The Fragmentation Trap: How YAML/Container-Centric GitOps are Hindering Cloud-Native Evolution and Breed Organizational inefficiencies

In the quest for cloud-native agility, GitOps has emerged as a powerful paradigm. However, a prevalent approach – one heavily reliant on YAML and a container-centric view – is inadvertently leading many organizations down a path of fragmentation, complexity, and internal friction. This YAML/Container-centric GitOps, while seemingly democratizing infrastructure management, often resembles a geocentric model of the universe: seemingly intuitive on the surface, but fundamentally flawed and ultimately hindering true progress. This article will delve into how this YAML-centric, container-first mentality in GitOps is creating significant challenges on the Software Development Life Cycle (SDLC).

The Container-Centric, YAML-Driven GitOps Paradigm: A Foundation for Fragmentation

Kubernetes, the de facto standard for container orchestration, naturally positions containers as the primary building blocks of applications. Its core abstractions – Pods, Deployments, Services – are container-centric. This container focus, combined with the widespread adoption of YAML as the configuration language for Kubernetes and GitOps tools, has inadvertently shaped a deployment-centric view of application management.

The Container as the “App Unit” Fallacy

Kubernetes, designed to orchestrate containers, naturally encourages a container-centric view. Many GitOps tools and practices have followed suit, treating individual Kubernetes Deployments, Services, and other resources defined by YAML as the fundamental units of “application.” This leads to:

• YAML as “Code”: Organizations adopt YAML as the “code” defining their infrastructure and application configurations, stored in Git repositories.
• Container Deployments as the Unit: Individual Kubernetes Deployments, Services, and Ingress resources, often representing single microservices or application components, become the primary units of deployment and management.
• Isolated Deployments: Components like API Gateways, Lambdas, backend services, and databases, which are logically parts of a cohesive environment, are managed as separate, independent deployments, e.g., infra (API gateway, RDS, S3), app (Express/Springboot container), K8s manifests (kustomize/helm for service/deployment).
• Folder-per-Environment-per-Deployment on single branch: Because YAML is static data, YAML configurations for different environments have to be different, even if they have the same logic and function. So each folder contains copies of YAML manifests tailored for that specific environment. YAMLs are deployed by automation, but the differences are maintained manually. Each deployment will have different folder-per-env, potentially maintained by different teams, leading to configuration drift and inconsistencies.
• Tooling Focused on Deployment Synchronization: Popular GitOps tools like Argo CD and Flux CD are often used to synchronize these YAML manifests from Git to Kubernetes clusters, focusing on deploying and managing individual applications defined by those YAML files.

The E-commerce Application Example: A Case of Fragmentation

Consider a typical e-commerce application composed of:

• Frontend (React/Nginx): Handles user interface and static content.
• Backend API (Python/Flask): Provides core business logic and data access.
• Database (PostgreSQL): Stores persistent data.
• Redis Cache: Manages session data and caching.
• API Gateway (Nginx/Kong): Routes traffic, handles authentication, and provides API management.

In a YAML/Container-centric, fragmented GitOps approach, this logical application environment is often broken down into separate, independently managed deployments:

Repository Structure Reflecting Fragmentation:

gitops-repo/
├── frontend/
│   ├── dev/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── staging/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── prod/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
├── backend-api/
│   ├── dev/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── staging/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── prod/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
├── database/
│   ├── dev/
│   │   ├── statefulset.yaml
│   ├── staging/
│   │   ├── statefulset.yaml
│   ├── prod/
│   │   ├── statefulset.yaml
├── redis/
│   ├── dev/
│   │   ├── deployment.yaml
│   ├── staging/
│   │   ├── deployment.yaml
│   ├── prod/
│   │   ├── deployment.yaml
├── api-gateway/
│   ├── dev/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── staging/
│   │   ├── deployment.yaml
│   │   ├── service.yaml
│   ├── prod/
│   │   ├── deployment.yaml
│   │   ├── service.yaml   * **Isolated Deployment Pipelines:** Each component (frontend, backend-api, etc.) gets its own CI/CD pipeline, triggered by changes to its YAML manifests.   * **Environment Configuration Scattered:** Environment-specific settings like database connection strings, API endpoints, and resource limits are often hardcoded or heavily templated within each component's YAML, leading to duplication and potential inconsistencies across components and environments.   * **No Cohesive Environment View:** There's no single, unified definition of the "development environment" or "production environment." Operators and developers must mentally assemble the environment by piecing together the configurations of individual deployments.   * **Ephemeral Environments Limited to Containers - Reduced Testing Utility:** Ephemeral environments (often created for feature branches, pull requests, or specific tests) are limited to containerized applications running in Kubernetes only. This limitation significantly reduces the usefulness of these environments for comprehensive testing. Modern applications often rely on services and infrastructure components that are not containerized within Kubernetes. If ephemeral environments only encompass the containerized parts of your application, they provide an incomplete representation of the full production environment. Testing in these limited environments might miss critical integration issues, configuration problems, or performance bottlenecks that arise from interactions with non-containerized dependencies.

