Five Years In, Jamstack’s Principles Hold Up

Five years ago, the idea of separating the front-end web layer from back-end business logic was still an emerging trend without a name. Today, that architecture — Jamstack — has gone mainstream, adopted by major brands and scaled to millions of requests in projects like the Covid Tracking Project. As the ecosystem grows louder and more commercial, it’s worth revisiting the original principles and examining where the architecture is heading.

The Core Idea: Compile the UI

Jamstack’s foundational principle is that the UI is compiled. The goal is to do as much work as possible ahead of time, often pre-rendering the entire site so it doesn’t require a backend once deployed. Decoupling the frontend from back-end services enforces a clear contract between the UI and the rest of the system, which defaults to simplicity: the frontend has a limited contact surface with external services, making it easier to reason about how changes will affect its operation.

Not everything can be pre-rendered, of course. The Jamstack architecture also handles dynamic, personalized applications by pulling data as needed through asynchronous requests — REST, GraphQL, or WebSockets. If you precompile as much of the UI as possible and load data to deliver a customized experience, you’re shipping Jamstack.

The ecosystem is evolving rapidly across back-end services, developer tooling, and client-side technologies. Three trends stand out for what they mean for Jamstack developers in the near term.

1. Distributed Persistent Rendering (DPR)

Jamstack’s simplicity has made building and deploying web applications easier to reason about. Code and content updates can be pre-rendered as clean, atomic deployments pushed to the edge, offering strong reliability and performance guarantees without complex infrastructure.

But pre-rendering a large site can mean waiting minutes for each new deployment. That’s driving innovation in build speed — esbuild, for instance, can complete a production bundle in under a second that might take Parcel or Webpack over a minute. Tools like Vite and Snowpack leverage native ES modules to make local development nearly instantaneous.

Not every trend points forward. Frameworks like Remix and Blitz lean on server-side rendering approaches from the past, risking a return of the complexity Jamstack was designed to avoid. Layers of caching can make server-rendered apps performant, but developers lose the guarantees of atomic deployments. Blitz, by moving the monolith into the frontend, makes full-stack apps runnable on typical Jamstack platforms but without clear decoupling between the web experience and back-end logic.

What’s gaining real momentum are hybrid frameworks — Next.js, Nuxt.js, SvelteKit — that let developers mix pre-rendered pages with routes rendered via serverless functions. Serverless functions have their own performance implications, however. The community appears to be moving toward an architecture with request-level control over performance profiles:

  • Pages entirely pre-rendered at build time
  • Pages delivered dynamically via serverless functions
  • Pages built on-demand that persist as static CDN assets

Next.js has pioneered Incremental Static Regeneration, pairing serverless functions with caching strategies like Stale While Revalidate. It’s a powerful technique, but unless developers explicitly opt out of stale-while-revalidate, the atomic deploy guarantee is violated by serving mixed assets from different deploys. It’s also currently exclusive to a single framework and deeply tied to one provider.

Netlify is advocating a different approach: Distributed Persistent Rendering (DPR), which renders critical pages at build time and defers other pages — like older blog posts — until they are requested. DPR is designed as an incremental build architecture compatible across frameworks and site generators, from 11ty to Nuxt to Next.js. It aims to dramatically reduce build times for larger sites, addressing a core criticism of static site generation. A Request For Comments is open on Jamstack.org to involve the community in refining the architecture.

2. Streaming Updates from the Data Layer

State management has largely been an in-browser, in-memory concern. Each browser tab holds its own state, and connecting that local state back to the data services that power it remains complex. That’s improving as more services support real-time data subscriptions — Hasura, OneGraph, and Supabase all offer this capability, and wider adoption is likely as data stores are cached and distributed to the edge.

Twilio’s expanding APIs now include streaming video plus streaming “data tracks,” which enable complex collaboration apps that stay synchronized across participants. New providers are also emerging that aggregate data across back-end services, connecting a UI to a single, standard stream of updates from multiple underlying APIs, whether or not GraphQL is used as the query language.

3. Collaboration Expands Beyond Developers

Jamstack is built on a Git workflow that scales well for development teams. Going forward, those developer-centric tools will expand to include writers, editors, designers, and SEO experts across an organization.

Synchronous collaboration — multiple cursors in a Google Doc — is coming to CMS and design tools like Sanity and Figma. Asynchronous work, though, still lacks the powerful branch, stage, and merge workflow developers use, with collaborative discussion attached to each pull request. Early git-based CMS tools helped non-developers manage content like code without knowing each change was committed. New tools are tackling visual page edits in ways compatible with popular site generators like Gatsby and Next.js, lowering the bar for non-developer participation.

Deep integrations are also bringing automated contributions into dev, build, and deploy workflows. GitHub pull request histories show how many tools now run automated tests to catch errors before deployment. Netlify’s docs, for example, are linted not only against code standards but also content standards, ensuring consistency in vocabulary and phrasing. Teams can tie performance budgets and SEO standards to each deployment, with alerts and logs linked directly to GitHub issues.

Expect those integrations to multiply, with Git workflows underpinning not just code changes but also content, data, and design assets. Friendly interfaces into these workflows will allow more contributors to comment, commit, and collaborate, bringing developer productivity tooling further into the mainstream.

From Static Sites To Dynamic Systems

The original promise of Jamstack was simple: pre-render as much as possible and serve it from a CDN. That model still holds, but the boundaries have shifted. Teams are no longer limited to purely static output; the architecture now supports dynamic, real-time features and very large-scale deployments while retaining the core benefits of decoupling and atomic, immutable deploys.

New build strategies and compute primitives are the key drivers. These tools let developers handle complex, personalized content and frequent updates without abandoning the performance and security advantages of a static-first approach. For web teams, marketers, and product managers, the practical impact is that Jamstack can now serve as the backbone for applications that were previously out of reach for the architecture.

Scaling Under Real-World Load

A useful reference point is the COVID Tracking Project, which grew from zero to two million API requests per month in just three months. The project illustrates how a Jamstack setup can absorb explosive traffic growth without requiring a fundamental re-architecture. The ability to scale horizontally by serving cached assets, combined with on-demand API calls, proved sufficient for a mission-critical, high-traffic use case.

This kind of workload — variable, spiky, and data-heavy — was once considered a poor fit for static hosting. The evolution of the ecosystem has made it a realistic option, pushing Jamstack beyond brochure sites and into the realm of data-driven web apps.

Rendering Models: ISR And Beyond

Incremental Static Regeneration (ISR) has been widely adopted as a way to update static pages after deploy. It works well for content that changes periodically, but it is not without flaws. Chief among them is the complexity of cache invalidation and the potential for stale or inconsistent data across multiple CDN nodes.

To address these shortcomings, a newer approach called Distributed Persistent Rendering (DPR) has emerged. DPR shifts the rendering work to the edge, persisting rendered pages across a distributed network. This model reduces build times by avoiding full-site rebuilds and improves freshness by allowing individual pages to be re-rendered on demand. The result is a system that combines the speed of static delivery with the flexibility of server-side rendering, but without the traditional monolithic server footprint.

These advances point toward a future where the line between "static" and "dynamic" is increasingly blurred. The focus is no longer on which one you choose, but on how you combine the strengths of each to match the specific needs of your project.

Additional Resources

  • Documentation on ISR's benefits and its trade-offs, by Cassidy Williams (Netlify Blog)
  • An explanation of DPR as a faster-build Jamstack approach, by Matt Biilmann (Netlify Blog)
  • The official Jamstack glossary for defining key terms in the ecosystem