Is "Microservices" Dead? — In 2026, Why Are Interviewers Favoring "Modular Monolith"?

In 2026, the pendulum of software architecture finally swung back to rationality, marking the end of the "Resume Driven Development" era and a full return to engineering pragmatism. Microservices architecture, once held as the gold standard, is facing unprecedented scrutiny and reflection after a decade of fanatical pursuit and large-scale adoption. In today's technical interviews, the tide has turned: interviewers no longer solely assess how to dismantle systems into fragments, but sharply question the necessity of distributed architectures and their hidden costs. This shift is no accident but a collective awakening to the "Distributed Monolith" disaster—in pursuit of theoretical extreme decoupling, countless teams paid a heavy operations "tax" and endured exponential network latency, gaining not the expected agility but a quagmire of debugging and deployment. Against this backdrop, the Modular Monolith returns to center stage as a revisionist architectural solution, leveraging its characteristics of "strict logical isolation, unified physical execution." It is by no means a compromise to traditional legacy code, but an advanced practice utilizing modern toolchains to enforce boundaries at compile time while enjoying local call performance at runtime. For developers in the current FinOps environment of cost reduction and efficiency, deeply understanding this architectural trend is crucial. This concerns not only demonstrating a profound understanding of technical trade-offs in rigorous interviews but also possessing the core ability to escape the trap of blind conformity, building highly maintainable systems with minimal entropy increase based on business lifecycle and team size. Mastering the Modular Monolith is effectively mastering the correct "intermediate state" for a smooth transition from monolith to microservices during system evolution; this is the essential qualification for senior engineers in 2026.

2026 New Architectural Trends: Why the "Modular Monolith" Has Become the Tech World's New Favorite?

In technical interviews in 2026, a distinct trend is emerging: interviewers no longer blindly ask "how to split microservices," but instead start asking, "why does your business scale require microservices?" This shift in direction is no accident; it marks a profound correction in the field of software architecture, moving from "blindly following trends" to a "rational return."

Farewell to "Resume-Driven Development": The Disenchantment with Microservices

Over the past decade, microservice architecture was once seen as the "silver bullet" for solving all scalability issues. However, as time passed, Gartner's Hype Cycle long ago verified the inevitable decline of this trend. By 2026, many teams discovered that what they built was not the loosely coupled, independently deployable microservices of their ideals, but a "Distributed Monolith"—it inherited the tight coupling of monolithic architecture while introducing all the network latency and operational complexity of distributed systems.

As pointed out in DZone's analysis regarding post-monolith architecture trends after 2025, many companies, after experiencing the pain of microservice splitting, have begun to re-examine the value of monolithic architecture. They realized that sacrificing development efficiency and system stability for so-called "scalability" often does more harm than good.

What is a "Modular Monolith"? (Definitely Not a Return to "Spaghetti" Code)

Interviewers advocating for the "Modular Monolith," are by no means encouraging a return to the logically chaotic, fragile traditional legacy systems (Legacy Code). The modular monolith is a highly disciplined architectural style.

Its core characteristics lie in:

• Physical Monolith: The entire application runs as a single deployment unit (Artifact) within a single process. This means no network overhead, no complex distributed transactions (Saga/TCC), and debugging requires only a single breakpoint.
• Logical Microservices: Internally, code is divided into modules through strict boundaries (Boundaries). Modules interact via well-defined public interfaces, and direct referencing of private code across modules is strictly prohibited.

This architecture retains the simplicity of monolithic architecture in development, testing, and deployment, while avoiding code rot through mandatory modular design. Modern toolchains (such as Java's JPMS, Go's Workspace, or specialized architecture guarding tools like ArchUnit) are very mature in 2026, making it possible to enforce module boundaries at compile time.

Why Now? The Rational Return of Engineering Efficiency

The tech community in 2026 places greater emphasis on cost reduction and efficiency enhancement (FinOps) and engineering pragmatism.