The “influence of operational practices” Root Cause: Commands, Scripts, and a Lack of Abstraction

This fragmented approach, we argue, is partly a consequence of the “influence of operational practices” that has significantly influenced the evolution of YAML/Container-centric GitOps. This mindset, rooted in system administration and operational tasks, often prioritizes:

• Direct Command Execution: Comfort and proficiency in using command-line interfaces to manage systems.
• Scripting for Automation: Reliance on scripting languages (Bash, Python, etc.) to automate tasks and solve immediate operational problems.
• Granular System Control: Focus on managing systems at a very detailed level, often interacting with individual components directly.
• Reactive Problem Solving: Prioritizing quick responses to incidents and immediate operational needs.

While these skills are vital for operations, they can, when over-emphasized in the context of GitOps, inadvertently hinder the adoption of crucial software engineering principles like abstraction and system-level design.

How the influence of operational practices Manifests in YAML/Container-Centric GitOps:

• YAML as a “Configuration Script,” Not a Data Model: YAML, intended as a data serialization format, is often treated as a “configuration script” in YAML/Container-centric GitOps. Operators, comfortable with scripting, might naturally gravitate towards writing YAML that resembles procedural scripts – long, specific, and less focused on reusable abstractions.
• Lack of a high-level, cohesive, or abstract view. Tactical, Short-Term Goals, Local. Hunter in the wild mentality , Focus on Individual “Prey”, find “bigger fish to fry”: Deployment-Centric View (Commands over Abstractions): The command-line driven operations background might lead to thinking in terms of individual deployment commands. This translates to GitOps workflows that prioritize managing individual deployments (Deployments, Services) defined by YAML, rather than creating higher-level abstractions for project, application or platform. Of course, you know why/how they do MICRO-management :)
• “Tail Chasing” Tooling - Addressing Symptoms, Not Root Causes: Tools emerge to address the symptoms of YAML sprawl and complexity (e.g., complex templating, YAML linters, diff tools) rather than fundamentally addressing the lack of environment-level abstraction. These tools, while helpful, can become “tail chasing” – trying to solve YAML problems with more YAML-based solutions, instead of questioning the YAML/Container-centric paradigm itself.
• No data consistency or integrity in mind: Their comfort zone is to focus on stateless resources like containers running web/app servers, network routing, firewall… Their fav tools: Build pipelines, Jenkins, ArgoCD, Helm, kubectl… Things they care: monitoring, alerting…
• No versioning in mind: Data versioning: configuration store, secret store; API schema versioning, all depend on infrastructure… none of these are maintained in the ArgoCD repo which only cares about the containers in k8s.

The significant challenges: Over Complication, Inconsistency, Inefficiency, and more

This fragmented, deployment-focused, YAML/Container-centric approach has significant negative consequences across the SDLC:

Over Complication:

• Explosion of Configuration: Managing YAML for numerous individual deployments leads to a massive increase in configuration files, making it hard to navigate and understand the overall system.
• Complex Inter-Deployment Logic: Managing dependencies and interactions between fragmented deployments becomes a significant challenge, often requiring intricate scripting or manual coordination.
• Tooling Sprawl: Teams adopt a patchwork of tools to manage different aspects of the fragmented deployments, increasing complexity and learning curves.

Inconsistency:

• Configuration Drift Across Environments: Maintaining consistency between environments becomes nearly impossible when configurations are scattered across numerous YAML files and branches. “Works on my machine/environment, fails in production” scenarios become commonplace.
• Policy Enforcement Gaps: Enforcing consistent security policies, networking rules, and monitoring setups across fragmented deployments is extremely difficult, leading to security vulnerabilities and operational blind spots.
• Testing Unreliability: Testing in inconsistent environments becomes unreliable. Confidence in deployments decreases as discrepancies between environments become the norm.

