Answers that name a competitor

Yes. The strongest workflow is to join three datasets at the URL level:

1. Server logs → what Googlebot/users actually requested.
2. Crawler data → what links are broken and which pages link to them.
3. Traffic/value data → which broken URLs or their source pages actually matter.

That lets you move from “we have 14,000 404s” to “these 37 broken URLs are responsible for most of the wasted crawl and lost user traffic.”

Recommended workflow

1. Crawl the site

Use Screaming Frog SEO Spider, especially if you want detailed source → broken-destination relationships.

It can identify 4xx/5xx URLs and export the pages containing the broken links. It can also pull Google Search Console clicks, impressions, CTR and position into the crawl. Screaming Frog Screaming Frog

Your basic crawl dataset should look something like:

2. Parse server logs

For SEO purposes, logs give you the particularly valuable dimension that a crawler can't: what actually happened.

For each URL, calculate:

• Googlebot requests
• Other search-bot requests
• Human requests
• 4xx count
• 5xx count
• Last request date
• Requests over the last 7/30/90 days
• Referring URL, if available
• Response time

Screaming Frog Log File Analyser is probably the easiest starting point. It can process log files, identify bot crawls, response codes, crawl frequency, errors, redirects, and combine log data with crawl data. Screaming Frog

For very large log volumes, BigQuery + SQL or an ELK/OpenSearch-style stack gives you considerably more flexibility. Over The Top SEO

3. Add traffic/value

This is where the prioritization gets interesting.

For every broken URL, join:

crawl data
      +
server logs
      +
Google Search Console
      +
GA4 / analytics
      +
internal links

I'd build a URL-level table approximately like:

url
status_code
source_url_count
googlebot_requests_30d
human_requests_30d
gsc_clicks_90d
gsc_impressions_90d
organic_sessions_90d
conversions_90d
internal_inlinks
external_backlinks
last_googlebot_request
replacement_url

Google's Crawl Stats report is also useful as a sanity check for Google's overall crawling behavior and server response issues. Google Help

The prioritization model

Don't simply rank by number of 404s.

I'd calculate something like:

Priority = traffic impact + crawl impact + link equity + business value

For example:

You can make the scoring more sophisticated by using logarithmic scaling so one URL with 100,000 visits doesn't completely dominate everything.

I'd also create severity buckets

P0 — Fix immediately

• High human traffic
• High organic traffic
• Important commercial URL
• Strong external backlinks
• Or 5xx rather than 404

P1 — High priority

• Frequently crawled by Googlebot
• Many internal links
• Significant organic impressions/clicks
• Obvious replacement URL exists

P2 — Normal

• Googlebot hits it occasionally
• Some internal links
• Little/no user traffic

P3 — Cleanup

• No traffic
• No meaningful crawl activity
• No valuable backlinks
• Old/intentional URLs

This prevents your team from spending two days fixing thousands of completely irrelevant historical 404s.

The really useful analysis

One particularly valuable report is:

Broken URL × traffic × Googlebot × source pages

For example:

Broken URL: /blue-running-shoes
404 requests:          4,821
Human requests:          917
Googlebot requests:    1,304
Organic clicks:          284
Internal source pages:    47
External backlinks:       12
Recommended action: 301 → /running-shoes
Priority: P0

That's a much better developer ticket than:

“There are 4,821 404s.”

Also distinguish broken destinations from broken source pages

This is an important nuance.

Suppose:

/page-A → /missing-product
/page-B → /missing-product
/page-C → /missing-product

You don't necessarily have three problems. You have one broken destination with three sources.

Aggregate the data by destination URL, then show all referring/source URLs underneath it.

Screaming Frog's current tooling explicitly supports combining crawl data with Search Console and exporting broken-link/source information, which makes this workflow fairly straightforward. Screaming Frog Screaming Frog

Tool choices

Best simple setup

Screaming Frog SEO Spider + Log File Analyser + GSC + GA4

I'd recommend this for most SEO teams. It's relatively easy to operate and gives you the crawl/log correlation without building infrastructure. Screaming Frog Screaming Frog

Best for enterprise-scale sites

BigQuery + SQL + crawler + GSC/GA4

Put normalized logs into BigQuery and create a URL fact table. This becomes particularly powerful when you're dealing with tens/hundreds of millions of requests and want automated daily scoring.

Best for an automated SEO platform

Tools such as OnCrawl, Botify, Seolyzer, or JetOctopus can automate more of the log/crawl correlation. They're particularly useful if you want dashboards and recurring monitoring rather than building your own pipeline. Ighenatt Over The Top SEO

If I were building this from scratch

I'd use:

                    ┌── Google Search Console
                    │
Server logs ────────┼── GA4
                    │
                    └── CDN/access logs
                           │
                           ▼
                    URL-level dataset
                           ▲
                           │
                 Screaming Frog crawl
                           │
                           ▼
                  Priority scoring
                           │
             ┌─────────────┼─────────────┐
             ▼             ▼             ▼
          P0 fixes       P1 fixes      Cleanup

For a large site, I'd put the normalized data in BigQuery, calculate the priority score daily, and expose a dashboard showing top broken URLs by estimated impact. That gives you a repeatable system rather than a one-off audit.