1. Considerations of Performance and Latency:
   In a microservices architecture, a simple user request might need to pass through an API Gateway, Auth Service, User Service, etc., causing a single frontend call to balloon into thousands of internal RPC calls. In a modular monolith, these interactions become in-process method calls, which are not only extremely fast but also type-safe, completely eliminating network latency and serialization overhead.
2. Infrastructure Costs:
   Martin Fowler once warned, "don't even consider microservices unless you have a system that's too complex to manage as a monolith." For most startups or mid-sized projects, maintaining a Kubernetes cluster containing Service Mesh, distributed tracing, and dozens of CI/CD pipelines involves staggering "invisible taxes" in terms of manpower and cloud costs.
3. The Collapse and Repair of Developer Experience (DX):
   Developers are fed up with processes that require coordinating three teams and updating four repositories just to modify a single field. The modular monolith makes "refactoring" easy; IDE intelligent code completion can cross module boundaries, and integration tests no longer require spinning up a dozen Docker containers.

Therefore, when you hear questions about "modular monoliths" in your current interviews, what the interviewer is truly assessing is not your nostalgia for retro technology, but whether you possess the ability to balance architectural complexity according to the business stage. They are looking for engineers who know how to use the simplest architecture to solve problems and have the capability to split the system only when it truly needs to scale, rather than architects who can only pile up technical buzzwords.

Calculating the Cost: The Invisible "Tax" of Microservices Architecture (The Microservices Tax)

In technical interviews in 2026, when interviewers ask "why not go directly with microservices," what they expect is no longer a textbook recitation of "decoupling" and "scalability," but a deep understanding of hidden architectural costs. This cost is commonly referred to as the "Microservices Tax"—the extra overhead teams must pay to keep a distributed architecture running smoothly, an overhead that is often unrelated to the delivery of business features.

When adopting microservices early on, many teams only see the flexibility of independent deployment, ignoring the accompanying "distributed premium." As Martin Fowler once warned, unless a system is too complex to be managed as a monolith, one should not lightly consider microservices. This "tax" is not only reflected in monthly cloud bills but is also deeply embedded in development efficiency, debugging difficulty, and the fragmentation of organizational structure.

In this chapter, we will set aside abstract theories and quantify just how heavy this "tax" is from two dimensions: operations infrastructure and team cognitive load. We will also explore why, in today's climate where cost reduction and efficiency improvement are the main themes, this expense often becomes the final straw that breaks small and medium-sized teams.

Operational Costs and Infrastructure Complexity

In technical interviews in 2026, when interviewers ask "why should one be cautious about using microservices," they usually expect not a textbook definition, but a deep understanding of Total Cost of Ownership (TCO). As FinOps (cloud cost optimization) becomes a core focus for enterprises, the infrastructure premium brought by microservices architectures can no longer be easily ignored.

1. The "Inflation" of the Infrastructure Footprint

Microservices architecture essentially swaps code complexity for operational complexity. This exchange is particularly evident in infrastructure bills. For a medium-sized system, migrating from a monolith to microservices usually means an exponential increase in infrastructure costs, rather than a linear one.

We can compare the resource consumption of the two architectures in a typical 2026 production environment:

According to architecture comparison data circulating in the industry, for a system with millions of users, the monthly infrastructure cost for an optimized monolithic architecture might be only around $3,000, whereas a microservices architecture of the same scale often reaches $12,000. This 4x price difference mainly stems from waste in fragmented resource reservation and expensive cross-service communication costs.

2. Debugging Difficulty: From "Checking Logs" to "Distributed Detective"

Interviewers often test with this scenario: "A user reports an order timeout; how do you troubleshoot it?"

