Lumar vs Schema App in 2026: bundled crawl-and-AI platform vs dedicated schema markup engine
Two enterprise Technical SEO tools sold through a demo, built for different jobs. Lumar treats structured data as one checklist item inside a five-part crawl platform. Schema App treats structured data as the entire product.
Lumar
Schema App- Lumar scores 8.3 overall against Schema App's 7.2, driven by a wider feature set (9.2 vs 8.8) and stronger API and integrations (8.8 vs 7.0).
- Schema App generates and validates JSON-LD schema automatically across page templates at scale. Lumar's crawler only detects and validates structured data that already exists; it does not generate new schema.
- Lumar includes native AI brand visibility tracking across ChatGPT, Gemini, and Perplexity as part of its GEO/AEO layer. Schema App has no AI citation tracking of its own; it argues that well-structured entity data is foundational for AI search readiness without measuring the outcome.
- Both tools require a sales demo, publish no pricing, and offer no free tier or self-serve signup.
- Schema App includes a dedicated multi-client workspace built for agencies running schema programs across client portfolios. Lumar's public feature set does not describe an equivalent multi-client management layer.
- Lumar bundles WCAG 2.2 accessibility compliance testing and Core Web Vitals monitoring directly into its crawl workflow, capabilities entirely outside Schema App's scope.
- Lumar confirms API access for data export. Schema App's public materials do not mention an API at all.
Lumar and Schema App both require a sales conversation before you see a price, and both are pitched at enterprise teams, but they rarely compete for the same budget line. Lumar bundles technical SEO crawling, AI brand visibility tracking for GEO and AEO, Core Web Vitals monitoring, and WCAG 2.2 accessibility testing into one contract, with structured data handled as a detection-and-validation check inside the crawl. Schema App does one thing: it generates, validates, and manages JSON-LD schema at scale across thousands of pages or client accounts, then ties that markup to measurable rich result performance. If your structured data need is "make sure the crawler flags broken schema," Lumar already covers it. If your need is "generate and maintain schema across 50,000 product pages without a developer touching each template," Schema App is built specifically for that, and Lumar is not.
The tools at a glance
Lumar
Enterprise website optimization combining technical SEO, AI visibility, and accessibility.
Lumar (formerly DeepCrawl) runs a full-site crawl that checks redirects, canonicals, hreflang, page depth, internal linking, and structured data, then layers AI-powered prioritization on top so the highest-impact issues surface first. Structured data here means detection and validation: the crawler flags broken or missing schema markup as one line item among dozens of technical checks, the same way it flags a broken canonical tag.
What the crawl does not do is generate schema. If a product page is missing Review or Offer markup, Lumar will tell you it is missing, but a developer or a dedicated schema tool still has to write and deploy the fix. Lumar's own AI-generated remediation code feature can help close that gap for some issue types, but it is not built around structured data specifically the way Schema App is.
The AI brand visibility layer is the more distinctive part of Lumar's pitch against a schema-only tool: GEO and AEO tracking across ChatGPT, Gemini, and Perplexity sits alongside the crawl data, plus Core Web Vitals monitoring and WCAG 2.2 accessibility testing. For a team that wants all of that under one enterprise contract and only needs structured data flagged rather than authored, Lumar is the more complete platform. For a team whose actual bottleneck is schema deployment at scale, none of that breadth substitutes for a dedicated schema engine.
| Feature | Enterprise Contact for pricing |
|---|---|
| Technical SEO crawling | Yes |
| AI visibility (GEO/AEO) tracking | Yes, ChatGPT, Gemini, Perplexity |
| Structured data handling | Detects and validates existing schema; does not generate new schema |
| WCAG 2.2 accessibility testing | Yes |
| Core Web Vitals monitoring | Yes |
| API access | Yes, for data export |
Schema App
Enterprise schema markup and structured data management at scale
Schema App exists to solve one problem: manually writing and maintaining JSON-LD across tens of thousands of pages does not scale. You configure schema mappings once against your page templates, and the platform generates and applies markup automatically as new pages publish, then validates the output continuously against Google's structured data guidelines so a CMS update does not silently break rich results.
The platform goes past basic schema types into entity-based markup that connects your content to known entities in the web's knowledge graph, and it closes the loop by tracking which schema types are actually producing rich results and how those placements move click-through rate. That measurement layer is something Lumar's crawl-level detection does not attempt: Schema App tells you not just that your schema is valid, but whether it is working.
