balanced-coupling

The Balanced Coupling Model

A comprehensive reference for understanding the Balanced Coupling model as described by Vlad Khononov. This document synthesizes the full model from the companion blog at coupling.dev, covering its foundations, dimensions, balancing mechanics, relationship to prior coupling models, and connections to domain-driven design.

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1. Foundations: Why Coupling Matters

1.1 Coupling Is Not the Enemy

The term "coupling" has long been synonymous with bad design. The balanced coupling model challenges this assumption. According to the dictionary, coupling is simply a device for connecting parts. If a system is a set of components interacting to achieve a goal, then coupling is what connects those components and makes it possible to achieve those goals. Coupling is what makes the value of a system greater than the sum of its parts.

You cannot build a system out of fully independent components — that would go against the very definition of "system." The question is not whether to couple, but how to couple. Some forms of coupling lead to modularity; others create complexity. The balanced coupling model treats coupling not as a nuisance to eliminate but as a design tool to wield deliberately.

1.2 Complexity (via Cynefin)

The model adopts the Cynefin framework's definition of complexity:

• Clear (Simple): You make a change and know exactly what the outcome will be.
• Complicated: You don't know the outcome, but an expert does and you can consult them.
• Complex: The only way to determine the outcome is to make the change and observe what happens. Cause and effect can only be identified in hindsight.
• Chaotic: No identifiable relationship between actions and outcomes, even in hindsight.

In software design, complexity means a system where the outcomes of changes are unpredictable. The goal of modular design is to avoid situations where the effect of a change can only be identified in retrospect.

Crucially, complexity is subjective — it is a function of both system design and our cognitive limits:

complexity = f(system design, cognitive limits)

Studies suggest humans can hold only 4 +/- 1 units of information in working memory simultaneously. When the information required to make a change exceeds this cognitive capacity, the design is complex.

1.3 Modularity: The Opposite of Complexity

If complexity makes change outcomes unpredictable, modularity is its opposite. A modular design satisfies two criteria:

1. It is clear what parts of the system need to change.
2. The outcome of the change is clear and predictable.

Modularity extends a system's goals into the future. It doesn't mean preemptively implementing future requirements — that's impossible. It means making the implementation of reasonable future changes easy by minimizing the cognitive load required.

A system is a set of components interacting to achieve goals. Even a "big ball of mud" can achieve its current goals. Modular design extends the system's goals by ensuring it can continue to achieve goals as they evolve.

1.4 The Shared Cause

Complexity and modularity are contrasting concepts driven by the same fundamental cause: coupling. The way components are connected determines whether the system is modular or complex.

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2. The Three Dimensions of Coupling

When two components are coupled, the connection between them — not the components themselves — is what determines modularity or complexity. The balanced coupling model evaluates this connection along three dimensions: integration strength, distance, and volatility.

2.1 Integration Strength (Knowledge)

Integration requires components to exchange knowledge about each other. The more knowledge is shared, the higher the probability that a change in one component will trigger cascading changes in others.

Rather than trying to "measure" knowledge quantitatively, the model categorizes types of knowledge. Each type involves significantly different amounts of shared knowledge. This is the Integration Strength model, which identifies four levels (from strongest/most knowledge to weakest/least):

Intrusive Coupling (Strongest)

Occurs when private interfaces or implementation details are used for integration — internal databases, private objects, undocumented APIs. We must assume all knowledge about a component's implementation is shared. Intrusive coupling is both fragile and implicit — the authors of the "intruded" component may not even know the integration exists.

Corresponds to content coupling (pathological coupling) in the classic module coupling model.

Functional Coupling

Shifts focus from how a component is implemented to what it implements: its functional requirements. Occurs when multiple components share knowledge of their business requirements and must change together when those requirements evolve.

An extreme case is duplicated knowledge — the same business rule implemented in both frontend and backend. If a specification change isn't reflected in both simultaneously, the system enters an inconsistent state.

Like intrusive coupling, functional coupling can be implicit: duplicated business logic in components that are not explicitly coupled yet still need to change together.

Corresponds to common, external, and control coupling in the classic module coupling model.

Model Coupling

Occurs when components share knowledge of a business domain model. If the model changes — due to new domain insights — all coupled components must change accordingly.

Domain-driven design emphasizes working with multiple specialized models rather than one massive all-encompassing model. Model coupling arises when components share one of these models.

Corresponds to stamp coupling in the classic module coupling model.

Contract Coupling (Weakest)

The lowest level of shared knowledge. An integration contract encapsulates implementation details, functional requirements, and business models, making integration explicit and stable.