• In a monolithic architecture: You only need to check the error logs of a single service and can restore the complete call stack via a thread ID or request ID. Logic jumps are function calls, and the IDE Debugger is readily available.
• In a microservices architecture: This becomes a cross-department detective game. A single frontend request might split into thousands of RPC calls in the backend (like the dilemma DoorDash faced). You need to:

1. 1. Rely on distributed tracing systems (such as Jaeger or SkyWalking) to pinpoint which link slowed down.
   2. Troubleshoot whether it is a K8s Ingress configuration error, DNS resolution latency, or if the Service Mesh Sidecar proxy caused an extra 50ms of latency.
   3. Face "local development hell": To reproduce a bug, you might need to launch 14 Docker containers locally, causing the development machine to run out of memory (starting at 32GB), or be forced to rely on a remote debugging environment.

3. The Invisible Overhead of Operational Manpower

Beyond machine costs, manpower costs are even more staggering. Maintaining a microservices architecture usually requires a dedicated Platform Engineering team.

Do the math:
If your microservices architecture introduces 40 independent services, each service requires an independent CI/CD pipeline, an independent monitoring dashboard, and independent alerting strategies.
Relevant analysis points out that for logging services (Logging) alone, if calculated at a SaaS fee of $100 per service per month, this item alone adds$4,000 in monthly expenditure. Not to mention that to keep this system running, you may need to hire an additional 2-4 high-paid DevOps or platform engineers; this annual manpower cost ($140k-$360k/person) often far exceeds the server costs themselves.

Therefore, architectural decisions in 2026 are no longer about blindly pursuing "splitting," but returning to rationality: If your team size is insufficient to support the extra platform operational manpower, then introducing microservices prematurely is actively increasing "technical debt".

Distributed Transactions and the Data Consistency Nightmare

After the honeymoon phase of microservices architecture, most engineering teams hit a thick wall: Distributed Transactions. This is the most painful and easily underestimated "invisible tax" developers face when migrating from a monolith to microservices.

The Cost of Losing ACID

In a monolithic application, maintaining data consistency usually only requires a single @Transactional annotation. The database's ACID (Atomicity, Consistency, Isolation, Durability) properties ensure that if order creation fails, the inventory deduction is automatically rolled back.

However, once you split the business into independently deployed microservices (e.g., OrderService and InventoryService), each with its own independent database, the original database-level transactions instantly become invalid. To ensure data consistency, you are forced to introduce complex compensation mechanisms, such as the Saga Pattern (Orchestration or Choreography) or TCC (Try-Confirm-Cancel).

This is not just an increase in the amount of code, but an exponential explosion in logical complexity:

• State Machine Bloat: You must handle all intermediate states (Pending, Confirmed, Cancelled, Failed).
• Idempotency Nightmare: When network timeouts cause retries, how do you ensure there are no duplicate deductions or duplicate shipments?
• Debugging Difficulties: A business process spans three services and two message queues; once data becomes inconsistent, troubleshooting the chain is like looking for clues in a maze.

RPC and the Physical Laws of Network Latency

Beyond logical complexity, microservices introduce non-negligible physical overhead. In a modular monolith, inter-module calls are In-process method calls, typically taking nanoseconds or microseconds, and almost never fail.

Whereas in a microservices architecture, this becomes RPC (Remote Procedure Call) or REST requests:

1. Serialization and Deserialization: Data must be packaged into JSON or Protobuf, consuming CPU.
2. Network Hops: Even in the same data center, network latency is in the millisecond range.
3. Unreliability: Network partitions, packet loss, and transient service unavailability become norms that must be handled.

For most systems that have not reached the scale of Google or Netflix, sacrificing the performance and reliability of local calls for so-called "independent scalability" is often a classic case of Over-engineering.

The Return of the Modular Monolith

This is why interviewers in 2026 are starting to advocate for the "Modular Monolith". It is not a simple regression, but a rational return. In a modular monolith, you can isolate business logic through clear code boundaries (as advocated by Spring Modulith), but at runtime, they still run within the same process.

This means you can retain the simplicity of ACID transactions while enjoying the code organization benefits brought by modularity. Only when a specific module (such as a high-frequency trading module) truly encounters a performance bottleneck should it be extracted into an independent service, rather than burdening all business logic with the complexity of a distributed system right from the start of the project.

