Is the Modular Monolith Shopify's Best-kept Secret to Scaling?

Each year, millions of people flood the internet and race to “checkout” as Black Friday deals go live. Behind the scenes, Shopify confidently manages this spike in traffic and transactions with speed and stability, supporting over a million businesses and processing billions of dollars in sales worldwide.

Remarkably, this is all achieved without relying on microservicesMicroservices architecture is a software design where the system is broken into many small, independent services—each handling a specific function and communicating over a network..

In an era where scaling often involves splitting systems into countless independent services, Shopify has gone with a different path: the modular monolith.

So, how does a platform of this scale remain fast, reliable, and resilient without fragmenting its architecture?

We'll cover all of that (and more) in today's newsletter:

• What a modular monolith really is.

• How strict folder boundaries keep engineers from colliding.

• Where the tension lies between monoliths, microservices, and the middle ground.

• Why Shopify stakes its scale on this architecture.

• What comes into play when Black Friday traffic hits and teams race to ship.

Happy learning!

A look inside the modular monolith architecture

Before we discuss how Shopify scales, it’s worth taking a moment to look at the structure that supports it. What makes the platform reliable and flexible is how it’s built. So first, let’s get a clear sense of their chosen architecture.

What defines a modular monolith?

In simple terms, a modular monolith means that the codebase is deployed as a single unit, but internally, it’s structured into clean, independent modules. Each module has clear boundaries, well-defined internal APIs, and minimal dependencies. This means developers can work on different parts of the system without stepping on each other’s toes.

At Shopify, this structure is reflected directly in the codebase’s directory layout. Core domains like checkout, billing, and orders live in their top-level folders, such as components/checkout, components/billing, or components/orders. These folders contain all the related models, logic, and tests for that domain and are treated as isolated modules.

Shopify uses static analysis tools that enforce strict separation to maintain clear boundaries between modules. For example, the checkout module can’t enter billing’s internal logic unless it goes through a defined interface. These automated checks prevent accidental dependencies and help the system stay clean and maintainable as it scales.

This structure gives teams confidence to work within their domains, knowing that the system protects architectural boundaries. To better understand where this approach shines, comparing it with more familiar models like traditional monoliths and microservices is useful.

How modular monoliths compare to other architectures

Architectural decisions always involve trade-offs, especially when trying to balance simplicity in the short term with long-term flexibility and maintainability. This tension often plays out in choosing between two well-known models: monoliths and microservices.

A traditional monolith bundles all application logic into a single, unified codebase. This makes it easy to develop and deploy in the early stages of a product. However, as the system grows, changes in one area can unintentionally affect others, making the codebase increasingly fragile and harder to maintain. To address these limitations, many teams look to microservices as an alternative.

In contrast to monoliths, microservices aim to improve modularity by splitting functionality into independently running services that communicate over the network. This model supports team autonomy and domain separation, which can be especially useful in large organizations. However, this flexibility comes at a cost. Teams must actively manage the operational overhead of service orchestration, ensuring network reliability and debugging across distributed systems.

To bridge the gap between a tightly coupled monolith and a fully distributed microservices architecture, the modular monolith offers a practical middle ground. It retains the simplicity of a single deployable unit, much like a traditional monolith, which helps streamline infrastructure and deployments. At the same time, it emphasizes strong internal boundaries and clear ownership of components (not dissimilar to the principles found in microservices). This structure allows teams to work independently within their domains while the system remains cohesive, testable, and observable.

The table below summarizes the trade-offs associated with each architecture to clarify how modular monoliths balance simplicity, modularity, and operational burden.

While architectural comparisons help clarify what a modular monolith offers in theory, its real value becomes clearer when viewed through the lens of an organization operating at scale. The next section examines how this model aligns with Shopify’s technical demands, team structure, and long-term approach to platform growth.

How does the modular monolith fit Shopify’s scale?

Shopify chose to stick with a modular monolith as a thoughtful, strategic decision — not just out of tradition. The size of their platform, the way their engineering teams are organized, and the operational demands of supporting global commerce influenced this choice.

By keeping most of their core functionality in one place, Shopify’s engineers can build and test features in the same environment the system runs in. This makes it easier to catch issues early, rely on shared tools for testing and deployment, and ship changes without the overhead of coordinating multiple separate services.