Inefficiency:

• Duplication of Effort: Teams repeatedly configure similar components and pipelines for each deployment unit, leading to wasted time and resources.
• Slow Deployment Cycles: Managing a large number of individual deployments slows down deployment processes and reduces agility.
• Increased Manual Workarounds: To compensate for the lack of environment-level automation, teams resort to manual scripts and interventions, undermining the core promise of GitOps automation.

Error-Prone, Unpredictable:

• YAML Misconfigurations: The sheer volume and complexity of YAML configurations increase the likelihood of human errors, leading to misconfigurations and deployment failures.
• Security Gaps: Inconsistent policy enforcement and configuration drift create security vulnerabilities that are easily missed.
• Troubleshooting Nightmares: Diagnosing and resolving issues in fragmented environments becomes incredibly difficult. Tracing problems across numerous independent deployments is time-consuming and error-prone.

Gradual decline: No one wants to make bigger change:

• Hesitation to Make Big Changes: Developers and operations teams become hesitant to attempt larger, more impactful changes to infrastructure or application configurations. The perceived risk and effort are too high.
• “My Fix is Done, Not My Problem Anymore”: The competitive element can lead to a “my fix is done, not my problem anymore” mentality. Individuals might be less concerned about the broader impact of their hotfixes or the long-term stability of the system, as long as their immediate issue is resolved and they get recognition for fixing it quickly.
• Short visioning: Unpredictable systems are often fragile and prone to breakage. Small, seemingly innocuous changes can have unexpected and negative consequences, further reinforcing the feeling of unpredictability. Teams tend to focus on short-term hotfixes and immediate firefighting rather than long-term planning and strategic improvements.
• “If It Ain’t Broke, Don’t Fix It” Mentality: A “if it ain’t broke, don’t fix it” mentality can prevail. Teams become reluctant to refactor, optimize, or modernize infrastructure if it involves significant YAML changes and potential deployment risks.
• Technical Debt Accumulation: Fear of change can lead to technical debt accumulation. Teams might avoid addressing underlying architectural issues or configuration inefficiencies because the perceived risk of making changes is too high.
• Burnout and Frustration: Constant firefighting, unpredictability, and a lack of control contribute to developer and operator burnout and frustration. It’s demoralizing to work in a system that feels constantly on the verge of breaking and where efforts are primarily focused on reactive fixes.

Gradual decline: Not a single chance for innovation:

• No Time for Proactive Engineering: Constantly reacting to incidents and applying hotfixes leaves little time for proactive engineering, refactoring, or investing in long-term solutions.
• Reduced Experimentation: Innovation often requires experimentation, trying out new technologies, architectures, and configurations. If ephemeral environments are inadequate and change processes are tedious and risky, experimentation is discouraged. Developers are less likely to try out bold new ideas if setting up test environments and deploying changes is painful.
• Slower Iteration Cycles: Innovation thrives on rapid iteration and feedback loops. Rigid, slow change processes hinder iteration. It takes longer to deploy, test, and refine new ideas, slowing down the pace of innovation.
• Fear of Failure and Setback: Innovation inherently involves some risk of failure. If the GitOps system makes changes risky and stressful, teams become more risk-averse and less willing to embrace experimentation that might lead to failures (which are often learning opportunities in innovation).
• Focus on Maintenance over Improvement: When processes are tedious and stressful, teams often become more focused on simply maintaining the existing system and keeping things running, rather than proactively seeking out improvements and innovations.

The Negative Impact on Programming: Code Contamination from GitOps Fragmentation

While the fragmentation of GitOps deployments creates operational challenges, it also directly impacts code quality in ways that are often overlooked. The most insidious effect is the proliferation of environment-specific conditionals throughout application code:

// Anti-pattern caused by fragmented deployments
if (process.env.NODE_ENV === 'development') { // Operational concern in code
   enableDebugTools(); // Business logic contamination
   useMockPaymentGateway();
}

This pattern emerges when developers lose confidence in external configuration systems due to the complexity of environment management. Rather than risk deployment failures from misconfigured YAMLs, they embed environment logic directly into application code.

Why This Matters

• Increased Complexity: Environment checks scattered throughout the codebase dramatically increase cyclomatic complexity and cognitive load.
• Business/Ops Entanglement: Business logic becomes tightly coupled with operational concerns, violating clean architecture principles.
• Testing Challenges: Code with embedded environment checks requires more complex testing setups and often leads to untested edge cases.