Practical Guide: How to Build a Modern "Modular Monolith"

In the technical context of 2026, when we talk about a "Monolith," we are certainly not referring to the "Big Ball of Mud" that terrifies developers. Traditional monolithic architectures are difficult to maintain not necessarily because of the sheer volume of code, but because of the lack of physical boundaries, causing business logic to become entangled like spaghetti—where any simple modification can trigger a butterfly effect, leading to system collapse.

The modern "Modular Monolith" aims to solve this pain point. It retains the simplicity of monolithic architecture in deployment, testing, and transaction management, while introducing the core advantages of microservice architecture: high cohesion, low coupling, and strict boundaries.

Building a successful modular monolith is not just about stuffing all code into a single Maven or Gradle project; it requires establishing several core technical pillars to enforce modularity and prevent system degradation:

1. Logical Boundaries as Physical Boundaries: Instead of relying on developer self-discipline, tools (such as Spring Modulith or ArchUnit) are used to enforce module boundaries at compile time. If the Order module attempts to directly call the internal implementation classes of the Inventory module, the build should fail immediately, just as microservices cannot access each other's private databases.
2. Explicit Public APIs: Each module must clearly define its "Named Interface." The internal domain logic and persistence layer implementation of a module must be invisible to other modules. This encapsulation is the essential difference between a modular monolith and a traditional monolith.
3. In-Process Event-Driven Architecture: To decouple dependencies between modules, modular monoliths widely adopt Domain Events. Unlike microservices that rely on Kafka or RabbitMQ for network communication, modular monoliths can efficiently handle event publishing and subscription in memory, maintaining module independence while avoiding the latency and complexity of distributed systems.

The following sections will dive deep into the code level, demonstrating how to translate these concepts into actionable engineering practices through specific directory structures and toolchains.

Code Structure: Shifting from "Layered Architecture" to "Business Domain Partitioning"

In 2026 technical interviews, presenting a project structure with controller, service, and dao packages in the root directory is often viewed as an "Architectural Smell." Although this traditional horizontal layering (Technical Layering) technically achieves separation of concerns, it leads to high maintenance costs during business evolution—modifying a single "Order" feature requires developers to jump repeatedly between three different packages.

Modern Modular Monoliths require us to adopt Vertical Slicing, organizing code by business domain (Feature/Domain). This structure maps directly to the Bounded Contexts of Domain-Driven Design (DDD), forcing the physical code structure to serve business boundaries rather than technical components.

Recommended Directory Structure Design

A mature modular monolith project should embody the "Module as a Service" philosophy. Below is a recommended structure consistent with Spring Modulith or modern architectural styles:

src/main/java/com/company/app
├── Application.java          // Bootstrap class
└── modules                   // Modules root directory
    ├── inventory             // [Inventory Module]
    │   ├── InventoryApi.java // Externally exposed interface (Public)
    │   └── internal          // Internal implementation details (Package-Private)
    │       ├── InventoryController.java
    │       ├── InventoryService.java
    │       └── InventoryRepository.java
    ├── order                 // [Order Module]
    │   ├── OrderApi.java
    │   ├── OrderDto.java
    │   └── internal
    │       └── ... 
    └── payment               // [Payment Module]
        └── ...

In this structure, visibility control is core. Only classes located in the top-level package of a module (such as InventoryApi) are public, while all business logic (Controller, Service, Repository) located under the internal sub-package should be set to package-private. This simulates the "network firewall" of microservices at the compilation level, preventing other modules from arbitrarily calling internal implementation classes, thereby preventing code coupling from spiraling out of control over time.

Beware of the Cyclic Dependency Trap

The biggest enemy of the modular monolith is Cyclic Dependency. For example, the Order module depends on the Inventory module to query stock, while the Inventory module inversely depends on the Order module to update status. This A -> B -> A reference relationship will quickly blur module boundaries, eventually degenerating into an unmaintainable "Big Ball of Mud."

