Introduction
Mathematical notation on the web has historically been a challenge. Unlike plain text, mathematical expressions require specialized rendering to display fractions, integrals, matrices, and complex symbols correctly. Server-side math rendering engines solve this problem by converting LaTeX markup into clean HTML, SVG, or MathML output — ensuring equations look perfect across all browsers and devices.
In this guide, we compare three leading open-source math rendering solutions: MathJax (the veteran), KaTeX (the speed demon), and TikZJax (the diagram specialist). Each serves different use cases, from documentation platforms to educational tools to scientific publishing.
Feature Comparison
| Feature | MathJax | KaTeX | TikZJax |
|---|---|---|---|
| Language | JavaScript/Node.js | JavaScript | WebAssembly (compiled from C) |
| Output Formats | HTML-CSS, SVG, MathML | HTML, MathML | SVG |
| LaTeX Coverage | Extensive (AMSmath + packages) | Core LaTeX + common packages | TikZ/PGF subset |
| Rendering Speed | Moderate | Very Fast (10x MathJax) | Slow (WASM compilation) |
| Server-Side Rendering | Yes (Node.js API) | Yes (Node.js API) | Yes (any HTTP server) |
| Diagrams | Limited (via extensions) | No | Full TikZ diagram support |
| Accessibility | Built-in (a11y extension) | Limited | None |
| Custom Macros | Yes (\ | ||
| ewcommand) | Yes (\gdef) | No (pre-compiled) | |
| File Size | ~2MB (full bundle) | ~280KB (minified) | ~2.5MB (WASM binary) |
| GitHub Stars | 10,100+ | 20,100+ | 560+ |
| License | Apache 2.0 | MIT | MIT |
MathJax: The Comprehensive Standard
MathJax is the most established math rendering engine, powering mathematics on sites ranging from Stack Exchange to arXiv to major academic publishers. Version 3 brought a complete rewrite with significant performance improvements and a modern Node.js API for server-side rendering.
Key Strengths
MathJax’s LaTeX compatibility is its biggest advantage. It supports nearly the entire AMSmath package plus extensions for physics notation, chemical equations, and commutative diagrams. If your LaTeX compiles in a standard distribution, MathJax can likely render it.
Server-side rendering with MathJax-Node:
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Docker deployment:
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MathJax also excels at accessibility. Its a11y extension provides screen reader support, collapsible sub-expressions, and semantic navigation — critical for educational institutions required to meet WCAG standards.
KaTeX: The Speed-First Renderer
KaTeX was created by Khan Academy to solve a specific problem: rendering thousands of math expressions quickly in educational content. It achieves rendering speeds up to 10x faster than MathJax by pre-compiling LaTeX into a streamlined internal representation.
Key Strengths
KaTeX’s speed is transformative for content-heavy math sites. It renders most expressions synchronously, eliminating the “flash of unstyled math” that plagues slower renderers. For a page with 100 equations, KaTeX completes rendering in milliseconds while MathJax may take seconds.
Server-side rendering with KaTeX:
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The small bundle size (280KB minified vs MathJax’s 2MB) makes KaTeX ideal for bandwidth-sensitive applications and mobile-first experiences. It’s the default choice for documentation platforms like Docusaurus, VuePress, and MDBook.
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TikZJax: The Diagram Specialist
TikZJax takes a fundamentally different approach — it compiles the TikZ/PGF LaTeX diagramming package to WebAssembly, enabling server-side rendering of complex mathematical diagrams, commutative diagrams, and vector graphics directly from LaTeX source.
Key Strengths
TikZJax’s diagramming capability is unique. While MathJax and KaTeX render individual equations, TikZJax renders complete diagrams — Feynman diagrams, neural network architectures, chemical structures, and circuit diagrams — using the same LaTeX syntax researchers already know.
Deploying TikZJax as a server:
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For pure server-side rendering without a browser, you can use a headless approach:
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Why Self-Host a Math Rendering Engine?
Performance and Reliability: CDN-hosted math renderers can be slow or unavailable during outages. Self-hosting ensures your math content loads instantly from your own infrastructure, independent of third-party services.
Customization: Self-hosted instances allow custom LaTeX macros, organization-specific notation standards, and integration with your existing build pipeline. You can pre-render all math at build time for static sites.
Privacy: When using CDN-hosted renderers, every math expression on your page triggers a request to an external server. Self-hosting keeps all rendering local, which matters for internal documentation, proprietary research, or privacy-focused platforms.
For related self-hosted documentation tools, see our API documentation guide for rendering API specs. If you’re building technical documentation at scale, our static site generator comparison covers the broader ecosystem.
Deployment Architecture
A complete math rendering stack for a documentation site:
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Integrating Math Rendering into Your Documentation Pipeline
The real power of server-side math rendering emerges when integrated into a documentation build pipeline. Here is how to make math rendering a seamless part of your content workflow.
Pre-Rendering at Build Time: The most efficient approach is to render all math during your static site build. Hugo, Zola, and other SSGs can invoke KaTeX or MathJax via their Node.js APIs during build, storing the output as static HTML. This means zero client-side JavaScript for math rendering—pages load instantly with fully rendered equations.
CI/CD Integration: Add math rendering verification to your CI pipeline. Before deployment, run all LaTeX expressions through your chosen renderer and flag any that fail. This catches malformed equations before they reach production:
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Caching Rendered Math: For dynamic sites, cache rendered math output. Use a Redis-backed cache keyed on the LaTeX source hash. KaTeX’s deterministic output makes this straightforward—identical input always produces identical output, so cache invalidation is never an issue.
Hybrid Approach for Complex Sites: Large documentation platforms can combine KaTeX for inline math (speed) with MathJax for display equations requiring complex packages (compatibility) and TikZJax for diagrams (specialized needs). Route each equation to the appropriate renderer based on its complexity via a simple API gateway.
FAQ
Which renderer should I use for a static site with 1000+ equations?
KaTeX is the clear winner for high-volume math content. Its synchronous rendering and small bundle size mean fast page loads even with hundreds of equations. Pre-render all math at build time using KaTeX’s Node.js API, and serve static HTML with zero client-side rendering overhead.
Can I use these renderers without Node.js?
MathJax and KaTeX both offer browser-side rendering that works without any server. For server-side rendering, Node.js is the primary runtime, though MathJax has limited Python support via third-party wrappers. TikZJax runs in the browser as WebAssembly and can be called server-side through a headless browser.
Does KaTeX support all the LaTeX I use in my academic papers?
KaTeX supports the most commonly used LaTeX commands — fractions, integrals, matrices, Greek letters, arrows, and AMSmath environments. It does NOT support: custom .sty files, TikZ/PGF diagrams (use TikZJax for those), and some obscure AMSmath environments. MathJax has broader coverage if you need esoteric packages.
How do I handle accessibility for math content?
MathJax has the best accessibility story with its built-in a11y extension providing screen reader support, semantic navigation, and collapsible expressions. KaTeX offers basic accessibility through its output format options. For WCAG compliance, MathJax is the recommended choice.
What’s the performance difference in a production environment?
In benchmarks, KaTeX renders a page of 100 equations in ~50ms while MathJax takes ~500ms. However, MathJax v3 has narrowed this gap significantly from v2. For server-side pre-rendering where speed is less critical, the choice should depend on LaTeX coverage needs rather than raw speed.
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