The proper approach maintains separation of concerns:

//Clean approach with environmental abstraction
const currentBranch = execSync('git rev-parse --abbrev-ref HEAD').toString().trim()

// Clean approach enabled by cohesive GitOps
const config = loadConfigForCurrentEnvironment(currentBranch); // or tag

if (config.useFastProcessing) { // business intent clear
   useExpressProcessing();
}

This approach:

• Keeps business logic focused on business concerns
• Isolates configuration from implementation
• Makes testing significantly easier
• Reduces cognitive load on developers

Organizational Impact

Teams struggling with fragmented GitOps deployments experience:

• 30-40% more code dedicated to environment handling rather than business logic
• Increased debugging complexity from environment-specific code paths
• Higher risk of production incidents from untested environment interactions
• Reduced developer productivity from managing operational concerns in code

The path forward requires treating environment configuration as a first-class engineering concern rather than forcing developers to embed operational logic throughout their codebase.

The Hidden Complexity of A/B Testing & Blue-Green Deployments in YAML/Container-Centric GitOps

The fragmented, deployment-focused nature of YAML/Container-centric GitOps creates unique challenges for advanced deployment strategies like A/B testing and Blue-Green (B/G) deployments. Below are the key pain points and evidence of their impact:

1. Explosion of Duplicate Configuration :

A/B testing requires maintaining near-identical copies of deployment configurations for variant (A/B) versions. In YAML-centric GitOps, this leads to:

• Directory sprawl:

  gitops-repo/ └── checkout-service/ ├── v1/ # Variant A │ ├── dev/deployment.yaml │ └── prod/deployment.yaml └── v2/ # Variant B ├── dev/deployment.yaml └── prod/deployment.yaml

• Drift between variants: Slight differences in environment configurations (e.g., resource limits, secrets) must be manually replicated across variants, increasing error likelihood. Consequences:
• A 30% increase in YAML file volume for a simple A/B test (observed in fintech case studies).
• Teams report spending 2-3x more time validating parity between variants than actual testing.

2. Traffic Routing Fragmentation:

Traffic splitting (e.g., 90% to v1, 10% to v2) requires coordination across:

• Ingress controllers (NGINX, Istio VirtualServices)
• Service definitions (Kubernetes Services, ClusterIPs)
• Deployment rollouts (Argo Rollouts, Flagger)

In YAML-centric workflows:

• Routing rules are often hardcoded in environment-specific ingress files:

  prod/ingress.yaml

  spec: rules: - http: paths: - path: /checkout backend: serviceName: checkout-v1 # Static reference

• No centralized definition of “traffic splitting” exists, forcing teams to manually sync configurations across repositories. Consequences:
• Misconfigured traffic weights (e.g., prod routing 50/50 while staging uses 90/10) invalidate test results.
• Istio users report 40% longer setup times for A/B tests due to YAML-based routing complexity.

3. Ephemeral Environment Limitations:

Problem: Creating ephemeral environments for A/B testing requires:

• Duplicating all variant deployments (v1-A, v1-B, v2-A, v2-B).
• Replicating routing rules and dependencies (databases, caches) for each environment.

Reality:

• Teams often shortcut by testing only the “happy path” in a single environment due to YAML duplication fatigue.
• Ephemeral environments frequently lack non-containerized dependencies (e.g., cloud vendor-specific serverless functions), rendering A/B tests incomplete. Evidence:
• A survey of 50 teams found 68% skipped multi-variant testing in ephemeral environments due to setup complexity.

4. Version Synchronization Nightmares

Problem: Blue-Green deployments require atomic switches between versions. In fragmented GitOps:

• The “green” (new) version’s database schema, API contracts, and service mesh policies are managed in isolated YAML files.
• No unified mechanism ensures all components switch simultaneously. Example Failure Scenario:
• backend-v2 deployed (green) but references db-v1 (blue).
• db-v2 schema changes break backend-v2, but YAMLs for DB and backend are in separate directories.
• Deployment proceeds because GitOps tools see no dependency linkage in YAML. Consequences:
• 22% of B/G deployments fail due to version mismatches (CNCF 2023 survey).
• Teams resort to “smoke test” scripts to detect version drift post-deployment.

5. Operational Overhead for Rollbacks

Problem: Rolling back a failed A/B test or B/G deployment requires:

• Reverting multiple YAML files across deployments, services, and ingress.
• Manual cleanup of “orphaned” variant resources (e.g., unused v2 pods). Real-World Impact:
• A telecom company reported 15-minute rollbacks taking 2+ hours due to fragmented YAML tracking.
• Fear of incomplete rollbacks leads teams to avoid testing in production altogether.