In interviews or actual practice, the importance of introducing architectural guarding tests must be emphasized. Modern toolchains (such as ArchUnit or Spring Modulith's verification mechanisms) allow us to block violating dependencies during the unit test phase. As emphasized in relevant technical practices, by running modules.verify(), any illegal cross-module calls or circular references can be automatically detected and reported as errors during the build phase, ensuring architectural rules are strictly enforced just like compilation errors.

Design Principles Summary:

• Default Private: Unless explicitly required to be exposed, all classes are non-public by default.
• Unidirectional Dependency: Upper-level modules can depend on lower-level modules; communication between modules on the same level should be decoupled via events (Event-Driven) rather than direct method calls.
• Physical Isolation: The physical structure of code folders must reflect the logical boundaries of the business.

Tools and Boundary Enforcement: Spring Modulith and ArchUnit

When discussing modular monoliths in interviews, the most frequently challenged point is: "Over time, the monolith will inevitably degrade into a 'Big Ball of Mud' due to arbitrary references by developers." In 2026, the most powerful weapon to refute this view is no longer "team discipline," but code-level mandatory boundary tools.

Modern architecture emphasizes physically blocking illegal inter-module calls through toolchains, incorporating architectural constraints into the Build process. The following are two mainstream implementation paradigms.

1. Framework-Level Constraints: Spring Modulith

For the Java ecosystem, Spring Modulith has become the standard library for building modular monoliths. It does not introduce the network overhead of microservices, but rather introduces the "sense of boundaries" of microservices.

Spring Modulith treats top-level packages under the application's main package as independent "modules" by default. It introduces the concept of ApplicationModule, allowing developers to explicitly define which classes are the module's public API and which are internal implementations (Internal).

• Mechanism: If the Order module attempts to reference a class marked as internal in the Inventory module, the compiler or runtime (depending on configuration) will report an error directly.
• Visualization: It can also automatically generate C4 architecture diagrams based on the code structure, ensuring that documentation and code are always synchronized, solving the pain point where "architecture diagrams only exist in the Wiki."

2. Test-Level Circuit Breaking: ArchUnit

If your technology stack lacks a framework like Spring Modulith, ArchUnit serves as a universal "architecture gatekeeper." It allows you to write architecture rules using unit test code. Once someone violates the rules (for example, calling the database implementation layer from the business logic layer), the test steps in the CI/CD pipeline will fail, thereby "breaking" (circuit breaking) the build.

Scenario Example:
Suppose you have an e-commerce system containing two modules: Pricing and Shipping. According to the design, Pricing should not directly depend on the internal implementation details of Shipping.

ArchUnit Test Code Example (Java):

@AnalyzeClasses(packages = "com.myapp")
public class ArchitectureTest {

@ArchTest
    static final ArchRule pricingshouldnotdependonshippinginternals =
        noClasses()
            .that()
            .resideInAPackage("..pricing..")
            .should()
            .dependOnClassesThat()
            .resideInAPackage("..shipping.internal..");

@ArchTest
    static final ArchRule cycles_check =
        slices()
            .matching("com.myapp.(*)..")
            .should()
            .beFreeOfCycles();
}

Code Analysis:

• Define Boundaries: Clearly divide module scopes via package name paths.
• Block Dependencies: The first rule enforces that any class under the pricing package cannot import classes under the shipping.internal package. If a newly hired engineer directly imports an internal utility class from the logistics module to save trouble, the test will directly turn red when submitting code.
• Cycle Dependency Detection: The second rule automatically scans all modules to prevent A -> B -> A circular dependencies, which is one of the main culprits making monoliths difficult to maintain.

3. Expansion to Multi-Language Ecosystems

This concept of "Test as Architecture" covers mainstream languages in 2026:

• TypeScript/Node.js: Use dependency-cruiser or eslint-plugin-boundaries to intercept illegal cross-module references during the Lint stage (e.g., prohibiting feature-a from importing feature-b).
• Go: Utilize go-arch-lint to define strict package import rules, ensuring layered unidirectional dependencies.