That doesn’t mean Shopify avoids services entirely. Certain components, such as payment processing and user identity, have been split out when specific demands around scale, security, or isolation make it necessary. But these cases are carefully considered and relatively rare. The engineering culture at Shopify favors keeping functionality within the modular monolith as long as it remains practical. This allows the system to evolve gradually, with changes driven by real-world constraints rather than architectural trends.

Now let's shift from architectural choices to operational reality.

4 steps to scaling for Black Friday

When millions of people flood online stores simultaneously — especially during major events like Black Friday — a platform like Shopify faces enormous pressure.

It doesn’t just need to stay online; it needs to stay fast, reliable, and responsive. Even a brief delay can lead to a loss in sales, and a single failure can impact not just one business but thousands. Meeting this challenge requires smart System Design to handle sudden demand surges while maintaining steady performance.

Let’s examine how Shopify prepares for that demand, starting with the nature of the traffic itself.

1. Understanding the load

Shopify handles massive amounts of traffic on Black Friday, and much of it arrives in unpredictable waves. A small merchant might go viral on social media with no warning, and flash sales can drive thousands of people to the same storefront within seconds. These sudden surges can create serious bottlenecks if the system isn’t designed to handle them smoothly.

Shopify treats every storefront as a performance-critical environment. A slow-loading page or a failed checkout can lead to serious lost revenue for the merchant. To prevent that, Shopify designs its infrastructure to scale efficiently and stay stable under pressure.

Another important component in managing that complexity is handling traffic before it reaches the core backend.

2. Handling traffic at the edge

At the edge of Shopify’s infrastructure is a service called Storefront Renderer (SFR). Its primary role is to manage storefront traffic before it reaches the core backend, helping the system stay stable and responsive under load.

To do that, SFR helps shape the flow of requests in real time. When incoming traffic exceeds safe thresholds, it doesn’t allow everything through simultaneously. Instead, it queues requests, letting them in gradually, much like a venue managing a crowd at the door. This prevents overload while keeping things moving smoothly.

SFR is also designed for scale. It’s stateless and horizontally scalable, meaning Shopify can spin up as many instances as needed, especially during traffic spikes. This scalability is like opening more checkout lanes at a busy grocery store. The more lanes available, the faster customers move through, even during a rush.

Beyond traffic shaping, SFR also boosts performance by efficiently rendering storefront pages and serving cached dynamic content at the edge. This reduces the need to contact the backend for every request, speeding up page loads and lightening the system’s overall workload.

While SFR plays a key role in controlling traffic flow, maintaining speed and stability at scale depends on how efficiently content is delivered to users. That efficiency is made possible by Shopify’s broader optimization strategy. This includes a thoughtfully designed, multi-layered caching system that manages traffic across every level of the stack.

3. Multi-layered caching

Once SFR manages requests, Shopify employs a multi-layered caching architecture to serve content quickly and reduce pressure on back-end systems.

The first layer of caching takes place at the edge of Shopify’s network, powered by a globally distributed content delivery network (CDN)A content delivery network (CDN) is a globally distributed system of servers that delivers web content like images, scripts, and pages to users based on their geographic location, improving speed and reducing server load.. This network stores and delivers static content such as images, stylesheets, and scripts from servers that are geographically closer to the user. By reducing the physical distance between users and the content, the CDN helps pages load more efficiently and reliably. In many cases, even full pages rendered by the Storefront Renderer (SFR) can be temporarily cached at the edge. This is especially valuable during high-traffic periods, such as flash sales, when maintaining fast load times and reduced pressure on the core system is critical.

Beyond the edge, Shopify uses additional caching layers within its application stack. These include:

• In-memory caching: This stores frequently used data like product details, basic store information (such as the store’s name, currency, or language settings), and feature toggles (settings that turn certain features on or off). This allows the system to access that information instantly without recalculating it or repeatedly fetching it from the database.

• Query caching: This intercepts common database requests and serves precomputed results, saving time and reducing load on the data layer.

This layered strategy is especially effective for read-heavy workloads, such as browsing products or navigating collections, allowing Shopify to handle them with minimal backend effort. It also ensures the database remains available for more demanding, write-heavy operations like inventory updates and checkout processing.

