Build vs Buy (Part 2): Building Vibe Apps at Scale Inside SaaS Platforms

The Challenges of Building Vibe Apps at Scale

In the previous article, we explored the architectural, cost, and time-to-market implications of building vibe coding capabilities internally. The conclusion was that building such systems is not just a development effort, but a long-term architectural commitment.

The next challenge emerges when these systems move beyond initial use cases and into production. Early demos often make vibe coding in SaaS apps appear straightforward. A prompt generates an app, data is displayed, and workflows can be assembled quickly.

However, once these capabilities are introduced into a SaaS platform, usage expands rapidly across users, data models, and business processes. This is where the real complexity begins.

Building a few applications is one thing. Building and operating vibe coding systems at scale inside a SaaS platform introduces a different set of constraints, particularly around execution, maintenance, and organizational ownership.

What Scale Must the Architecture Support?

Once a native vibe creation capability is introduced, usage rarely remains limited to a single use case. Platforms must support potentially hundreds of thousands of generated dashboards, agents, workflows, and micro-applications running simultaneously across users, datasets, and integrations.

This makes scaling AI applications on SaaS a core concern. Plus, this introduces requirements that go beyond generation. The system must manage execution efficiency, cloud infrastructure usage, permissions, and operational reliability across a large number of concurrent applications.

Designing such an execution layer internally is a significant engineering challenge. It requires not only building the generation capability, but also building the infrastructure that runs and governs it.

If not designed correctly from the start, this layer becomes a bottleneck. Systems that work well in isolated scenarios often struggle when required to operate across multiple users, objects, and workflows. Eventually, it leads to performance issues and higher infrastructure costs. These scenarios reveal vibe coding limitations and highlight the challenges of vibe coding, especially in complex apps.

Commercial vibe creation solutions designed for SaaS platforms address this differently. They provide an execution layer that is built to operate within the platform environment, supporting large numbers of generated applications while maintaining efficiency and control.

What Will It Take to Maintain the System Over Time?

Maintenance rarely receives the same attention as initial development, yet in AI-driven systems, it becomes a continuous effort. Changes in platform schemas, evolving model providers, application versioning, and new use cases all require ongoing adaptation.

The pace of change in the AI ecosystem further increases this complexity. Models, tools, and architectural patterns evolve rapidly, meaning that decisions made during the initial implementation can quickly become outdated. Internal systems must therefore be continuously adjusted to keep up with both external changes and internal platform evolution. Failure to do so may negatively impact enterprise AI app development.

When vibe coding is designed for developers, maintenance is supported by a human in the loop. Developers review outputs, manage versioning, handle publishing, and resolve edge cases. In business-user scenarios, this assumption breaks down. Vendors must allocate engineering resources to maintain and evolve the generated applications over time.

There is also a knowledge continuity challenge. Home-grown AI systems are often understood by a limited number of engineers. When those engineers leave, the underlying architecture and prompt logic can become difficult to maintain.

New engineers may revisit earlier design decisions and rebuild parts of the system rather than maintain existing logic, creating repeated cycles of rework. Commercial platforms reduce this burden by embedding maintenance into the system itself.

Instead of requiring continuous re-architecture, they evolve alongside changes in models, infrastructure, and platform schemas. In fact, with agentic code generation, some of these internal tasks could be automated.

Is This the Best Use of Your AI Team?

AI talent has become one of the scarcest resources inside technology organizations. Building and operating a vibe creation layer requires sustained effort from AI engineers, platform engineers, architects, and product teams.

At scale, this effort competes directly with core product development. Every month spent maintaining infrastructure, resolving edge cases, and adapting to changes in the AI stack is time not spent on differentiated capabilities within the product.

Here, the build vs buy decision becomes strategic. It is not only about whether the system can be built, but whether building and maintaining it internally is the best use of limited engineering capacity.

Native SaaS Vibe Coding Creation Layer: A Practical Example

A leading CRM vendor recently released a beta version of its native vibe capabilities. Early adoption focused on simple use cases, such as generating UI extensions and surfacing data within existing objects.

As usage expanded, the limitations and risks of vibe coding began to surface. The capability was initially constrained to a narrow scope, requiring applications to operate within a single object or context. Extending workflows across multiple objects, handling more complex business logic, or supporting real operational use cases required significant additional engineering effort.

Users also encountered challenges with real-world data. Scenarios involving fragmented data, duplicated records, or loosely structured fields required manual adjustments and repeated iterations. Over time, maintaining these applications became increasingly dependent on engineering teams.

At the same time, the cost model became harder to justify. As usage grew, infrastructure and compute costs increased, while the effort required to extend and maintain the system reduced the perceived value of the capability. This pattern is not unique. It reflects the gap between building initial vibe functionality and operating it as a reliable, scalable platform capability.

From Innovation to Complexity: Managing Vibe Apps in SaaS

Building vibe coding capabilities is only the first step. Operating them at scale introduces challenges in execution, maintenance, and organizational focus. What begins as a fast path to innovation can become a source of growing complexity over time. Without the right architecture, scale introduces friction rather than leverage.

The question is no longer whether vibe apps can be built internally by SaaS platforms. It is whether they can be built and operated at scale in a way that remains reliable, governed, and sustainable for the organization over time.