Home Insights White Papers The modern browser AI stack: Web platform APIs and built-in intelligence

The modern browser AI stack: Web platform APIs and built-in intelligence

White paper cover visualizing the modern browser AI stack

The browser evolved alongside the AI boom

For the last few years, conversations about AI have focused on models, cloud infrastructure, and developer tools. Meanwhile, the browser has quietly undergone its biggest transformation in more than a decade.

Modern browsers now include capabilities once reserved for native applications: GPU compute, neural network inference, advanced media processing, hardware acceleration, intelligent text services, and increasingly, built-in AI models. Initiatives such as Baseline and Interop have dramatically improved cross-browser consistency, making it easier to adopt new platform features with confidence.

Understanding the modern browser AI stack

This white paper explains how the browser evolved into a platform for intelligent applications. It examines the technologies that make up the modern browser AI stack, including WebGPU, WebNN, Gemini Nano, Transformers.js, MediaPipe, Chrome’s built-in AI APIs, and WebMCP.

The paper also explores how the browser has become a runtime for AI, media processing, storage, and high-performance computing.

Learn how WebGPU, WebNN, built-in AI models, and client-side runtimes are reshaping modern application development

What you’ll learn

  • How WebGPU, WebNN, built-in AI models, and client-side runtimes fit together
  • The role of Baseline and Interop in reducing browser compatibility risks
  • When to use cloud AI, client-side AI, and on-device AI
  • Which technologies are production-ready today, and which remain experimental
  • How browser-native intelligence can improve performance, privacy, offline functionality, and infrastructure efficiency
  • Why emerging standards such as WebMCP could shape the future of AI agents on the web

New architectural choices for AI-first teams

As AI becomes part of everyday software, technology teams face new decisions around cost, latency, privacy, infrastructure, and user experience.

The browser now offers new architectural options that did not exist a few years ago. Some workloads can run locally, others can combine browser capabilities with cloud-based models, and new approaches can reduce infrastructure costs while improving responsiveness and privacy.

Download the white paper to understand how modern browser capabilities are creating new opportunities for AI architecture, application performance, and browser-native intelligence.

Tags

You might also like

Grid Dynamics white paper cover showing metallic runners and the title “AI agent evaluation: Point of view.”
White Paper
Why AI agents without evaluation are a failure waiting to happen
White Paper Why AI agents without evaluation are a failure waiting to happen

Ask five people in your organization what AI agent evaluation means, and you will get five different answers. Product teams track conversions and outcomes. Engineers test prompts and tool calls. SREs monitor uptime, latency, and token usage. Security teams run red-team exercises. Everyone evaluates...

A large sculpture of a human head profile made of reflective silver square tiles. Stacks of books protrude horizontally from the back of the head.
White Paper
Why advanced media and audio are the future of high-performance UI engineering
White Paper Why advanced media and audio are the future of high-performance UI engineering

AI is making standard frontend work cheaper and faster to produce. Forms, dashboards, CRUD apps, design-system components, and routine full-stack tasks are increasingly automated. That is changing where UI engineers create real value. As routine implementation becomes easier to generate, d...

Grid Dynamics white paper cover titled ‘The architecture of intelligent interfaces’ with floating glass-like UI panels.
White Paper
The architecture of intelligent interfaces
White Paper The architecture of intelligent interfaces

Intelligent interfaces are changing how applications are designed and built, moving from fixed screens to systems that can restructure themselves around the way people actually work. Instead of just swapping content, intelligent user interfaces can decide which components appear, how they are a...

Cover of the “AI SDLC in 2026: Point of view” white paper on AI SDLC maturity, featuring a stylized person looking upward with dynamic light trails.
White Paper
AI SDLC in 2026: Point of view
White Paper AI SDLC in 2026: Point of view

Most enterprises are already betting big on AI… but very few have turned it into a reliable, industrial‑grade software factory. On the backend, most engineering leaders know they need AI SDLC, but few know how to measure whether they’re actually doing it well. Download the white paper to run a...

Two black and white robot faces representing agentic AI framework comparison
White Paper
Agentic AI frameworks comparison and capabilities analysis
White Paper Agentic AI frameworks comparison and capabilities analysis

Choosing the right agentic AI framework matters. Crew AI, Google ADK, LangGraph, and OpenAI Agents SDK each solve different problems, from rapid multi-agent prototyping to durable, stateful workflows and cloud-native enterprise agentic AI deployments.  This comprehensive white paper examine...

White paper cover featuring the same robot and title, emphasizing agentic AI deployment readiness.
White Paper
Production-ready agentic AI deployment
White Paper Production-ready agentic AI deployment

As an enterprise leader, you’ve likely seen countless AI prototype demos over the last few years promising empty buzzwords like “transformation”, “efficiency”, and “competitive edge”. But how many of those prototypes actually work in production? Over the past decade, multiple AI hype cycles ha...

Building an enterprise-grade agentic AI platform using Temporal white paper cover
White Paper
Building an enterprise-grade agentic AI platform using Temporal
White Paper Building an enterprise-grade agentic AI platform using Temporal

Running agent-based systems across your enterprise comes with tough problems. The main ones are keeping costs down, scaling up fast, and making sure nothing breaks when things go wrong. This white paper gets into the real challenges that come up when teams move from simple agent pilots to a ful...

Let's talk

    This field is required.
    This field is required.
    This field is required.
    By sharing, I consent to the use or processing of my personal information by Grid Dynamics for the purpose of fulfilling this request and in accordance with Grid Dynamics’s Privacy Policy. For more details about how to opt-out, please refer to the Privacy Policy and Terms & Conditions.
    Submitting
    quote icon

    We consistently turn to Grid Dynamics for our most complex challenges. Their data scientists and AI engineers are top-notch—highly experienced and deeply knowledgeable.

    Sr. Engineering Director, global auto parts retailer

    Geometric composition with teal car wheel

    Thank you!

    It is very important to be in touch with you.
    We will get back to you soon. Have a great day!

    check

    Thank you for reaching out!

    We value your time and our team will be in touch soon.

    check

    Something went wrong...

    There are possible difficulties with connection or other issues.
    Please try again after some time.

    Retry