When preparing to make an enterprise-wide decision about a target “Future State” — such as selecting a standard platform for application deployments — it’s essential to begin with a deep understanding of the existing landscape. Too often, organizations attempt to leap ahead to solutioning without first accounting for the sprawl of their current environments. This “inventory” phase is rarely neat.

In fact, it’s typically quite messy: countless non-conforming applications and services are scattered throughout the enterprise, creating a tangled web of legacy systems, bespoke solutions, and half-migrated technologies. It is, in many cases, true technology sprawl.

But within that apparent chaos, patterns begin to emerge. These patterns can be thought of as archetypes — recurring architectural or platform models that, while not always identical, share a recognizable structure and operational profile. Each archetype has its own quirks and deviations, but they generally conform to a known template. Identifying and understanding these archetypes is foundational to making rational, scalable decisions about what to support and how to design for the future.

Why Archetypes Matter

Enterprise organizations simply cannot build paved roads for every unique snowflake. Attempting to satisfy every use case results in fragmentation, complexity, and an unsustainable support burden. Instead, a prioritized list of archetypes should guide decision-making. By analyzing each archetype’s prevalence and strategic importance, we can determine which ones are “worth it” to invest in. Rare or outlier archetypes — those that truly don’t map to anything else — should have limited influence on the platform decision, simply because their uniqueness prevents scale.

However, while this is the traditional lens, it may evolve. The advent of intelligent systems, particularly AI, opens the door to more dynamic handling of edge cases. That said, pragmatism still demands focus: start with what’s common and operationally significant, then dive deeper into the anatomy and lifecycle of those archetypes to understand how best to support them.

Common Archetypes in the Enterprise

A well-known and highly prevalent archetype is the traditional N-tier architecture. These applications typically consist of a front end (often web-based), a back end (such as a REST API or service layer), and a relational database. They’re everywhere in most large organizations, and they share a consistent anatomy and a familiar set of operational tasks. Provisioning, deploying, scaling, maintaining, and troubleshooting all tend to follow well-known procedures. These commonalities make the N-tier archetype an ideal candidate for standardization and automation.

Another common example is the COTS (Commercial Off-The-Shelf) application running on virtual machines. While the infrastructure might look similar at a high level — still VMs underneath — the operational norms can diverge significantly. COTS software often has its own update schedules, vendor-specific patching mechanisms, licensing constraints, and limitations around automation. Failover, recovery, and monitoring can be radically different. These apps require special handling and cannot always benefit from the same paved roads as custom-built services.

Then there are Kubernetes-based platforms, often championed by developer teams with a strong open source or cloud-native orientation. In these environments, the desired state is typically to run everything on top of Kubernetes, taking advantage of its abstraction layer for deployment and scaling. Operational tooling tends to revolve around kubectl, Helm, and GitOps-based flows. The key advantage here is that Kubernetes itself begins to normalize the operational biodiversity — reducing variability by enforcing conventions and surfacing a single control interface for many tasks.

Digging Deeper: The Subdomains Within Archetypes

While identifying major workload archetypes is a crucial step toward rationalizing an enterprise IT environment, the next layer of insight comes from recognizing the subdomains that exist within each archetype. These subdomains — or species, if we continue the “tech sprawl” ecology metaphor — share a broader architectural pattern but differ in ways that significantly affect how they are deployed, operated, and supported.

The Flora and Fauna of N-Tier

The n-tier application architecture is ubiquitous, but it’s far from monolithic in its own right. Within this archetype, we find multiple subdomains that each have their own unique characteristics and operational implications:

  • Single Page Applications (SPAs): These are modern front ends typically built using frameworks like Angular or React, paired with RESTful API backends. While they follow the basic front end–back end–database model, their separation of concerns (with the front end often deployed independently) changes how deployment pipelines, security, and versioning are managed. SPAs usually require CORS handling, token-based auth like OAuth/JWT, and CDN-based delivery strategies.
  • Server-Side Page Generation Monoliths: Think classic ASP.NET Web Forms or MVC-style apps where front-end and back-end logic are tightly coupled. These monoliths often deliver both the HTML and business logic in a single deployable unit. This tight coupling simplifies some operational tasks (e.g., fewer moving parts) but introduces rigidity when it comes to scaling, modernization, and CI/CD practices.
  • Hybrid or Transitional Forms: In many enterprises, you’ll encounter hybrid n-tier apps — part SPA, part server-rendered, with maybe a little jQuery-era legacy thrown in. These hybrids are harder to classify cleanly but are important to acknowledge because they represent real operational constraints and migration complexity.

