JSON vs XML: Choosing the Right Data Format

Origins: Where Each Format Came From

Understanding why JSON and XML exist — and the problems they were designed to solve — helps explain the trade-offs you encounter when choosing between them today.

XML: The W3C Standard (1998)

XML (Extensible Markup Language) was published as a W3C Recommendation in February 1998. It descended from SGML (Standard Generalized Markup Language), a powerful but complex meta-language that had been an ISO standard since 1986. SGML was the foundation for HTML, but its full specification was too heavyweight for everyday use on the web. XML was designed to retain SGML's flexibility — the ability to define custom elements and document structures — while dramatically simplifying the syntax and removing rarely used features. The result was a format expressive enough to describe virtually any data structure, backed by a rich ecosystem of companion specifications: XSD for schema validation, XPath for querying, XSLT for transformation, and namespaces for combining vocabularies from different sources without collision.

Through the early 2000s, XML became the dominant format for data interchange. SOAP web services exchanged XML envelopes. RSS and Atom feeds used XML to syndicate content. Enterprise integration platforms built entire architectures around XML message buses. In ad tech specifically, XML became the foundation for VAST (Video Ad Serving Template), VMAP (Video Multiple Ad Playlist), and MRAID creative specifications — standards that remain central to digital advertising today.

JSON: The Lightweight Alternative (2001–2017)

JSON (JavaScript Object Notation) emerged in the early 2000s, popularized by Douglas Crockford, who recognized that the object literal syntax already built into JavaScript could serve as a lightweight, human-readable data interchange format. Crockford first specified JSON on json.org around 2001, and it was later formalized as ECMA-404 and IETF RFC 8259 (published December 2017). Unlike XML, JSON was not designed by a standards committee to be all things to all people. It was designed to be simple: six data types (strings, numbers, booleans, null, objects, and arrays), no attributes, no namespaces, no processing instructions. This simplicity was its greatest advantage. Any JavaScript runtime could parse JSON natively with JSON.parse(), and the format was compact enough to transmit efficiently over network connections.

JSON's adoption accelerated with the rise of REST APIs, AJAX-driven web applications, and mobile apps. By the 2010s, JSON had replaced XML as the default format for most web APIs. In programmatic advertising, JSON became the format for OpenRTB bid requests and responses, Prebid.js configuration, and virtually all real-time communication between demand-side and supply-side platforms.

Syntax Comparison: Side by Side

The most immediate difference between JSON and XML is their syntax. Consider a simple data structure representing an ad placement:

In JSON, this might look like: {"placement": {"id": "abc123", "size": [300, 250], "floor": 1.50, "video": false}}. The structure uses curly braces for objects, square brackets for arrays, colons to separate keys from values, and commas between elements. Keys are always double-quoted strings. Values can be strings, numbers, booleans, null, objects, or arrays.

The equivalent in XML might be: <placement id="abc123"><size><width>300</width><height>250</height></size><floor>1.50</floor><video>false</video></placement>. XML uses opening and closing tags to define elements, attributes within opening tags for metadata, and text content between tags for values. There is no native array construct — repeated elements or wrapper elements serve the purpose.

Several structural differences become apparent. JSON has a clear distinction between arrays ([]) and objects ({}), while XML represents both with elements, requiring context or schema to distinguish a list from a single item. JSON values are typed (numbers, booleans), while XML text content is inherently string-typed and requires schema-level typing for validation. XML supports attributes as a separate concept from child elements, giving document authors two ways to attach data to an element; JSON has only key-value pairs. XML supports mixed content — text interspersed with child elements — which is essential for document markup but uncommon in data interchange.

When to Use JSON

JSON excels in scenarios where simplicity, speed, and tight integration with web technologies are priorities.

REST APIs and Real-Time Bidding

The OpenRTB specification — the protocol that powers most programmatic advertising auctions — uses JSON for bid requests and bid responses. A typical bid request contains the impression opportunities, device information, user data, site or app context, and publisher preferences, all encoded as nested JSON objects. SSPs send these requests to DSPs, which parse the JSON, evaluate the opportunity, and return a JSON bid response — all within a latency budget of roughly 100 milliseconds. JSON's compact syntax and fast native parsing in every major language make it ideal for this high-throughput, latency-sensitive workflow.