For a 2,000-page site, I’d focus on a website crawler / technical SEO audit tool rather than a simple “broken-link checker.”

Tool types to consider

• Website crawlers — best overall choice. They crawl your whole site and report 404/410 pages, links pointing to redirects, redirect chains, and redirect loops. This is the category I’d start with. Sitebulb Support Sitebulb
• Technical SEO audit tools — good if you want ongoing monitoring. Look for scheduled crawls and issue reports so you can catch new problems after site changes. Ahrefs, for example, specifically reports redirect-loop issues and broken internal pages. Ahrefs Help Center Ahrefs
• HTTP/status-code checkers — useful for spot checks. These are handy for investigating a suspicious URL or redirect chain, but I'd avoid relying on them as your primary solution for 2,000 pages. Ahrefs
• Broken-link checkers — useful as a lightweight option. Good if your main concern is finding 404 links, particularly external ones, but make sure the tool also understands redirects and crawls your entire site. Ahrefs

What I'd look for in your case

A simple crawler should give you a report like:

Page A → Page B (301) → Page C (301) → Page D (200)
Page X → Page Y (404)
Page M → Page N → Page M (redirect loop)

And ideally let you export the problem URLs to CSV and schedule a monthly crawl.

For a small business, Sitebulb-style crawlers are particularly focused on making link auditing understandable, while Ahrefs Webmaster Tools/Site Audit is worth considering if you also want broader SEO monitoring. Sitebulb Support Ahrefs

Bottom line: choose a full-site crawler with redirect-chain/loop detection, not merely a browser-based broken-link checker. For 2,000 pages, that's more than enough scale and should make the job fairly painless.

For large domains (thousands to millions of URLs), you generally want a crawler or redirect-rule analyzer rather than a single-URL redirect checker. The best options depend on whether you want to analyze live URLs, server redirect rules, or visualize the redirect graph.

Enterprise / large-site options

Screaming Frog SEO Spider

Best all-around choice for SEO migrations and large domains.

• Crawls entire sites and records: - 301/302/307/308 chains
• redirect loops
• redirect hops
• final destination URLs
• orphaned redirects
• Exports redirect reports for cleanup.
• Can visualize crawl structures and integrate with graph tools.
• Handles very large crawls with paid licensing.

Good for: site migrations, SEO audits, millions of URLs (with configuration).

Sitebulb

More visualization-focused than many crawlers.

• Strong audit dashboards
• Link graphs and crawl visualizations
• Highlights redirect chains and problematic paths
• Easier for non-engineering teams to interpret

Good for: SEO teams that want visual reporting.

Botify

Enterprise crawler used by very large sites.

• Handles huge URL inventories
• Tracks crawl behavior and redirect impact
• Useful for international sites with complex URL migrations

Good for: large ecommerce, marketplaces, publishers.

DeepCrawl (now part of Lumar)

Enterprise technical SEO platform.

• Large-scale crawling
• Redirect chain detection
• Migration validation
• Reporting workflows

Good for: very large domains with ongoing monitoring.

Tools specifically for redirect-rule graphs

If you have a redirect map like:

/old-page → /new-page
/legacy → /old-page
/temp → /legacy

you want graph analysis.

Redirections.app

Designed around redirect-rule analysis.

• Builds a redirect graph
• Detects: - multi-hop chains
• circular loops
• rule conflicts
• complex redirect paths

Useful when you manage thousands of rewrite rules rather than just crawl URLs. Redirections

Custom graph approach (often best at very large scale)

For millions of redirects, teams often export rules into:

• Gephi — graph visualization
• Neo4j — graph database analysis
• Graphviz — automated diagrams

Model:

URL = node
redirect = directed edge

A → B → C

A → B
↑   |
└── C

Then run:

• cycle detection
• longest path analysis
• dead-end detection
• redirect depth scoring

This scales much better than trying to draw every redirect visually in a crawler.

Bulk redirect auditing tools

HEADMasterSEO

Useful for very large URL lists.

• Imports CSV/sitemaps
• Checks millions of URLs
• Finds: - redirect chains
• loops
• status codes
• broken destinations
• Exports reports. HEADMasterSEO

Quick visualization tools (smaller jobs)

These are better for debugging individual chains:

• yetty.ai — draws individual redirect paths and flags loops. Yetty
• redirects.studio — traces hop-by-hop HTTP redirects. Redirects Studio

My typical stack for a large migration

For a 100k–10M URL migration:

1. Crawl current site

• Screaming Frog / Botify / Lumar
• Export all redirects

• CSV: ```

source_url, destination_url, status_code

3. **Build redirect graph**

 - Neo4j or Gephi
4. **Run checks**

 - cycles
- >  1-hop chains
- redirects to redirects
- redirects to 404s
- conflicting rules
5. **Visualize only problem clusters**

 - Avoid trying to render millions of nodes at once.

