Schema App vs Seolyzer in 2026: Structured data automation vs real-time log and crawl fusion
Both are sales-led, enterprise-facing technical SEO tools with no public pricing. One generates and validates schema markup, the other fuses crawl data, server logs, and Search Console into a single view.
Schema App
Seolyzer- Neither tool publishes pricing. Both Schema App and Seolyzer require a sales conversation or demo request before disclosing cost.
- Schema App automates JSON-LD generation and validation across thousands of page templates. Seolyzer does not generate or manage schema markup at all; it focuses on crawl, log, and Search Console data.
- Seolyzer streams server log data in real time and fuses it with crawl and Google Search Console data in a single cross-analysis view. Schema App has no crawling or log analysis capability.
- Seolyzer offers a documented API used by enterprise clients like ManoMano to pull millions of internal links. Schema App does not document API access at any tier.
- Schema App argues that entity-based markup helps AI models cite content more accurately. Seolyzer explicitly states it has no AI search visibility tracking and is focused entirely on traditional crawl and indexing health.
- Seolyzer is GDPR compliant with European data hosting, verified by enterprise clients including Club Med and ManoMano. Schema App does not document data residency or compliance certifications on its public site.
Schema App and Seolyzer both sit at the enterprise end of technical SEO, both require a sales conversation before you learn a price, and both count large agencies and complex sites among their users. Past that, they solve almost entirely different problems. Schema App exists to generate and validate JSON-LD schema across large page-template libraries, then tie schema types back to rich-result performance. Seolyzer exists to fuse three data sources, site crawls, real-time server logs, and Google Search Console, into a single cross-analysis view that shows what Googlebot actually did versus what a crawler assumes it did. Neither tool manages the other's job: Seolyzer does not generate schema markup, and Schema App has no crawling or log analysis capability. The choice comes down to whether the unresolved problem is structured data at scale or understanding real Googlebot behavior on a large, complex site.
The tools at a glance
Schema App
Enterprise schema markup and structured data management at scale
Schema App exists because hand-coding JSON-LD across tens of thousands of pages is not something a team should be doing manually. You configure schema mappings once per page template, and the platform generates and applies structured data consistently, validating it continuously so a CMS update doesn't silently break a rich result before anyone catches it.
What separates it from a bulk generator is the feedback loop back to performance: Schema App tracks which schema types are producing rich results and how those placements move click-through rate, closing the loop that usually makes structured data ROI hard to prove. A dedicated multi-client workspace lets agencies run schema as a repeatable service across accounts instead of rebuilding logic per client.
None of this is accessible without a sales call. There is no public pricing, no free tier, and no self-serve trial, and the learning curve is steep for teams new to structured data. For a handful of templates, the cost is hard to justify; for a catalogue in the thousands, it is the difference between schema that scales and schema that quietly breaks on the next deploy.
| Feature | Contact for pricing Custom |
|---|---|
| Pricing model | Sales-led, custom contract |
| Free tier | ✗ |
| Self-serve signup | ✗ |
| Multi-client management | ✓ |
| Schema validation | ✓ |
| Rich result tracking | ✓ |
Seolyzer
Technical SEO data platform combining site crawling, real-time log analysis, and Google Search Console in one interface
Seolyzer is a French technical SEO platform built around three data sources most tools handle separately: site crawling, server log analysis, and Google Search Console. Its cross-analysis mode merges all three so you can see not just what your site structure looks like, but what Googlebot actually did when it visited: which pages it crawled, which it skipped, and where it spent time that never converted into indexing.
The log module streams Googlebot activity in real time rather than batching it weekly, which matters most during a migration or when diagnosing why technically sound pages are not getting indexed. Enterprise clients including Club Med and ManoMano, plus an endorsement from Aleyda Solis, confirm it is used at real scale; ManoMano specifically uses the API to pull millions of internal links for data science work.
The platform is built for teams that already understand log analysis, not beginners. There is no public pricing page, everything routes through a demo request, and there is no white-label or client-sharing feature visible on the public site. It also has nothing to do with schema: it flags pages a crawler found, but generating or managing structured data is entirely outside its scope.
