The traditional tools for monitoring how search engines crawl your website—Google Search Console’s Crawl Stats, log file analyzers, even enterprise crawl platforms—all have significant limitations. For large sites that need to track both traditional search engines and the growing array of AI crawlers, there’s a better approach: partnering with your site reliability engineering team.
The Crawl, Render, Index Pipeline
The foundational principle of technical SEO hasn’t changed: if anything breaks down in the crawl-render-index process, your content will never rank. Search engines must be able to find your pages, render the content, and add it to their index. Even the best content fails if this pipeline breaks.
What has changed is how AI crawlers interact with this pipeline. Three critical differences: First, many sites are blocking AI crawlers by default in their robots.txt—if you want visibility in AI platforms, you need to fix that. Second, AI crawlers generally do not render JavaScript. If you’ve gotten comfortable relying on Google’s ability to execute JavaScript, you need to rethink that approach for AI visibility. Third, AI systems don’t maintain a traditional index—they have “knowledge straight in mind,” which fundamentally changes how we think about indexation.
Why Traditional Crawl Monitoring Falls Short
Google Search Console Crawl Stats
Google’s Crawl Stats report, available since 2020, shows crawl activity for roughly the past 90 days. But the limitations are significant: it’s only available for domain properties, only segmentable by subdomains (not subfolders), provides only example URLs rather than comprehensive data, and of course only tracks Googlebot.
The bigger question is accuracy. Comparing Crawl Stats data against first-party Datadog logs for a three-month period revealed a correlation—both showed spikes at similar times—but the Datadog logs consistently showed approximately 50,000 fewer requests. The consensus among technical SEOs: Crawl Stats is useful for trends and helpful for small domains, but becomes less accurate as domain size increases.
Log File Analysis
Log files offer the most accurate crawl data, but accessing them is notoriously difficult. Common challenges include: engineering teams not storing data in the formats SEOs need, data retention periods too short for meaningful analysis, and file sizes that crash local machines. Communicating log file requirements to engineers who aren’t familiar with SEO tooling documentation adds another layer of friction.
Enterprise Crawl Platforms
Tools like Botify and Lumar (formerly DeepCrawl) offer log file integration with real advantages: your computer doesn’t crash, you get third-party support for engineering communication, and you can visualize crawl data alongside technical crawl information. However, you still face the fundamental challenge of ensuring engineering stores the right data. Ingestion pipeline failures can make it appear that sections of your site aren’t being crawled when the problem is actually with data flow. You’re also limited to the tool’s existing visualizations and product roadmap. These platforms excel at merging crawl and log data for audits but are less ideal for ongoing monitoring.
The SRE Advantage
Site reliability engineers are the unsung heroes of crawl monitoring. Their core job—keeping the website up, secure, and fast while protecting against bad bots—means they already care deeply about crawl activity. They use logs constantly and maintain advanced tools like Datadog, Sumo Logic, or Splunk. When data issues arise, they fix them quickly because it’s their responsibility.
The key insight: SRE teams are the true crawl experts. Technical SEOs understand Googlebot and increasingly AI bots, but SRE engineers operate at the god-tier level for crawl monitoring broadly. Approaching them as the experts—not asking them to implement your specifications—creates a collaborative dynamic that produces better results.
Building Your SRE Partnership
Step One: Meet and Learn
Start by meeting with your SRE team to understand what resources search engines and AI bots are crawling. Frame the conversation around your desire to better understand how new AI user agents interact with your site and how traditional crawlers access specific areas. Ask what log tools they use, then study the documentation for those platforms—Datadog, Sumo Logic, and Splunk all have excellent docs.
Step Two: Review and Define
Examine existing dashboards the SRE team has built for other monitoring purposes. Note the formats and let them spark ideas for your needs. Then clearly define what information you need: which bots to track, what time ranges matter, and what segmentation (subfolder-level data is often critical for SEO but unavailable in GSC).
Step Three: Collaborate on Dashboards
Create a detailed ticket outlining your requirements—crawl stats for multiple bots within Datadog, geographic data for Googlebot crawls, subfolder segmentation—and meet with SRE to review it. They’ll create a draft, you’ll provide feedback, and through iteration you’ll end up with something powerful. Crucially, collaboration helps SRE find more cost-effective ways to store the data you need.
Dashboard Components That Matter
A comprehensive crawl monitoring dashboard should include: crawls by path (to see which folders get attention), crawls by path and HTTP status code (to spot problems quickly), top 100 URLs for any given status code (for rapid investigation), robots.txt crawl activity, and separate tracking for AI user agents.
User agents worth monitoring include Googlebot, Bingbot, and Yandex for traditional search, plus ChatGPT-User, PerplexityBot, and Claude-Web for AI platforms. Don’t forget CCBot for Common Crawl, since that corpus trains many AI models. The ability to click into any visualization and see the actual log entries—including IP address verification to identify spoofed bots—transforms troubleshooting.
Automated Alerts
Once dashboards exist, set up automated monitoring alerts—but only for critical items to avoid alert fatigue. Useful triggers include: Googlebot receiving more than 50% 500-level errors in a five-minute period, or Google getting a 404 when requesting robots.txt. Start conservatively and adjust thresholds based on what generates actionable versus noisy alerts.
Real-World Applications
The dashboard immediately revealed that a third-party security tool was periodically blocking Googlebot. Every six weeks to three months, Googlebot would start receiving 406 errors because the vendor wasn’t keeping up with Google’s IP address range updates. The dashboard data gave engineers the evidence they needed to cancel that contract.
The AI user agent tracking revealed that most AI bots were being blocked by default—an easy fix once identified. After a taxonomy overhaul project on category pages, the dashboard showed Google picking up changes quickly. More exciting: Perplexity dramatically increased its crawls to the strains subfolder afterward, with Datadog’s anomaly detection automatically highlighting the spike.
For businesses with geographic complexity—like cannabis companies operating in a federally illegal U.S. market where everything must happen within state lines—knowing where Googlebot crawls from matters significantly. When Google crawls from a state where cannabis is illegal, internal linking changes and can affect how the site appears. Dashboard geolocation data proved far more useful than manually inspecting the homepage weekly.
LLM.txt: The Verdict
After months of buzz about LLM.txt files for AI crawler guidance, the data tells a clear story: don’t waste your time. Despite creating an LLM.txt file and linking to it from robots.txt, no AI crawlers accessed it for months. A monitoring alert was set up because manual checking became tedious. On October 21, 2025, OpenAI finally crawled it. The impact? None whatsoever.
The Partnership Payoff
Beyond better data, the real reward of SRE partnership is a cross-functional relationship. Working together on dashboards creates mutual understanding—SRE learns what matters for SEO, SEO understands SRE’s priorities. This leads to collaboration on vendor decisions, infrastructure projects, and environment rebuilds. When SRE asks “what does SEO need from the acceptance environment?” you’ve achieved something far more valuable than any dashboard.
Key Takeaways
Log files remain the best source of crawl data, but getting them is hard—especially for AI crawler data. Google Search Console Crawl Stats has significant accuracy limitations for large sites and tracks only Googlebot. Enterprise crawl platforms excel at audits but struggle with ongoing monitoring due to ingestion issues and visualization constraints.
The solution is partnering with your site reliability engineering team. They have advanced tools already running, maintain them properly, and fix data issues quickly. Approach them as the crawl experts they are, collaborate on dashboard creation, implement alerts for critical issues only, and build a relationship that pays dividends far beyond crawl monitoring.