6. Metric Disconnect

Problem: A/B testing relies on correlating deployment versions with business metrics (conversion rates, error rates). In YAML-centric GitOps:

• No standardized way to tag YAML-defined deployments with business context.
• Monitoring dashboards require manual mapping of Kubernetes pod hashes to A/B variants. Evidence:
• 45% of teams report “guessing” which variant caused metric changes due to poor tagging (DevOpsDays 2023).

7. Cross-Service Dependency Chaos

Problem: Modern A/B tests often span multiple services (e.g., testing a new checkout UI and payment microservice). In fragmented GitOps:

• Teams must synchronize YAML changes across unrelated repositories.
• No unified “feature flag” or “test cohort” abstraction exists across deployments. Example: A retail company’s Black Friday test failed because:
• checkout-service-v2 YAML was deployed.
• inventory-service-v1 (not part of the test) lacked YAML configuration for the new API version.
• The mismatch caused checkout failures for 12% of users.

Summary

• YAML Sprawl: Teams report 200-500% more YAML files when adopting A/B testing in GitOps.
• Deployment Times: A/B pipelines take 3x longer than standard deployments due to fragmented resource checks.
• Incident Surge: 33% increase in post-deployment incidents traced to A/B/B-G configuration errors.
• Innovation Tax: 70% of developers say YAML complexity discourages them from proposing new A/B tests.

This operational friction directly undermines the agility promised by GitOps, turning advanced deployment strategies into fragile, high-effort endeavors.

Organizational inefficiencies

Beyond technical fragmentation, YAML/Container-centric GitOps can breed negative organizational dynamics. By breaking cohesive into multiple deployment tasks, each task is easily replicable “easily replica tasks,” it can inadvertently foster Organizational inefficiencies:

The Replicability of “Shallow” YAML Tasks:

• Each Task Becomes “Shallow”: Deploying a single container, configuring a Lambda function, setting up an API Gateway route – these tasks, when viewed in isolation and managed through YAML, can become relatively “shallow” in terms of required expertise and effort (at least superficially). These shallow tasks are often highly replicable. You can copy YAML snippets, reuse deployment patterns, and follow standardized procedures to perform these individual tasks.
• The “Hidden Logic” and “Drifted Configurations” - The Real “Valuable Currencies”: As the explicitly managed parts of the system (the YAML deployments) become simplified and replicable, the real complexity and value shift to the implicit, undocumented, and often drifted aspects of the system. So you see a lot of tickets are closed with single word “done”?

Hidden Logic:

• Implicit Dependencies and Interactions: In a fragmented, deployment-centric world, the logic of how different components interact, depend on each other, and function as a cohesive environment becomes implicit and often undocumented. This logic is not explicitly defined or managed in code; it’s spread across numerous YAML files, scripts, and tribal knowledge.
• Undocumented Assumptions and Conventions: Implicit logic relies heavily on undocumented assumptions, conventions, and “how things are done around here.” These are not codified or versioned, making them fragile and hard to understand for newcomers.
• “Tribal Knowledge” as Currency: Understanding this hidden logic becomes “tribal knowledge” held by a few individuals who have been around long enough to piece together how the system really works. This knowledge becomes a source of power and control, until the last paycheck.

Drifted Configurations:

• Configuration Drift as Inevitable Consequence: YAML-centric, fragmented GitOps, as we’ve discussed, is prone to configuration drift. Environments diverge, YAML files become inconsistent, and the “desired state” in Git no longer accurately reflects the “actual state” in production.
• Drift as a Source of “Unique” Knowledge: Over time, these drifted configurations become a unique and often poorly understood aspect of each environment. Understanding why an environment is configured a certain way, even if it’s drifted from the intended state, becomes valuable knowledge.
• “Fixing Drift” as a Specialized Skill: “Fixing” configuration drift, often through manual intervention, becomes a specialized (and often stressful) skill. Individuals who can navigate and “correct” drifted configurations gain perceived value.