Configuration and Browser Applications

Prebid.js, the most widely used header bidding wrapper, uses JSON for adapter configuration, ad unit definitions, and bid parameters. Because Prebid.js runs in the browser, JSON is the natural format — configurations can be defined as JavaScript object literals and serialized to JSON for storage or transmission without any conversion layer. Similarly, consent management platforms (CMPs), analytics libraries, and ad server integrations all use JSON configuration objects that integrate seamlessly with the JavaScript execution environment.

Data Storage and Event Logging

JSON is the format of choice for structured logging, NoSQL databases (MongoDB, DynamoDB, Elasticsearch), and event streaming platforms (Kafka messages, AWS Kinesis records). Ad tech data pipelines that process impression events, click events, and conversion data typically serialize these events as JSON records for ingestion into data warehouses and real-time analytics systems.

When to Use XML

XML remains the right choice in specific areas where its richer feature set provides capabilities that JSON cannot match.

Document Markup and Mixed Content

XML's ability to mix text content with structured elements makes it suitable for document-oriented formats. HTML (and XHTML) are XML-derived. Publishing formats like EPUB use XML for content and metadata. If your data involves paragraphs of text with embedded formatting, citations, or annotations, XML handles this naturally while JSON would require awkward workarounds like embedding HTML strings within JSON values.

Schema Validation with XSD and Namespaces

XML Schema Definition (XSD) provides rigorous, standardized validation that goes far beyond what JSON Schema currently offers. XSD can define complex type hierarchies, enforce data type constraints on element content, specify occurrence rules for child elements, and validate against multiple namespaces simultaneously. For industries that require strict compliance validation — financial services, healthcare, government — XSD-based validation is an established practice with mature tooling. Namespaces allow XML documents to combine elements from different vocabularies (for example, a VAST document that includes elements from a custom tracking extension) without name collisions.

VAST and VMAP Video Ad Tags

The IAB's VAST (Video Ad Serving Template) specification uses XML to define video ad responses. A VAST document describes the ad creative (media files, duration, codec), tracking events (impression, start, first quartile, midpoint, third quartile, complete), click-through URLs, companion ads, and verification scripts. VMAP (Video Multiple Ad Playlist) extends this with ad break scheduling for long-form content. These specifications leverage XML's hierarchical structure and attribute system to express complex ad configurations. Video players and SDKs across the industry parse VAST XML to render ads, making XML fluency essential for anyone working in video advertising.

Performance Considerations

Performance differences between JSON and XML are measurable and can matter at scale, though they are often less dramatic than commonly assumed.

Payload Size

JSON is typically 30–50 percent smaller than an equivalent XML representation of the same data. This difference comes from XML's requirement for closing tags (every <element> needs a matching </element>), the verbosity of element names repeated in both opening and closing tags, and the overhead of XML declarations, namespaces, and attributes. For a single API call, this difference is negligible. For a system processing billions of bid requests per day, where every kilobyte multiplied by billions translates to terabytes of bandwidth, the size advantage is financially meaningful.

Parsing Speed

JSON parsers are generally faster than XML parsers for equivalent data. JSON's simpler grammar — no DTDs, no namespaces, no processing instructions, no comments — means the parser has fewer code paths and fewer edge cases. In JavaScript environments, JSON.parse() is implemented in the engine's native code (C++ in V8, for example), making it extremely fast. XML parsing typically involves either DOM-based parsing (building a full in-memory tree, which is memory-intensive for large documents) or SAX/streaming parsing (event-driven, lower memory but more complex application code). For ad tech's latency-sensitive bid processing, JSON's parsing speed advantage is a practical consideration.

Compression Equalizes Size

When HTTP compression (gzip or Brotli) is applied, the size difference between JSON and XML shrinks significantly. XML's repetitive tag names compress very well because compression algorithms exploit repeated patterns. A 50 percent raw size advantage for JSON might shrink to 10–15 percent after gzip. If your payloads are always served compressed (as they should be in production), the raw size difference matters less than the parsing speed difference.

Ecosystem and Tooling

Both formats have mature ecosystems, but the tooling has evolved in different directions reflecting each format's strengths.