Design patterns that introduce contracts include: Facade, Open-host service / Published language (DDD), Anti-corruption layer (DDD), Data transfer objects (DTOs).

Corresponds to (and refines) data coupling in the classic module coupling model, distinguishing between internal models and explicit integration contracts.

The Implicit-Explicit Spectrum

The four levels of integration strength also reflect how implicit or explicit the shared knowledge is:

• Contract coupling = most explicit
• Model coupling = explicit but broader
• Functional coupling = can be implicit
• Intrusive coupling = most implicit and fragile

Degrees of Integration Strength

Within each level, finer-grained comparisons are possible using the levels of connascence:

• Static connascence levels (name, type, meaning, algorithm, position) can compare degrees of contract and model coupling.
• Dynamic connascence levels (execution, timing, value, identity) can compare degrees of functional coupling.

2.2 Distance

The physical and logical distance between coupled components affects the cost of change. It is significantly easier to co-evolve two methods in the same file than two objects in different microservices or separate systems.

Distance increases through levels of abstraction: methods -> objects -> namespaces/packages -> (micro)services -> systems. At each level, the cognitive effort and coordination cost of simultaneous change increases.

Distance Is Relative to the Observed Level of Abstraction

Distance is not an absolute measure — it is relative to the level of abstraction being analyzed. The model is fractal: the same balance rule applies whether you are looking at methods within a class, classes within a module, modules within a service, or services within a system.

At any given level of abstraction, the highest distance is the boundary of that level. If a project consists of a single service with multiple modules, then the distance between those modules is the highest distance in that context. The same principles apply: if two modules share high integration strength, that coupling is unbalanced — even though, from a system-wide perspective, they live in the same service.

You do not need microservices or distributed systems to have high-distance coupling problems. A single-person project with a single deployable unit can still suffer from tight coupling between its modules, because at the module level of abstraction, cross-module integration is the highest distance available.

Lifecycle Coupling (The Counterforce)

Opposing cost of change is lifecycle coupling: the closer components are, the more tightly their lifecycles are bound — they must be tested and deployed together. Many "monolith to microservices" refactorings were motivated primarily by the desire to reduce lifecycle dependencies.

Distance is therefore a trade-off: lower distance means cheaper co-evolution but tighter lifecycle coupling; higher distance means more independent lifecycles but more expensive co-evolution.

Distance Is Socio-Technical

Distance is not purely technical. The organizational structure significantly affects it:

• Two microservices implemented by the same team have lower effective distance than two microservices implemented by different teams, even if the code structure is identical.
• When multiple teams must coordinate changes, the cognitive effort and collaboration overhead increase substantially.

This reflects Conway's Law: the organizational structure shapes the system's architecture.

Runtime Coupling

Runtime dependencies between components also affect distance. Synchronous integration creates tighter lifecycle coupling than asynchronous integration. A tight runtime dependency binds components' lifecycles together — a failure in one can cascade to the other.

Reducing runtime coupling (e.g., via asynchronous messaging) increases the effective distance between components. To keep coupling balanced, this increased distance should be compensated by reducing the integration strength.

2.3 Volatility

Integration strength reflects the likelihood of a change propagating across boundaries. Distance determines the cost of a cascading change. Volatility — the third dimension — reflects the probability of a component needing to change in the first place.

The higher the volatility, the more acute and "painful" any design issues will be. A component with high integration strength and high distance that never changes causes no practical problems. But the same component changing frequently is a constant source of pain.

Evaluating Volatility with DDD's Subdomains

Predicting the future is impossible, but domain-driven design's concept of subdomains provides useful heuristics:

• Core subdomains (competitive advantage, "interesting problems") -> highest volatility. Companies continuously optimize and improve them.
• Supporting subdomains (no competitive advantage, no ready-made solutions, "boring problems") -> low volatility. Typically CRUD data entry or ETL processes.
• Generic subdomains (solved problems, off-the-shelf solutions) -> low functional volatility, variable implementation volatility. The functionality itself is mature and rarely changes — it's a solved problem. However, the implementation may be volatile: switching providers, adopting a different technology, or running multiple implementations in parallel are all realistic change vectors. For example, a TTS engine (AWS Polly) is generic functionality, but switching to Google TTS, ElevenLabs, or a self-hosted solution is a reasonable expectation. In such cases, strong integration contracts are essential to encapsulate provider-specific knowledge. Conversely, some generic subdomain implementations are inherently sticky — choosing Auth0 for identity and access means switching to Clerk would be a significant event regardless of contract design. When designing boundaries for generic subdomains, evaluate the probability of switching implementations or using multiple ones simultaneously, and enforce integration contracts accordingly.

