Oncrawl vs Deepcrawl (Lumar): Which one is the best ?

Search visibility is constrained by crawlability, indexation, and site health. Large-scale websites face compounding inefficiencies: orphan pages, crawl budget waste, rendering issues, and log blind spots. Enterprise-grade crawlers solve this by simulating search engine behavior and exposing structural inefficiencies with measurable impact.

Modern platforms extend beyond crawling. They integrate log analysis, data science layers, and automation to answer three core questions:

• What can search engines access?

• What do they actually access?

• Where is crawl efficiency being lost?

Two tools dominate this segment: Oncrawl and Lumar (formerly Deepcrawl). Both operate at enterprise scale but differ in architecture, depth, and analytical philosophy. Let's take an overview of these tools and understand how they are believed to be one of the best in technical SEO consulting.

Quick Comparison Between Oncrawl vs Deepcrawl (Lumar)

Choosing between Oncrawl and Lumar (formerly Deepcrawl) often comes down to whether you prioritize data science and log analysis (Oncrawl) or enterprise health monitoring and automation (Lumar).

Both are heavyweight, cloud-based crawlers designed for large-scale technical SEO, but they excel in different areas.

What is OnCrawl?

OnCrawl is a technical SEO software that aims at crawling and analyzing logs. Unlike traditional crawlers, which only show how a site appears to be seen by crawlers, OnCrawl tries to reveal how search engines actually interact with your site.

Its main selling point is reconciling data, specifically the combination of crawl data, server logs, and other information sources such as analytics and rankings, to understand search engine behavior. Consequently, OnCrawl is widely adopted in revenue-generating situations.

Features: What Does it Do?

Oncrawl is made up of the following three data layers:

Crawl Data Engine

Performs simulated search engine crawling to assess site architecture, interlinking, status codes, and indexability. The data layer reveals SEO technical problems like orphan pages, redirection loops, and duplicate content.

Log File Analysis Engine

Analyzes raw logs from web servers to detect actual bot activity (Googlebot, Bingbot). It tells us how often bots visit certain URLs and what response codes they receive from those visits.

Data Correlation Layer

Connects data from the previous two layers into a single view along with other data (performance). This makes it possible to link technical metrics with effects (indexation, traffic).

Other features include:

• URL segmentation by template/directory

• Crawl budget allocation

• Detection of indexation gaps

• Mapping SEO performance impacts

Pricing

Oncrawl does not publish fixed pricing publicly. It follows an enterprise-only model where cost is determined by usage and infrastructure scale rather than predefined plans.

Typical enterprise contracts generally fall in the $20,000–$50,000+ per year range, scaling upward for high-volume websites with continuous log ingestion and multi-domain setups.

Costs are typically based on:

• Number of URLs crawled per month

• Volume of log file data processed

• Data retention and historical analysis depth

• Number of domains and projects managed.

• Level of integrations (BI tools, APIs, analytics stacks)

• Frequency of crawling and reporting cycles

Best For

• SEO specialists responsible for managing large-scale enterprises with complicated website structures (with more than 100,000 URLs), where crawl efficiency directly affects the bottom line

• Those who have to make data-driven SEO decisions based on log files and not assumptions about how crawlers work

• Companies are facing the challenge of indexing and crawl budget management. Those involved in sophisticated technical SEO consulting, combining SEO, data engineering, and analytics

• Businesses looking to test their SEO assumptions based on the actual crawl of Googlebot

Use Cases

Lumar is commonly deployed for:

• Parameter and faceted navigation control

Detecting crawl traps caused by filter URLs, sorting parameters, and duplicate URL generation.

• Indexation gap analysis

Comparing crawled, indexed, and discovered URLs to detect pages that are ignored or underrepresented in search.

• Log-based SEO diagnostics

Using server logs to detect real bot behavior, crawl frequency, and response patterns across different site sections.

• Migration and restructuring validation

Monitoring how site changes affect crawl distribution, indexation stability, and bot behavior post-deployment.

Alternatives to Oncrawl

• Screaming Frog (limited log integration)

• Botify (similar data depth, higher cost)

• JetOctopus (faster, lighter log analysis)

What is Deepcrawl (Now Lumar)

The company was founded in 2010 as Deepcrawl and focused on creating a web crawling tool for massive-scale sites. In 2022, it changed its name to Lumar and added more services to its range beyond crawling, including SEO optimization, accessibility, and site governance.

Its system is suitable for use in massive-scale environments where the software handles hundreds of URLs in one second and works within highly scaled-site infrastructures. Lumar's platform is SOC 2 Type 2 accredited and operates on serverless architecture.

Features: What Does It Do?

Lumar is structured into four core modules:

• Lumar Analyze

  Core crawling and audit engine. It identifies technical SEO issues such as broken links, redirect chains, duplicate content, indexation problems, and site speed inefficiencies. It also supports large-scale exports and integrations with tools like Google Data Studio, Tableau, and Power BI for reporting and analysis.