Each of these subdomains may still fall under the n-tier umbrella, but their differences materially affect how you provision, monitor, scale, and update them. Ignoring these nuances leads to oversimplification and can sabotage any attempt at standardization.

The Spectrum of COTS Deployments

COTS (Commercial Off-The-Shelf) software represents another broad archetype that includes several deployment and operational subdomains:

  • Single-Box Deployments: These are standalone applications often installed on a single physical or virtual machine. Think of legacy apps that bundle everything — application logic, database, and UI — into one installation. These are often inflexible, difficult to scale, and may lack APIs or automation hooks.
  • N-Tier COTS Monoliths: Some commercial applications do follow an n-tier pattern, with separate presentation, application, and data layers, but they’re still tightly coupled and managed via proprietary tools. These apps usually have strict vendor constraints on patching, backup, failover, and must often be treated as a black box operationally.
  • Hardware/Software Appliances: These are turnkey systems, sometimes delivered as physical appliances or virtual images, that include not only the software but also a vendor-managed OS and even hardware-level tuning. They typically come with vendor SLAs and rigid support boundaries, making them nearly impossible to modify or integrate cleanly with broader enterprise standards.

Again, understanding the operational implications of each of these COTS subdomains helps avoid treating all commercial software as equally manageable or equally supportable. Some may allow modest automation; others may reject it entirely.

Kubernetes: Not a Workload Archetype

It’s worth clarifying that Kubernetes is not itself a workload archetype. Unlike n-tier or COTS, Kubernetes does not prescribe a particular application architecture. Instead, it is an infrastructure architectural pattern — a powerful abstraction layer that allows different application architectures to conform to a standardized operational model. With Kubernetes, nearly any kind of application — whether it’s a mesh of microservices, a REST API, a monolithic service, a batch job, or even a legacy system containerized into compliance — can be deployed and managed using a unified set of tools (kubectl, Helm, ArgoCD, etc.) and declarative manifests. Kubernetes doesn’t eliminate the need for understanding application archetypes, but it flattens the biodiversity by offering a common control surface for managing diverse workloads.

The adoption of Kubernetes can in fact mask underlying architectural differences, which makes it even more critical to identify the true workload archetype beneath the orchestration layer. An N-tier app deployed to Kubernetes is still an N-tier app, and it still brings with it all the same lifecycle, integration, and scaling considerations. Kubernetes merely changes how it is deployed and operated — not what it fundamentally is.

Thinking Through Additional Archetypes

Beyond these there are likely many other archetypes lurking in most environments:

  • Serverless or Function-as-a-Service (FaaS) workloads, which rely on fully managed platforms with highly opinionated deployment and monitoring paths.
  • Data platform services like Spark or Hadoop clusters, which require their own runtime environments and scaling logic.
  • Desktop-bound or thick-client apps, still common in industries like finance or healthcare, with deeply entrenched local dependencies.
  • Monoliths deployed to bare-metal, often in industrial or manufacturing contexts, with little or no virtualization.

Each of these could potentially define its own archetype, provided it meets the following criteria:

  1. It is sufficiently common or strategically important
  2. It has a consistent anatomical structure
  3. It has a consistent set of operational patterns

From Archetypes to Operational Requirements

Once you’ve mapped out your workload archetypes, the next step is to enumerate the operational actions associated with each. These are the day-to-day tasks that engineers and administrators must perform to keep workloads healthy and running. Examples include deploying updates, performing backups, patching OS or middleware, handling failovers, or responding to performance degradation.

Operational actions may be manual, semi-automated, or fully automated, but in practice, many remain manual or require human oversight. That’s why understanding the operational lens — not just the architecture or tooling — is so crucial. It is these actions that ultimately shape the requirements list for any future platform strategy. If a proposed solution makes routine operational work harder or fails to support a critical archetype’s needs, it won’t succeed, no matter how elegant it looks on paper.

Conclusion

Strategic decisions about standardizing deployment platforms or defining a “Future State” for IT environments must begin with reality. That means starting with a clear-eyed inventory of the present, recognizing the archetypes that dominate your environment, and understanding the full lifecycle and operational context of each. From there, you can create a prioritized roadmap — one that targets scale, repeatability, and supportability over perfection.

And while AI and platform abstraction layers like Kubernetes promise to simplify the future, the path forward still depends on knowing your workloads, knowing your people, and making disciplined, informed tradeoffs.