The Wardley Maps commoditization axis provides another perspective on volatility.

Essential vs. Accidental (In)Volatility

A critical distinction:

• Accidental volatility: Frequent commits don't necessarily mean the business domain is volatile. Poor design — ineffective management of integration strength and distance — can cause a component to change frequently.
• Accidental involatility: A seemingly stable component may not be inherently stable. The business may want to change it, but the high risk and effort of modifying a poorly designed system prevents them from doing so.

Therefore, volatility should be evaluated from the business domain perspective (essential volatility), not from commit history alone.

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3. Fractal Geometry

Software design is fractal. You design components and their interactions at different levels of abstraction — from objects to modules to services to interactions across entire systems. The balanced coupling model applies at all of these levels.

However, the meaning of the integration strength levels shifts depending on the level of abstraction you are working at. What constitutes an integration contract, an implementation model, or a functional requirement is different at each level. An integration contract at a lower level of abstraction (e.g., a class's public interface) may be an implementation detail at a much higher level (e.g., within a service boundary). Similarly, a model shared between two objects inside the same module is model coupling at the object level, but from the system level it is entirely internal to the module — invisible and irrelevant.

The same applies to distance. At any given level of abstraction, the highest distance is the boundary of that level. If you are designing modules within a single service, the cross-module boundary is your highest distance — even though from a system perspective those modules are co-located. If you are designing interactions between systems, the cross-system boundary is the highest distance.

Therefore, when applying the model you must always establish which level of abstraction you are operating at. This determines:

• Integration strength: What counts as a contract, a model, a functional dependency, or an intrusive dependency at this level.
• Distance: What constitutes the lowest and highest distance at this level.

Without this framing, the same coupling relationship can appear balanced or unbalanced depending on the observer's perspective.

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4. Balancing the Dimensions

4.1The Four Extreme Combinations (Strength x Distance)

The interplay of integration strength and distance can be understood through their extreme combinations:

	Low Distance	High Distance
Low Strength	Low Cohesion (Complexity) — Unrelated components located close together. Increases cognitive load. Drifts toward big ball of mud.	Loose Coupling (Modularity) — Low strength minimizes cascading changes. High distance is balanced.
High Strength	High Cohesion (Modularity) — Components must co-evolve, but low distance makes cascading changes cheap.	Tight Coupling (Complexity) — Frequent cascading changes (high strength) that are expensive to implement (high distance). A step toward a distributed monolith.

4.2The Balance Rule

The key insight: modularity emerges when integration strength and distance balance each other (one is high, the other is low). Complexity emerges when they are equal (both high or both low).

Expressed as binary logic:

MODULARITY = STRENGTH XOR DISTANCE
COMPLEXITY = STRENGTH AND DISTANCE

This explains why "loose coupling" and "high cohesion" are both desirable — they are both forms of balanced coupling, just with different dimension values.

4.3Pragmatic Balance (Adding Volatility)

Eric Evans observed that "not all of a large system will be well designed." The dimension of volatility introduces pragmatism.

If coupling is unbalanced (high strength + high distance) but the component is not expected to change, the effects are neutralized by low volatility. A legacy system with high intrusive coupling that never evolves is tolerable — the coupling is technically "bad" but practically harmless.

The complete balance expression:

BALANCE = (STRENGTH XOR DISTANCE) OR NOT VOLATILITY

This means coupling is balanced when either:

• Strength and distance counterbalance each other (XOR), OR
• The component has low volatility (regardless of strength/distance relationship).

Note: The blog uses a simplified binary model for illustration. The book Balancing Coupling in Software Design contains a more elaborate mathematical model with continuous values rather than binary extremes.

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5. Connection to Domain-Driven Design

The balanced coupling model has deep connections to DDD. Many DDD patterns and practices directly relate to the dimensions of coupling:

Strategic Design

• Bounded contexts define physical boundaries (medium distance, shared lifecycle) for components that share a model of the business domain (model coupling).
• Bounded context integration patterns (shared kernel, partnership, conformist, anti-corruption layer, open-host service, separate ways) determine the level of knowledge sharing. Ad-hoc patterns allow more knowledge exchange; formal patterns reduce it. This reflects both the integration strength and the organizational aspect of distance.
• Some integration patterns (anti-corruption layer, open-host service) encapsulate internal models behind integration-specific contracts (contract coupling), used when high distance exists.