When tasks become shallow and easily replicated with tribal knowledge as currency, the organizational dynamic can breed inefficiencies:

Information Hoarding and Lack of Transparency:

• Tribal Knowledge as Power: Individuals who possess the “tribal knowledge” of hidden logic and drifted configurations gain power and influence. They might be incentivized to hoard this knowledge rather than share it openly, as it becomes their source of value. Someone has magic?
• Lack of Documentation and Clarity: There’s less incentive to document the implicit logic or clean up drifted configurations. Transparency is reduced as valuable information becomes concentrated in the hands of a few.
• Jira Tickets Sprawl: Backlog tickets are out of context/date. Ops tickets closed with single word “done”.
• “Black Box” Systems and Processes: The system as a whole becomes more of a “black box,” harder for newcomers to understand, and more reliant on the “experts” who hold the hidden knowledge.

“Hero Culture” and Individualism Over Teamwork:

• Rewarding “Firefighting” over Prevention: As systems become unpredictable due to hidden logic and drift, “firefighting” becomes more frequent and necessary. Individuals who can quickly resolve incidents and “fix” drifted configurations are often lauded as “heroes.”
• Individual Recognition for “Fixing” Problems They Might Have Contributed To: Ironically, individuals who contribute to the complexity and drift (perhaps unintentionally :) ) can become heroes for “fixing” the problems they indirectly helped create.
• Undermining Collaborative Problem Solving: The focus on individual heroics and “quick fixes” discourages collaborative problem-solving and long-term systemic improvements.

“Configuration Gatekeepers” and Control:

• Control Over “Valuable” Knowledge: Individuals who understand the hidden logic and drifted configurations become “configuration gatekeepers.” They control access to and understanding of how the system really works.
• Power Through Obscurity: This control can be used to maintain power and influence. By keeping the system complex and undocumented, these individuals can make themselves indispensable and resist changes that might threaten their control.
• Resistance to Simplification and Abstraction: There might be resistance to efforts to simplify configurations, improve documentation, or adopt more code-based, transparent approaches, as these changes could diminish the value of their “tribal knowledge.”

Inhibition of Innovation and Progress:

• Fear of Disrupting “Working” Systems (Even if Flawed): Systems built on hidden logic and drifted configurations, while unpredictable, might still be “working” in a fragile way. There’s a fear that any significant changes to improve transparency or adopt better practices could disrupt these fragile systems and lead to outages.
• Stagnation and Technical Debt: The organization becomes trapped in a cycle of maintaining complex, undocumented systems, accumulating technical debt, and stifling innovation.
• Talents leave: Talented engineers and innovators can become frustrated with rigid, slow, and error-prone systems. They are punished for bringing up problems, told to “do as told even it’s wrong!”, assigned the most tedious and dangerous tasks, labelled as bad performance, not team player when they refuse to take blame for others’ fault or keeping their mouth shut or colluding along …

See content credentials Just as ancient astronomers forced complex epicycles and deferents to explain planetary motion in an Earth-centered universe, the YAML/container-centric GitOps model contorts itself with unnecessary complexity.

The Geocentric Trap of YAML/Container-Centric GitOps

Much like the Earth-centric model of the universe—where convoluted epicycles were invented to explain planetary motion—YAML/container-centric GitOps imposes artificial complexity on modern software delivery. By treating containers and their YAML configurations as the “center” of the universe, organizations force themselves into a fragmented reality:

• Orbiting around deployment units: Individual containers and YAML files become the focal point, akin to Earth in the geocentric model. Teams waste energy reconciling endless configuration copies (dev/staging/prod directories) rather than defining environments holistically.
• Epicycles of complexity: Just as geocentrism required layers of corrective math, YAML sprawl demands templating, overlays, and sync tools to compensate for its inherent rigidity. A/B testing and blue-green deployments devolve into manual traffic-shifting and version-juggling.
• Lost celestial harmony: Environments fracture into disconnected deployments, mirroring the geocentric failure to see orbital unity. Configuration drift and tribal knowledge replace predictable, versioned systems.

In contrast, a sun-centered paradigm would position the logical environment (test/prod, ephemeral/permament) as its gravitational core—a single source of truth governing all components (containers, serverless, databases) through declarative abstractions. This eliminates the need for “epicyclic” workarounds, just as heliocentrism simplified astronomy. Until this shift occurs, teams remain stuck in a pre-Copernican era of deployment chaos, where simplicity is sacrificed to sustain a flawed cosmic order:

https://www.linkedin.com/pulse/from-fragmented-tools-unified-stacks-embracing-cloud-gary-yang-zfjpe/

https://www.linkedin.com/pulse/embracing-application-centric-infrastructure-cloud-2-gary-yang-6jzje/

The Geocentric Trap: Visualizing complexity in modern software delivery.