JSON-LD vs Microdata vs RDFa: Which Structured Data Format Wins in 2026?

All three formats express the same schema.org types and properties. The difference is how they bind annotations to HTML.

JSON-LD places all structured data in a standalone <script type="application/ld+json"> block, completely decoupled from visible HTML. It uses @context, @type, and property keys like a conventional JSON object.

Microdata embeds annotations directly into HTML attributes using itemscope, itemtype, and itemprop. The structured data is interleaved with the content it describes — e.g., <div itemscope itemtype="https://schema.org/Article"><h1 itemprop="headline">...</h1></div>.

RDFa works similarly to Microdata but uses typeof, property, and vocab attributes. It is an extension of W3C's RDF standards, offering more expressiveness for complex data graphs.

JSON-LD has become the dominant format for several concrete reasons.

Separation of concerns is the strongest argument. Because JSON-LD lives in a script tag, it cannot be damaged by CMS content filters, minification pipelines, or DOM manipulation. Microdata and RDFa can be stripped or corrupted when HTML passes through sanitizers or template engines.

Google's explicit preference has driven adoption. Google's documentation has recommended JSON-LD since 2015, and in 2026 it remains the format most thoroughly tested by the Rich Results Test and Search Console (Google Search Central, "JSON-LD Recommendation," 2025).

Ease of generation matters for engineering teams. JSON-LD can be constructed server-side as a pure data structure, serialized to JSON, and injected into the page head or body — no HTML parsing or attribute manipulation required. This makes it the natural choice for headless CMS, API-first stacks, and Jamstack deployments.

Performance is effectively identical across formats. The difference in page weight between a JSON-LD block and equivalent Microdata is negligible at scale (Schema.org, "FAQ: JSON-LD vs Microdata," 2025).

Microdata is not obsolete, but its use cases have narrowed. It may be right when you cannot inject script tags into the page (some legacy CMS platforms restrict <script> elements), when structured data must be authorable in a WYSIWYG editor, or when maintaining an existing validated implementation without budget for migration.

The major risk is fragility. A single CMS update that strips itemprop attributes will silently break your structured data. Monitoring Microdata validity requires continuous regression testing (Google Search Central, "Structured Data Best Practices," 2025).

RDFa offers more expressive power than JSON-LD or Microdata — supporting multiple vocabularies in a single document, custom property chains, and deep graph relationships. However, adoption has declined steadily. In 2026 it is primarily used in government and academic publishing where RDF-based data standards are mandated. For commercial web development, the complexity overhead rarely justifies the benefits. Google supports RDFa for most rich result types, but testing tooling is less mature than for JSON-LD (W3C, "RDFa 1.1 Recommendations," 2025).

For existing Microdata deployments, use a dual-markup strategy: deploy JSON-LD alongside existing Microdata for a transition period. Google deduplicates identical markup from the same page, so no conflict arises. Run both formats for two to four weeks, validate through the Rich Results Test, then remove Microdata once JSON-LD is confirmed working.

• [ ] Choose JSON-LD for all new structured data implementations
• [ ] Document the format decision and rationale in your team's technical standards
• [ ] If maintaining Microdata, set up automated validation checks in CI/CD pipeline
• [ ] Verify that your CMS allows script tag injection for JSON-LD placement
• [ ] Run Rich Results Test after any template or CMS update
• [ ] Consider dual-markup migration only if existing Microdata is well-established