Core Conclusion: The success of a modular monolith relies not on the self-discipline of developers, but on the ruthlessness of tools. In an interview, demonstrating how you configure these tools to "physically" isolate modules can directly prove that you possess practical experience in governing large-scale monolithic codebases.

Decision Tree: The Golden Rule for Architecture Selection in 2026

In 2026, architecture selection is no longer a debate about "which technology is cooler," but a strategic game based on constraints. Blindly pursuing microservices architecture often leads to the consequences of "Resume Driven Development," ultimately leaving the team with high infrastructure bills and a distributed quagmire that is difficult to debug.

The golden rule for building a sustainable system lies in honestly assessing the team's cognitive load and the true complexity of the business. As SoftwareSeni's architecture evaluation framework points out, technical superiority alone does not determine success or failure; organizational context is the key to determining whether an architecture can survive.

When making decisions, we should introduce a multi-dimensional evaluation model, focusing mainly on the following three core variables:

1. Team Size and Organizational Structure (Conway's Law)
   Microservices are essentially designed to solve organizational scaling problems, not just technical scaling problems. Splitting only yields positive returns when a monolith's codebase becomes so large that multiple teams collaborating on the same codebase generate huge communication friction.
2. Domain Complexity
   If a team cannot clarify module boundaries within a monolithic application, splitting it into microservices will only result in a "distributed monolith"—retaining the coupling of the monolith while adding the network latency and operational difficulty of a distributed system. Matthias Patzak emphasizes in his analysis that in the short term, modular monoliths have an overwhelming advantage in development speed and refactoring flexibility; the long-term value of microservices only becomes apparent when business boundaries are extremely clear and require independent evolution.
3. Scalability Requirements
   It is necessary to distinguish between "throughput scaling" and "functional iteration scaling." Most performance issues can be solved by horizontally scaling the monolith (Running multiple instances) or database optimization. Physical splitting is a necessary measure only when there is a severe conflict in resource demands (e.g., CPU-intensive vs. IO-intensive) between different modules.

The core of this decision tree lies in: Default to a modular monolith unless there is solid evidence that the benefits of microservices cover their high operational tax.

Thresholds for Team Size and Business Stage

In architectural decisions for 2026, abandoning the vanity metric of "microservices for the sake of microservices" and returning to the alignment between organizational structure and business stage is a consensus among all technical leaders. Microservices essentially exist to solve collaboration bottlenecks brought about by organizational expansion, rather than purely technical scalability issues.

1. The "Golden Ratio" of Team Size

Based on engineering practices and data models accumulated within the industry, we can define clear team size thresholds as the first filter for architectural selection:

• < 20 Person Engineering Team: Stick to Modular Monoliths
  For teams with fewer than 20 people (approximately 2-3 "two-pizza" teams), monolithic architecture holds an absolute advantage in efficiency. Introducing microservices at this stage brings a significant "distributed premium": you need to invest a vast amount of energy in service discovery, distributed transactions, and cross-service debugging, rather than business logic.
• 20 - 50 People: The "Gray Area" of Architectural Decisions
  This stage depends on the complexity of the business domain. If the Bounded Contexts are extremely clear and independent (e.g., independent e-commerce and independent logistics systems), splitting can be considered. However, if the business is highly coupled, continuing to reinforce monolith boundaries through tools like Spring Modulith is usually the choice with a higher ROI.
• > 50 People: The Benefits of Microservices Begin to Emerge
  When the team size exceeds 50 people, and merge conflicts in the monolithic codebase and deployment queues begin to seriously slow down delivery speed, the "independent deployment" feature of microservices truly translates into productivity.