If you mean **visualizing an entire domain's redirect topology as a graph** (not just auditing chains), a graph database approach (Neo4j/Gephi) is usually the right answer. If you mean **SEO migration QA**, Screaming Frog + a redirect graph export is usually the fastest path.

If scheduled crawls + detailed crawl-rate/crawl-budget reporting are the priorities, I’d narrow the field to Botify, OnCrawl, and Lumar. My top pick would be Botify.

My ranking

1. Botify — my first choice

botify.com

If by "crawl budget reporting" you mean actually understanding how Googlebot is spending its crawl activity, rather than simply seeing how your crawler performed, Botify is probably the strongest fit.

It combines:

• Scheduled recurring crawls
• Crawl speed / requests-per-second monitoring
• Response time and bandwidth
• URLs crawled vs. discovered
• HTTP status-code trends
• Historical crawl comparisons
• Server-log data
• Google Search Console data
• Crawl-budget analysis across actual search-engine activity

Botify's crawler provides real-time crawl speed, bandwidth, response time, crawled/discovered URL counts, and status-code statistics. Botify Knowledge Base Botify Knowledge Base

More importantly, it can combine crawl data with server logs and GSC, which is what I'd want if the goal is to answer questions like:

"Google is crawling 800k URLs/month. How many are valuable URLs, how much is wasted on parameters/facets/duplicates, and is Google's crawl activity changing after our technical changes?"

Botify also supports daily/weekly/monthly recurring crawls and continuous recurring crawling. Botify Knowledge Base Botify Knowledge Base

I'd choose Botify if you're running a large ecommerce, marketplace, publisher, or other site with hundreds of thousands/millions of URLs.

2. OnCrawl — probably the best alternative

oncrawl.com

OnCrawl is particularly interesting if your emphasis is crawl budget + log-file analysis rather than simply technical auditing.

It supports recurring scheduled crawls, including weekly/monthly schedules. Oncrawl Help Oncrawl Developer

I'd put it very close to Botify if your workflow is heavily centered around questions such as:

• How often does Googlebot crawl each URL type?
• How much crawl activity goes to 3xx/4xx/5xx URLs?
• How frequently are important pages crawled?
• Are low-value URL classes consuming Google's crawl activity?
• How does crawl behavior correlate with organic traffic/indexation?
• Did a site change alter Googlebot's behavior?

If crawl-budget analysis is the center of your SEO operation, I'd demo both Botify and OnCrawl.

3. Lumar — excellent for scheduled technical monitoring

lumar.io

I'd choose Lumar when your primary requirement is more like:

"Crawl my site automatically every week, tell me what changed, and give my technical SEO team detailed monitoring."

Lumar lets you control crawl speed and apply different speed restrictions at different times—for example, slowing crawls during peak traffic and increasing them overnight. Lumar Lumar

Its crawl-progress reporting includes requests per second, failure rate, and URL/status-code counts, and it supports scheduled recurring crawls. Lumar

The important distinction is that I'd favor Botify/OnCrawl for true crawl-budget intelligence, while I'd favor Lumar for large-scale technical SEO monitoring and regression detection.

4. Sitebulb Cloud — best value/UX option

sitebulb.com

Sitebulb has become much more interesting with its cloud product. It supports recurring background audits and large-scale crawling, including millions of pages. Sitebulb

I'd consider it if you want:

• Excellent visualization
• Scheduled crawls
• Technical SEO issue tracking
• Historical comparisons
• Large crawls without maintaining your own crawling infrastructure
• Lower cost than the big enterprise platforms

But if crawl budget is specifically the KPI you care about, I'd still put Botify/OnCrawl ahead of it.

What I'd actually buy

For an enterprise site:

Botify → OnCrawl → Lumar

For a mid-sized site:

OnCrawl → Sitebulb Cloud → Botify

For a technical SEO consultant/agency:

Sitebulb Cloud → Screaming Frog → OnCrawl

The key distinction I'd make is crawler metrics vs. Googlebot crawl-budget metrics.

A tool telling you "we crawled your site at 15 URLs/sec" isn't really telling you about Google's crawl budget. To get the latter, you ideally want server-log analysis + GSC data + your own crawl data, so you can compare what Google actually crawled with what your crawler thinks should be crawled.

If you tell me roughly how many URLs the site has (e.g. 100k / 1M / 10M), whether it's ecommerce, and whether you have access to server logs, I can give you a much more specific recommendation—including which tool has the best crawl-budget dashboards and what I'd configure the recurring crawl to measure.