-a-boss/
How to Write End-to-End Software Tests Like a BOSS! | Logz.io
pricing 72 29.5× 1123 ⚠️ RAG Fracture
Showing 20 of 100 pages. Unlock full subpage table →
📂
Health by Sub-Directory
Average ACRI and top issues aggregated by URL path prefix
Path Pages Avg ACRI Ghost % Bloat Top Issue
/blog/ 110 70 0% 37.6× High JS Bloat
/learn/ 2 64 0% 219.7× High JS Bloat
/pricing/ 2 64 0% 34.6× High JS Bloat
/case-studies/ 1 64 0% 68.9× High JS Bloat
/contact/ 1 0 0% 0.0× Low AI Readiness
/faq/ 1 0 0% 0.0× Low AI Readiness
/about/ 1 0 0% 0.0× Low AI Readiness
/integrations/ 1 54 0% 82.3× High JS Bloat
/features/ 1 0 0% 0.0× Low AI Readiness
/docs/ 1 0 0% 0.0× Low AI Readiness
/products/ 1 64 0% 58.6× High JS Bloat
🔗
Outbound External Citations
0 unique external domains cited across 122 pages
youtube.com ×117
github.com ×117
app.logz.io ×117
linkedin.com ×117
twitter.com ×117
docs.logz.io ×117
aws.amazon.com ×117
elastic.co ×12
🔄 Re-Crawl & Update 📡 Track this Domain

Scores update automatically each month. Create a free account for on-demand re-crawls (3/month free).

🔌 API Access

Pull this data programmatically. All sub-page metrics are available via our public API.

curl https://seodiff.io/api/v1/deep10/domain/logz.io

Get your free API key — 100 requests/month included.

🔗 Similar developer Sites

Domains with a similar tech stack, industry, and AI readiness profile to logz.io. Compare side-by-side.

Domain ACRI AI Score Tech Stack Token Bloat Schema
logz.io (this site) 67 80 WordPress 2.6× 2
jitterbit.net 82 88 WordPress 2.7× 1 Compare →
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📊 Semantic Share of Voice

How often would an AI cite logz.io when users ask about topics in this domain's niche? We run entity queries through our 188k-page search index and measure citation probability.

Analyzing citation landscape…

🩹

Remediation Patches

COPY-PASTE

Auto-generated code fixes tailored to logz.io. Copy and paste these into your codebase to improve AI visibility. These patches are mathematically proven to increase extraction accuracy →

Add FAQ Schema
Medium Impact ⏱ 10 min
FAQ schema lets AI models directly extract Q&A pairs. This is the easiest way to get featured in AI responses.
html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is Logz?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Add your answer here — describe what Logz does in 1-2 sentences."
      }
    },
    {
      "@type": "Question",
      "name": "How does Logz work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Explain the key features and how users interact with Logz."
      }
    }
  ]
}
</script>
📈

Projected Impact

ROI EST.

If you apply the patches above, here's the estimated improvement for logz.io:

Current Score
80
Projected Score
83
Improvement
+3 pts
Add FAQ schema +3 pts

*Estimates based on SEODiff's scoring model. Actual results depend on implementation quality.

📋 Data Export

Download scores and metadata for audits, client reports, or CI/CD pipelines. Exports contain computed metrics only (no copyrighted content).

All data is generated automatically and updated with each crawl. JSON exports contain scores and metadata only (no copyrighted content).

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🧭 Self-Diffing (Private Layer)

For owned domains, combine this world snapshot with private drift + regression history.
Template Drift
Track in My Site
Drift → Traffic Impact
In development coming soon
Regression Incidents
Track in My Site
Internal Linking
Deep Audit graph
Semantic Structure
GEO view in Deep Audit
Content Quality
Thin/duplicate tracking

🕒 History

Score over timeAvailable in My Site history
Drift eventsTemplate timeline + incidents
Drift → Revenue AttributionComing soon
Schema/rendering/extractability changesTracked per scan in project history
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