• Lumar Monitor

  Continuous monitoring layer that tracks changes in technical SEO health across domains. It detects regressions in crawlability, indexation signals, and on-page structure, and triggers alerts when anomalies appear.

• Lumar Protect

  Governance and pre-deployment validation system. It integrates with CI/CD pipelines to catch SEO issues before they go live. This reduces risk during site releases and platform migrations.

• Lumar Impact

  
  A reporting and visualization layer that translates technical findings into structured insights for stakeholders. It benchmarks performance changes and simplifies communication between SEO, product, and engineering teams.

Lumar Alco connects with enterprise systems such as Jira, Trello, Asana, BigQuery, and GraphQL APIs. This allows technical SEO data to flow directly into development workflows rather than remaining soiled in SEO tools. It also extends into web accessibility monitoring and GEO/AEO.

Pricing

Lumar does not publish transparent pricing on its website. Based on third-party data and publicly available information, the entry-level Starter plan begins at approximately $89/month (billed annually), covering up to 100,000 URLs and five projects. However, this tier is limited in scope for most enterprise use cases.

According to Vendr, Lumar charges the large organizations a typical annual cost of $15,000–$30,000+ for a comparable team size, depending on usage limits. But they mainly operate on flexible pricing with packaging options

Best For

Lumar is the best choice for:

• Large enterprise SEO teams with complicated site structures and multiple stakeholders to deal with

• Technical SEO teams requiring a structured audit along with quality assurance (QA) and continuous integration and delivery (CI/CD)

• Companies looking to integrate technical SEO with web accessibility guidelines and governance

Teams that prefer clear dashboards and reports for their non-technical colleagues

Use Cases

Lumar is commonly deployed for:

• Site migration planning, post-migration testing to prevent any drop in indexation or traffic

• Technical crawls scheduled for larger e-commerce, media, and corporate sites

• Analysis of crawlability, indexation, and page structure problems

• Website accessibility tests in accordance with the WCAG guidelines, along with SEO testing

Alternatives to Lumar

• Sitebulb (visual insights, desktop-first)

• ScreamingFrog

• Ahrefs (limited technical depth)

• SEMrush (broad marketing suite)

Oncrawl vs Lumar: Functional Comparison

While they overlap in core functionality (crawling millions of pages), they have evolved to serve different "hero" use cases: Oncrawl is a data science platform for SEO, while Lumar is a website intelligence and risk management platform.

Strategic Positioning

• Oncrawl operates as a diagnostic engine. It answers why issues exist

• Lumar operates as a monitoring system. It answers what issues exist.

How These Tools Fit into a Technical SEO Workflow

A structured workflow typically follows:

1. Crawl the website - you can use crawlers like Luar to simulate search engine access and map the full URL structure. This helps you establish the baseline visibility across templates, internal linking, and indexable pages.
   
   

2. Identify structural issues - Audit outputs to isolate issues such as duplicate content, broken links, redirect chains, and faceted URL sprawl. This stage surfaces common tech seo issues that affect crawlability and indexation at scale.
   
   

3. Validate with real bot behavior - Overlay crawl data with log analysis from Oncrawl to confirm how search engine bots actually interact with the site. This removes assumptions and highlights gaps between intended structure and real crawl patterns.
   
   

4. Prioritize fixes based on impact - Correlate technical issues with crawl frequency, indexation, and traffic data. Focus on fixes that improve crawl efficiency and index coverage.
   
   

5. Monitor improvements - Continuously track crawl patterns, indexation changes, and site health. Tools like Lumar help maintain consistency and detect regressions after implementation.

Oncrawl dominates steps 3 and 4.
Lumar dominates steps 1, 2, and 5.

This aligns with the broader framework of how to do a technical seo audit, where discovery, validation, and prioritization must be separated.

Statistical Context: Why These Tools Matter

• Google processes billions of pages daily, but allocates crawl budget selectively.

• Studies show that over 30% of large websites contain orphan pages

• Crawl inefficiencies can reduce indexation by 20–40% in large domains

• Log analysis often reveals that less than 50% of pages are regularly crawled

Without enterprise crawlers, these inefficiencies remain invisible.

Conclusion

Oncrawl and Lumar address the same problem from different angles.

• Oncrawl is built for precision. It focuses on data correlation, log intelligence, and ROI-driven prioritization.

• Lumar is built for scale and clarity. It emphasizes accessibility, monitoring, and usability across teams.

The selection depends on operational maturity:

• Choose Oncrawl when decision-making requires data modeling and crawl behavior analysis

• Choose Lumar when consistency, reporting, and cross-team visibility matter more than analytical depth

In reality, both platforms are at different levels of technical SEO development, where one concentrates on diagnosing inefficiencies, while the second makes sure that the systems continue working efficiently.

What is always guaranteed? Better crawl efficiency, better indexation, and a website design that matches what search engines expect from sites.