Microdata is a syntax for embedding machine-readable metadata inside HTML documents using attributes like itemscope, itemtype, and itemprop. Unlike JSON-LD, which isolates structured data in a script block, Microdata intertwines annotations with visible content, allowing search engines to parse semantic meaning directly from the DOM. It relies on the Schema.org vocabulary to define entity types and properties.
Glossary
Microdata
An HTML specification used to nest structured data within existing page content using tag attributes like itemscope and itemprop, serving as an alternative to JSON-LD.
STRUCTURED DATA FORMAT
What is Microdata?
Microdata is an HTML specification used to nest structured data directly within existing page content using tag attributes, serving as an alternative to JSON-LD for annotating entities.
While historically significant, Microdata has been largely superseded by JSON-LD as Google's recommended serialization format due to JSON-LD's cleaner separation of concerns and easier dynamic injection. However, Microdata remains relevant for legacy systems and specific use cases where tight coupling between visible text and structured markup is required for entity linking and semantic HTML authoring.
STRUCTURED DATA SYNTAX COMPARISON
Microdata vs. JSON-LD vs. RDFa
A technical comparison of the three primary syntaxes for implementing Schema.org structured data markup on web pages.
| Feature | Microdata | JSON-LD | RDFa |
|---|---|---|---|
W3C Recommendation Status | Completed (HTML5) | Completed (JSON-LD 1.1) | Completed (HTML+RDFa 1.1) |
Google Preferred Format | |||
Markup Location | Inline within HTML body | Standalone script block in head or body | Inline within HTML body |
Separation of Concerns | Mixed with content markup | Cleanly separated from content | Mixed with content markup |
Ease of Manual Authoring | Moderate | High | Low |
Dynamic Injection via JavaScript | |||
Supports @id Node Referencing | Limited (itemid) | ||
Supports All Schema.org Types |
Enabling Efficiency, Speed & Accuracy
Intelligent Analysis, Decision & Execution
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Talk to Us
Search across company data
Give teams answers from docs, tickets, runbooks, and product data with sources and permissions.
Useful when people spend too long searching or get different answers from different systems.
Enterprise searchRAGPermissions
Read more
Automate internal workflows
Use AI to route work, draft outputs, trigger actions, and keep approvals and logs in place.
Useful when repetitive work moves across multiple tools and teams.
AI agentsWorkflow automationGovernance
Read more
Add AI to products and internal tools
Build assistants, guided actions, or decision support into the software your team or customers already use.
Useful when AI needs to be part of the product, not a separate tool.
AI integrationDecision supportModel routing
Read moreMICRODATA IMPLEMENTATION
Frequently Asked Questions
Clear, technical answers to the most common questions about implementing HTML Microdata for structured data markup and AI-driven search visibility.
Microdata is an HTML5 specification that allows you to nest machine-readable structured data directly within the existing content of a web page using specific tag attributes. It works by annotating HTML elements with itemscope, itemtype, and itemprop attributes to define entities and their properties. When a search engine or AI parser crawls the page, it extracts these annotations to understand the semantic meaning of the content—such as identifying a person's name, a product's price, or an event's location—without requiring a separate data block. Unlike JSON-LD, which lives in a <script> tag in the document head, Microdata is interwoven with the visible markup, making it a direct, inline method for declaring structured entities.
About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
LinkedIn
Limited slotsGet a Free AI Consultation
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
01
Review the use case
We understand the task, the users, and where AI can actually help.
Read more02
Pick the right approach
We define what needs search, automation, or product integration.
Read more03
Build the first useful version
We implement the part that proves the value first.
Read more04
Improve from there
We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
Talk to Us