Of course, caching isn’t always an option. Some actions, especially those that modify data, require real-time coordination with the back-end services.

4. Serving write-heavy requests

Write-heavy actions like checkout, inventory updates, and payment processing involve real-time changes to critical data. Because they demand accuracy and consistency, these requests bypass caching and interact directly with the back-end services.

To keep these operations responsive under pressure, Shopify uses internal queues and traffic prioritization to ensure that time-sensitive requests, such as completing a purchase, are given priority over less urgent tasks. This means that essential actions continue to work smoothly even during massive traffic spikes. In the meantime, lower-priority background operations may be delayed briefly.

This careful handling of writes ensures that the platform remains reliable when it matters most. Together with caching, queuing, and traffic shaping, it’s part of a broader strategy that lets Shopify scale intelligently. It does this by responding not just to the volume, but also to the nature of each request.

Now that we’ve seen how Shopify’s architecture handles traffic and workload at scale, let’s examine how engineers manage the complexity of building within such a large, fast-moving codebase.

Scaling development in a shared codebase

So far, we’ve seen how Shopify’s architecture handles massive traffic and keeps the platform responsive under load. But handling user demand is only part of the story. True performance also means enabling engineers to build, ship, and iterate quickly, even as the team and codebase grow.

With a massive team contributing to the same codebase, speed, safety, and coordination become as critical as infrastructure. Shopify addresses this challenge with a mix of modular structure, internal tooling, and disciplined workflows that help teams build quickly and reliably within a large, shared system.

Let’s start by looking at how Shopify enables many teams to contribute in parallel while keeping the system stable.

Supporting parallel development at scale

Shopify’s modular monolith is designed to let teams move quickly while staying out of each other’s way. With hundreds of developers shipping changes daily, the system must ensure that one team’s work doesn’t accidentally affect another’s. This requires clear module ownership, strong separation, and disciplined deployment.

To maintain this structure at scale, Shopify relies on internal tooling that makes architectural boundaries visible and enforceable.

Enforcing boundaries with Packwerk

Packwerkhttps://shopify.engineering/a-packwerk-retrospective is a static analysis tool that ensures modules interact only through defined interfaces. Developers explicitly declare which modules can talk to which, and any unintended cross-dependencies are flagged automatically during development.

This ensures that architectural rules are enforced automatically, rather than relying on developers to remember and follow them manually. For example, the checkout module is blocked from reaching into inventory, and marketing logic cannot access billing. Each module is self-contained, with a clear interface that makes it easier to test, reason about, and evolve safely over time.

But while clear boundaries help keep the codebase clean, they’re just as important for speeding up development.

Accelerating feedback with scoped testing

With strong modular separation, Shopify doesn’t have to run the entire test suite for every change. A modification to billing won’t trigger tests for unrelated systems, like fulfillment or product recommendations. Instead, test runs are scoped to the affected module, reducing unnecessary overhead and accelerating results.

This structure speeds feedback loops. Because each module is tested independently, Shopify’s CIContinuous Integration (CI) is the practice of automatically testing and validating code changes as they are integrated into a shared codebase. infrastructure can run thousands of tests in parallel, often surfacing issues within minutes. As a result, developers can move quickly without sacrificing reliability, as problems are caught early and well before reaching production.

Of course, once tests pass and the code is ready to go live, there’s still one final step: making sure it reaches users safely.

Releasing safely with canary deployments

Every production change at Shopify undergoes a canary deploymenthttps://shopify.engineering/software-release-culture-shopify, where new code is gradually released to a small portion of real traffic. During this phase, the system monitors any signs of trouble, such as rising error rates, slower response times, or unusual behavior.

If everything remains stable, the rollout continues smoothly. But when issues arise, the system immediately pauses the deployment and rolls back changes to minimize disruption. This controlled release process helps teams move quickly while keeping the system stable during real-time commerce, across thousands of active storefronts.

Now that we’ve seen how Shopify enables fast, coordinated development within a shared codebase, we’ll focus on long-term sustainability and explore how the system continues to evolve while remaining stable, reliable, and easy to work with.

2 pillars of designing for resilience and evolution

The real test of any architecture is how well it adapts to change over time. New features, shifting workloads, and evolving team structures continuously reshape the system’s behavior, making adaptability as essential as performance.