Tactical Design

• Aggregate and value object patterns co-locate (low distance) behaviors sharing transactional boundaries (high degree of functional coupling). This is balanced: high strength is offset by low distance.
• Business logic implementation patterns (transaction script, active record, domain model, event-sourced domain model) are chosen based on domain complexity — which directly relates to volatility.

Subdomains and Volatility

• Core subdomains = highest volatility -> require the most careful coupling management
• Supporting subdomains = low volatility -> DDD explicitly allows pragmatic shortcuts here ("Not all of a large system will be well-designed" — Eric Evans), because low volatility neutralizes unbalanced coupling
• Generic subdomains = low functional volatility (the problem is solved), but implementation volatility varies. When the probability of switching providers or using multiple implementations is high (e.g., TTS engines, payment gateways, notification services), enforce strong integration contracts to encapsulate provider-specific knowledge. When switching cost is inherently high and unlikely (e.g., identity providers, core databases), pragmatic shortcuts are acceptable — but verify this assumption and brainstorm plausible change vectors before committing

DDD's subdomains are the primary tool for evaluating the volatility dimension of components in the balanced coupling model.

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6. Summary: The Model at a Glance

+-----------------------------------------------------+
|                 BALANCED COUPLING                     |
|                                                       |
|  Three Dimensions:                                    |
|  +---------------------+                              |
|  | Integration Strength | -> Likelihood of cascading  |
|  | (Knowledge shared)   |   changes                   |
|  |                      |                              |
|  | Levels:              |                              |
|  |  Intrusive (highest) |                              |
|  |  Functional          |                              |
|  |  Model               |                              |
|  |  Contract  (lowest)  |                              |
|  +---------------------+                              |
|  +---------------------+                              |
|  | Distance             | -> Cost of cascading         |
|  | (Socio-technical)    |   changes                   |
|  |                      |                              |
|  | Factors:             |                              |
|  |  Code structure      |                              |
|  |  Org structure       |                              |
|  |  Runtime deps        |                              |
|  +---------------------+                              |
|  +---------------------+                              |
|  | Volatility           | -> Probability of change     |
|  | (Rate of change)     |   occurring at all           |
|  |                      |                              |
|  | Evaluated via:       |                              |
|  |  DDD subdomains      |                              |
|  |  Wardley Maps        |                              |
|  |  (NOT commit history)|                              |
|  +---------------------+                              |
|                                                       |
|  Balance Rule:                                        |
|  MODULARITY = STRENGTH XOR DISTANCE                   |
|  BALANCE = (STRENGTH XOR DISTANCE) OR NOT VOLATILITY  |
|                                                       |
|  Key Insight:                                         |
|  Loose coupling and high cohesion are BOTH forms of   |
|  balanced coupling, not separate concepts.             |
+-----------------------------------------------------+

The Core Principles

1. Coupling is not inherently bad. It is what makes systems work. The goal is to balance it, not eliminate it.
2. Coupling manifests in three dimensions. Evaluating only one (e.g., number of dependencies) misses the full picture.
3. Modularity = balanced dimensions. When integration strength and distance counterbalance each other, the design is modular.
4. Complexity = unbalanced dimensions. When strength and distance are both high (tight coupling / distributed monolith) or both low (low cohesion / big ball of mud), the design is complex.
5. Volatility adds pragmatism. Low volatility can neutralize unbalanced coupling. Not every part of a system needs perfect design.
6. The model is fractal. It applies at every level of abstraction: methods, objects, packages, services, systems.
7. The model is socio-technical. Organizational structure affects distance as much as code structure does.
8. Prior models are incorporated, not replaced. Module coupling provides the foundation for integration strength levels. Connascence provides the degrees within those levels. The balanced coupling model addresses blind spots in both.

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Communicating with the User

Do not assume the user is familiar with domain-driven design terminology. When discussing volatility and subdomain classification, explain the concepts in plain language:

• Core subdomain — the part of the system that gives the business its competitive advantage; the "interesting problems" the organization is actively investing in. Changes frequently.
• Supporting subdomain — something the business needs but that doesn't differentiate it, and no off-the-shelf solution exists. Typically straightforward workflows like data entry, ETL, or internal tooling. Rarely changes.
• Generic subdomain — a solved problem where ready-made solutions exist (authentication, payment processing, email delivery). The functionality is mature, but the specific provider or technology may change.

Use the DDD terms, but always accompany them with a brief plain-language explanation on first use. Do not expect the user to know what "core subdomain" means without context.