JSON Ecosystem

JSON Schema provides validation capabilities that, while less powerful than XSD, cover the vast majority of API validation needs. Tools like jq allow command-line querying and transformation of JSON data with a concise expression language — useful for piping API responses through filters in shell scripts. Every modern programming language includes JSON parsing in its standard library. GraphQL APIs return JSON. REST API documentation tools like OpenAPI/Swagger describe JSON request and response bodies. The JSON ecosystem is optimized for API-driven, web-centric workflows.

XML Ecosystem

XML's ecosystem is broader and more established in enterprise contexts. XPath provides a powerful query language for navigating XML documents — selecting nodes, filtering by attributes, computing values across elements. XSLT (Extensible Stylesheet Language Transformations) can transform XML documents into other XML documents, HTML, or text, enabling complex data pipelines without writing procedural code. XSD validation is deeply integrated into enterprise middleware, message brokers, and API gateways. For ad tech teams working with VAST, familiarity with XPath is valuable for programmatically extracting tracking URLs, media file sources, and extension data from ad responses.

Working with Both in Ad Tech

In practice, ad tech teams do not choose between JSON and XML — they work with both daily. A single video ad impression illustrates this perfectly. The programmatic auction uses JSON throughout: the publisher's Prebid.js configuration is a JSON object, the OpenRTB bid request sent to SSPs is JSON, the bid responses from DSPs are JSON, and the winning bid notification is JSON. But once the auction resolves and the video player needs to render the winning ad, it fetches a VAST tag — which is XML. The tracking events fired during playback may be logged as JSON events in the analytics pipeline.

This dual-format reality means that ad tech engineers and QA professionals need fluency in both formats. You need to read and debug JSON bid requests with the same confidence that you read and debug VAST XML responses. You need formatting tools for both. You need to understand validation for both. Treating one format as "better" than the other misses the point — each serves a specific role in the ad delivery pipeline, and both must work correctly for an ad to reach the user.

When debugging a video ad that fails to play, the investigation often crosses the format boundary. You might start by inspecting the JSON bid response to confirm that the creative URL and ad markup are correct. Then you follow that URL to the VAST XML response and verify that the <MediaFile> elements point to valid video assets, that the <TrackingEvents> are present and properly formed, and that no XML parsing errors exist. Tools that help you work fluently in both formats — formatting, validating, comparing — are essential for this cross-format debugging workflow.

Practical Advice for Teams Using Both Formats

For teams that interact with JSON and XML on a daily basis, the following practices help maintain efficiency and reduce errors across both formats.

• Keep formatting tools bookmarked. Have your JSON and XML beautifiers accessible at all times. When a payload arrives minified — from a network inspector, a log file, or a colleague's Slack message — format it before reading. The seconds spent formatting save minutes of interpretation effort.
• Learn the query languages. For JSON, learn basic jq syntax for command-line filtering. For XML, learn XPath expressions for selecting elements and attributes. These skills let you extract specific data from large payloads without manually scanning the entire document.
• Validate early and often. Before debugging business logic, confirm that your data is structurally valid. A JSON beautifier that reports parse errors catches trailing commas and unquoted keys. An XML beautifier that reports well-formedness errors catches missing closing tags and mismatched element names. Structural validation is a 5-second check that prevents 30-minute wild goose chases.
• Standardize within your team. Agree on a common set of tools so that formatted output is consistent across team members. When everyone uses the same JSON indentation (2 spaces, 4 spaces) and the same XML formatting rules, shared payloads are directly comparable and diffs are clean.
• Document with examples. When writing internal documentation about APIs, integrations, or data flows, include formatted JSON and XML examples. A formatted example communicates structure more clearly than a paragraph of description. Annotate the examples to explain non-obvious fields or values.
• Use diff tools for both formats. Structural comparison tools that understand the format (rather than comparing raw text) produce more meaningful diffs. For JSON, structural diff tools ignore key ordering and whitespace differences. For XML, tools that understand element semantics ignore attribute ordering and insignificant whitespace. This reduces false positives and helps you focus on actual changes.

Conclusion

The JSON vs XML debate is not a matter of picking a winner. Both formats have earned their place in the technology landscape, and both are deeply embedded in ad tech workflows. JSON dominates real-time data interchange — bidding, configuration, event logging — because of its simplicity, compact size, and native browser support. XML remains essential for document-oriented formats, schema-driven validation, and established industry standards like VAST and VMAP. The most effective ad tech professionals are fluent in both, equipped with the right tools for each, and pragmatic about choosing the right format for each specific use case.