| Feature | Starter Contact for pricing | Professional Contact for pricing | Enterprise Contact for pricing |
|---|---|---|---|
| SEO Crawler | ✓ | ✓ | ✓ |
| Log analysis | ✓ | ✓ | ✓ |
| Cross-analysis (data fusion) | ✗ | ✓ | ✓ |
| API access | ✗ | ✓ | ✓ |
| Scheduled / recurring crawls | ✗ | ✓ | ✓ |
Head-to-head feature comparison
| Feature | Schema App | Seolyzer |
|---|---|---|
| Core function | Automated schema markup generation and validation at scale | Crawl, real-time log, and Google Search Console data fusion |
| Full-site crawling and audits | No | Yes |
| Structured data / schema automation | Yes, automated JSON-LD across templates | No |
| Real-time server log analysis | No | Yes, in real time |
| Cross-analysis (crawl + log + GSC fusion) | No | Yes, its most distinctive feature |
| Rich result / SERP performance tracking | Yes, ties schema to SERP performance | No |
| API access | Not specified | Yes, on Professional and Enterprise tiers |
| Multi-client / agency management | Yes, dedicated multi-client workspace | Not specified |
| GDPR-compliant hosting | Not specified | Yes |
| Free tier or trial | No | No, demo request required |
| Starting price | Contact for pricing | Contact for pricing |
Considering AI Peekaboo alongside Schema App and Seolyzer?
Schema App argues that clean entity-based markup helps AI models understand and cite your content accurately, but it has no way to confirm that is actually happening. Seolyzer is explicit that it has no AI search visibility tracking at all, it is built entirely around traditional crawl, log, and indexing health. Neither tool can tell you whether ChatGPT, Gemini, or Google AI Overviews are actually mentioning your brand. AI Peekaboo tracks real brand mentions across those engines, with a read and write API on every plan starting at $50 a month and no sales call required. If the schema work or the crawl-and-log diagnostics are already underway, AI Peekaboo is the layer that shows whether any of it is translating into AI citations.
Read the AI Peekaboo review →Which should you choose?
Schema AppSeolyzerSchema AppSeolyzerSeolyzerSchema AppThese two tools rarely compete for the same budget decision because they solve different halves of a technical SEO problem. Seolyzer's job is showing what Googlebot actually does on a site, fusing crawl data, real-time logs, and Search Console into one diagnostic view. Schema App's job is generating and validating structured data at a scale no one wants to hand-code, then proving that markup earns rich results. A large enterprise site with both an opaque crawl budget problem and an unmanaged schema program plausibly needs both, since one will not do the other's job regardless of which sales team you talk to first.
Bottom line
Request the Seolyzer demo if the unresolved problem is understanding real Googlebot behavior, crawl budget waste, or indexing gaps on a large site, and confirm pricing and crawl limits during that conversation since none of it is public. Book the Schema App demo if the actual bottleneck is a schema program too large to hand-code. Both require a sales process before you see a number, so budget the time for two separate conversations rather than expecting either to be a quick self-serve signup.
Frequently asked questions
Do Schema App and Seolyzer do the same job in a technical SEO stack?
Schema App and Seolyzer barely overlap despite both being enterprise, sales-led technical SEO platforms. Seolyzer fuses site crawls, real-time server logs, and Google Search Console data to show what Googlebot actually did on a site; Schema App generates and validates structured data markup and does not crawl a site or analyze logs at all.
Which tool has lower or more transparent pricing?
Neither publishes pricing. Seolyzer routes every plan, Starter through Enterprise, through a demo request, and Schema App requires a sales conversation with no listed tiers at all. A direct cost comparison is not possible without contacting both vendors, though the demo-request model on both tools signals enterprise-level budgets rather than self-serve pricing.
Can Seolyzer generate or manage schema markup the way Schema App does?
No, Seolyzer has no schema generation or management functionality. Its scope is crawling, real-time log analysis, and Google Search Console data fusion. If the goal is automated JSON-LD deployment across page templates, Seolyzer will not do that job on any tier; that is Schema App's entire function.
Does either tool offer an API for pulling data into a custom dashboard?
Seolyzer does, on its Professional and Enterprise tiers, and it is verified in production: ManoMano uses it to pull millions of internal links for data science work. Schema App does not document API access at any tier, so teams needing programmatic schema data should confirm current capabilities directly with Schema App before committing.
Is Seolyzer suitable for a team new to log file analysis?
Not ideally. Seolyzer's own positioning assumes familiarity with server log formats and Googlebot behavior, and the real value of its cross-analysis mode comes from already knowing what discrepancies between crawl, log, and Search Console data actually mean. Teams new to log analysis will get more immediate value from a simpler crawler before adopting Seolyzer's full workflow.
Do Schema App or Seolyzer track AI search visibility, like ChatGPT or AI Overviews mentions?
Neither one does, though for different reasons. Schema App argues that clean entity-based markup helps AI models cite content accurately, which is a claim about groundwork, not a measurement of actual citations. Seolyzer states directly that it has no AI search visibility tracking and is focused entirely on traditional crawl, log, and indexing health.