For agencies, a dedicated multi-client workspace lets you run schema as a packaged service across five, ten, or fifty client accounts from one login, each with its own configuration and reporting. What Schema App does not do is crawl your site for broken links, redirect chains, or accessibility violations; it is a schema layer, not a general technical SEO platform, and pricing requires a sales call the same as Lumar.
| Feature | Contact for pricing Custom |
|---|---|
| Automated JSON-LD generation at scale | Yes |
| Structured data validation | Yes, continuous, against Google guidelines |
| Rich result / SERP performance tracking | Yes |
| Agency multi-client workspace | Yes |
| Entity-based / linked data markup | Yes |
| API access | Not publicly documented |
Head-to-head feature comparison
| Feature | Lumar | Schema App |
|---|---|---|
| Overall score | 8.3 / 10 | 7.2 / 10 |
| Primary focus | Enterprise technical SEO, AI visibility, speed, and accessibility platform | Enterprise schema markup and structured data management |
| Technical SEO crawling | Yes | No, not a general crawler |
| Automated schema generation | No, detects and validates existing schema only | Yes, at scale across page templates |
| Structured data validation | Yes, as part of the crawl (structured data error detection) | Yes, continuous, against Google guidelines |
| Rich result / SERP performance tracking | Not documented | Yes, ties schema types to rich result and CTR impact |
| AI brand visibility tracking (GEO/AEO) | Yes, ChatGPT, Gemini, Perplexity | No, positions structured data as an AI-search foundation but does not track AI citations |
| Site speed / Core Web Vitals monitoring | Yes | No |
| WCAG accessibility testing | Yes (WCAG 2.2) | No |
| Agency multi-client workspace | Not documented | Yes, dedicated multi-client workspace |
| API access | Yes, for data export | Not publicly documented |
| Free tier | No | No |
| Starting price | Contact for pricing | Contact for pricing |
Considering AI Peekaboo alongside Lumar and Schema App?
Both tools touch AI search from a different angle. Lumar tracks brand mentions across ChatGPT, Gemini, and Perplexity but locks that layer behind an enterprise demo with no public pricing. Schema App argues that entity-based structured data helps AI models cite your content accurately, but it does not measure whether that citation is actually happening. Neither gives you a self-serve way to see where you show up in AI answers. AI Peekaboo ships a read and write API on every plan from $50 per month, tracks named AI surfaces directly, and includes white-label delivery without a sales call, which makes it the more direct fit if AI visibility measurement, not schema authoring or crawl breadth, is the actual requirement.
Read the AI Peekaboo review →Which should you choose?
Schema AppLumarSchema AppLumarLumarSchema AppLumarThese two barely compete for the same evaluation. Lumar's structured data handling is a validation step buried inside a five-part crawl platform; useful for catching broken schema, useless for authoring new markup at scale. Schema App does the opposite: it has no crawler, no accessibility testing, and no AI visibility tracking, but it will generate and maintain JSON-LD across a catalogue Lumar would only ever flag as broken. The decision is really about which problem you actually have, not which platform scores higher (Lumar does, at 8.3 vs 7.2, mostly on breadth), because the two tools solve different problems inside the same category.
Bottom line
Book the Schema App demo if your structured data need is generation and validation at scale, across thousands of product, article, or review pages, or if you are packaging schema as a service across client accounts. Book the Lumar demo if you want technical SEO crawling, AI answer-engine visibility, and WCAG 2.2 accessibility testing under one contract and are fine with schema being flagged rather than authored. Large enterprise sites with genuinely complex schema requirements will often end up running both, since Lumar's crawl will catch the broken markup that Schema App's automation did not anticipate.
Frequently asked questions
Is Schema App better than Lumar for structured data at scale in 2026?
Yes, if structured data is the actual problem you are solving. Schema App generates and validates JSON-LD automatically across page templates and ties schema types to measurable rich result performance, none of which Lumar attempts. Lumar's crawler only detects and validates schema that already exists, so for authoring and maintaining markup across thousands of pages, Schema App is the deeper tool.
Does Lumar generate schema markup, or only check it?
Lumar only checks it. The crawl engine flags missing or broken structured data as one item among many technical checks, alongside redirects, canonicals, and hreflang, but it does not generate new JSON-LD or apply schema across templates the way Schema App does.
Which tool tracks AI search visibility, Lumar or Schema App?
Lumar does. It includes a native GEO and AEO tracking layer covering ChatGPT, Gemini, and Perplexity as part of its platform. Schema App does not track AI citations at all; it positions structured data as a foundation for AI search readiness but does not measure whether your brand is actually being cited.
Can a small agency afford Schema App or Lumar?
Neither is built for a small budget. Both require a sales conversation, publish no pricing, and offer no free tier or self-serve trial, so an agency managing five or six small clients should expect enterprise-level cost from either one before evaluating fit.
Does Schema App replace the need for a technical SEO crawler like Lumar?
No. Schema App has no general crawler, so it will not catch broken links, redirect chains, or accessibility violations the way Lumar does. Schema App is a structured data layer specifically, and most enterprise sites running it still need a separate technical SEO crawl to cover everything outside schema.
Which tool has multi-client management for agencies, Schema App or Lumar?
Schema App has a dedicated multi-client workspace built for agencies running schema programs across several client accounts from one login. Lumar's public feature set does not describe an equivalent multi-client management layer, even though it lists agencies as a target user for its broader crawling and AI visibility platform.