2. "Monolith First" Strategy and Cost Reality

Martin Fowler's "Monolith First" strategy remains a golden rule in 2026. The cost of premature splitting is staggering. Comparative data indicates that there is a huge difference in infrastructure costs and delivery speed between a well-optimized monolithic system and a typical microservices architecture in the early stages:

• Infrastructure Costs: A monolithic architecture might only require $3K per month in resource costs, whereas microservices, to maintain the same throughput, often see costs soar to over$12K due to complex sidecars, gateways, and multiple database instances.
• Delivery Speed: In a monolith, adding a cross-module field might take only 2 days; under a microservices architecture, involving 3 services, API gateway updates, and coordinated deployment, the cycle can stretch to 2 weeks.

3. Decision Comparison Table: When to Switch?

In interviews or architectural reviews, the following comparison table can be used to judge whether the critical point for switching architectures has been reached:

Core Conclusion: Microservices solve people problems (communication costs, cognitive load, allocation of rights and responsibilities), not machine problems. If your team has not yet grown large enough to be unable to work due to "stepping on each other's toes," then the modular monolith remains the most competitive technical choice in 2026.

Interview Strategy: How to Demonstrate "Architect Mindset"

In 2026 tech interviews, when interviewers throw out the question "Microservices or Monolith?", they are often not looking for a recitation of standard definitions, but are testing your technical decision-making maturity. This is usually a trap question: blindly advocating for microservices can appear to lack cost consciousness, while sticking to a monolith without reason can be misjudged as technically conservative.

To demonstrate an "Architect Mindset", you need to step out of the "black and white" binary opposition and construct your answer from three dimensions: business stage, team scale, and evolution strategy.

Reject "Junior" Answers, Build a "Senior" Perspective

The biggest difference between junior engineers and senior architects is that the former focuses on technology itself, while the latter focuses on the fit between technology and business.

Core Strategy: Advocate for "Evolutionary Architecture"

The core of architect thinking is embracing change. The most perfect answer usually revolves around "evolution": Start with a modular monolith, and split gradually as the business grows.

You can organize the logic of your answer like this:

1. Start with "Organizational Structure" (Conway's Law)
   First, ask or assume the context: "How big is our team?". If the team is only 5-10 people, forcing microservices will lead to the "Microservice Premium"—meaning the cost of maintaining a distributed system far exceeds its benefits. Citing Matthias Patzak's view, team structure will shape your architecture; in the startup phase, a modular monolith allows a small team to focus on business logic rather than infrastructure.
2. Demonstrate "Modular" Governance Capabilities
   Emphasize that a monolith does not equal a "Big Ball of Mud". You can mention using DDD (Domain-Driven Design) within the monolith to define Bounded Contexts, and enforcing dependency rules via tools (like ArchUnit or language-specific module systems). This proves to the interviewer: You have the ability to write high-cohesion, low-coupling code within a monolith, laying the foundation for future splitting.
3. Define Triggers for "Splitting"
   Don't say "split for the sake of splitting"; provide specific trigger metrics. For example:

• • Independent Scaling Needs: A specific module (like image processing) consumes extreme resources and needs independent scaling.
  • Release Frequency Differences: The core transaction link requires stability, while the marketing campaign module releases three times a day; their release rhythms conflict.
  • Team Cognitive Boundaries: When the monolith code volume exceeds what a "two-pizza team" can fully understand.

1. Mention Migration Strategies
   Mention that progressive migration (like the Strangler Fig pattern) is more robust than "starting over". An architect is responsible not just for drawing diagrams, but for "execution". Describe how to use the old system as a proxy to gradually shift traffic to new services, demonstrating your control over risk management.

Suggested Closing Statement

At the end of the interview, you can summarize your position with a powerful statement to deepen the impression:

"As an architect, I lean towards a 'Monolith First' strategy. During the business validation phase, a modular monolith can win the 'sprint' with the lowest latency and infrastructure costs. Only when business scale grows to the point where the monolith cannot support it, or organizational expansion leads to excessive communication costs, should we pay the 'complexity tax' of microservices in exchange for scalability. Architecture is not static; it grows along with the business."

This answer not only demonstrates technical depth but also reflects your pragmatic attitude of saving money and improving efficiency for the company—qualities most desired by enterprises in 2026.