The modular monolith must remain flexible without becoming fragile to remain resilient amidst this constant change. In this section, we’ll explore how Shopify addresses that challenge through selective service extraction and platform-wide observability.

1. Selective service extraction

Selective service extraction is removing certain parts of a large system from the main codebase and turning them into separate services, but only when there is a clear benefit.

Shopify applies this approach with intention, extracting services when it meaningfully improves performance, scalability, or team independence. In most cases, product features remain within the modular monolith, where teams can move faster, use common tools, and keep the development process more manageable. But sometimes, a specific part of the system becomes so complex that keeping it within the modular monolith causes more problems than it solves.

In those cases, Shopify may extract that component into its service.

Before making that decision, they look for specific indicators that suggest the time is right, for example:

• The component needs to scale independently from the rest of the platform to accommodate high or unpredictable traffic.

• It handles sensitive data and therefore requires isolation for privacy, security, or regulatory compliance.

• It relies on a different programming language, runtime, or infrastructure that doesn’t integrate well with the core system.

• The part has evolved into a mature, stable functionality that can serve as a standalone platform for other teams or systems.

By watching for these signals, Shopify avoids unnecessary complexity and only extracts services when they make sense. This careful approach goes hand in hand with another key principle: ensuring every part of the system remains visible and understandable. In the next section, we’ll see how Shopify uses observability to give developers a clear, consistent view of how the system behaves in real time.

2. Observability as the backbone

As systems grow more complex, it becomes harder to understand what’s happening inside them. That’s why Shopify strongly emphasizes observability, ensuring teams can see what the system is doing at any time, especially when something goes wrong.

To make this work, Shopify has built a consistent observability setup that spans both the modular monolith and the few services that have been separated. Each module logs information in a standard format. When users interact with the site, a trace follows their request step-by-step through the system. For key areas like checkout and search, ready-made dashboards clearly show how those flows perform.

Because all this information is unified and easy to access, developers don’t have to jump between tools or guess what’s happening. If a page loads slowly or an error appears, they can trace the full path of a request from the browser to the servers to the database, all in one place. This level of visibility makes it much safer to enact changes, test new features, and continuously improve the platform without fear of breaking something. It also reflects deeper architectural choices that make the system more resilient and developer-friendly.

So with all of that — what practical lessons can we learn from Shopify’s approach as it relates to System Design?

6 System Design takeaways from Shopify’s approach

Shopify’s architecture demonstrates how thoughtful design can support systems at scale, while offering practical lessons for developers working on projects of any size. Whether building a growing startup product or maintaining a mature platform, many principles here can help us design fast, clear, and sustainable software.

Here are some key takeaways we can carry forward:

1. Modular monolith first: Start with a monolith, but organize it into clear, well-separated modules to support growth and keep the system maintainable.

2. Design around team responsibilities: Give each team clear ownership of a specific module or area of the system, so they can work independently and avoid unnecessary overlap or conflicts.

3. Build for fast feedback: Set up testing at the module level, and use automation to surface issues quickly so developers can fix problems early in the development process.

4. Make updates safely: Roll out changes slowly and with control, so mistakes don’t affect everyone simultaneously.

5. Extract services when necessary: Only split services when justified by scale, performance, or operational need.

6. Make the system observable: Include tools like logs, metrics, and traces to make the system easy to monitor, debug, and improve.

These takeaways reflect a mindset of scaling through clarity and discipline rather than letting the system grow out of control.

TL;DR

Shopify’s architecture challenges the idea that scale always demands microservices. Instead, it shows what’s possible when a system is designed with care: it's modular enough to support thousands of developers, robust enough to handle Black Friday, and simple enough to keep evolving.

Throughout this case study, we’ve also seen how Shopify makes that possible:

• structuring its monolith with strict module boundaries

• reinforcing those boundaries with tooling

• and maintaining performance under pressure through layered caching and traffic shaping

Our hands-on courses are a great place to start if you want to turn some of these ideas into real-world skills. They'll walk you through architectures just like Shopify’s and teach you how to design scalable, maintainable systems from the ground up. Here are some of the most popular ones:

The future of large-scale System Design is already here. Start building it today!