Why Frontend Engineers Must Understand Backend Limitations

The conversation between Stack Overflow's Ryan Donovan and Xano CEO Prakash Chandran reveals a fundamental tension in modern web development: frontend engineers are increasingly building interfaces that make demands their backends cannot satisfy. This isn't just about API calls—it's about understanding the entire lifecycle of a request, from user interaction to database query and back.

The Classic Problem: Writing Checks Your Backend Can't Cash

Chandran, who spent years as a UX product lead at Google before co-founding the no-code backend platform Xano, describes this as a "rite of passage" for frontend developers. "There's this moment where you're like, 'I can just do everything on the front-end, and I can just process whatever workload I want.' And you run into walls pretty quickly," he explains.

The issue manifests in several ways:

• Client-side JavaScript overload: Frontend-heavy applications that process too much in the browser, buckling under load
• Server-side rendering bottlenecks: Even with SSR, improper delegation of workload to the backend creates production failures at scale
• API mismatch: Frontend components designed for instantaneous responses calling APIs that take seconds to process

Chandran notes that companies built on heavy frontend processing "start to fall down under load," forcing a painful rearchitecture.

The Universal Interface Trap

The conversation pivots to a newer problem: AI-generated "universal interfaces" where the frontend is created on-the-fly based on user needs. While exciting, Chandran sees this as a magnification of the same issue.

"It is a misconception to think that there isn't a back-end component to that, and oftentimes, what is being rendered is only as powerful as the back-end that is helping to render it, or the APIs," he says. The demo he references—where a booking interface generates dynamically—actually requires multiple coordinated APIs, permissioning logic, and performance guarantees.

The real challenge becomes orchestrating these disparate systems to meet user expectations for instant response. "It's not enough to say we'll just put a loading indicator while this API finishes processing," Chandran emphasizes. Users expect speed, and temporary interfaces that make them wait create broken experiences.

Componentization as the Bridge

Both hosts agree that the solution isn't generating entire interfaces, but working at the atomic level. "Starting more at the atomic or componentized level is 100% the right way to go," Chandran states.

For a booking platform, the atomic unit might be a single card showing "here's the place we think you should stay." Rather than rendering an entire web experience, you generate that component, then provide pathways for users to scope out and learn more.

This approach enables:

• Caching strategies: Pre-built components can be cached and served quickly
• API optimization: Each component makes specific, known demands on the backend
• Performance predictability: You understand exactly what each piece requires

The Backend Literacy Gap

Chandran argues that frontend engineers must develop "intimate understanding" of backend constraints. "If they're not the ones that are creating the back-end code, that's fine, but they have to have a pretty intimate understanding of how it works, what they can expect out of it, and it should be collaborative with the engineers that are writing it."

This means learning:

• Caching and indexing: How data retrieval actually works
• API design patterns: What makes an API fast vs. slow
• Scaling considerations: How systems behave under concurrent load
• Request lifecycle: The full journey from browser to database and back

He recommends frontend developers use tools like Xano's visual business logic builder to understand backend primitives—variables, loops, conditionals—without getting lost in syntax. "Even outside of a tool like Xano, I think now more than ever, it's possible for a front-end engineer to become back-end literate."

The AI Code Ownership Problem

The conversation extends beyond architecture to how teams use AI for code generation. Chandran is cautious: "If you don't understand it, you don't own it."

He shares an anecdote about an operations team member who would simply copy-paste ChatGPT responses. "I hired you for you and your abilities... I want your thoughts. Right?" This reflects a broader concern: AI-generated code without understanding creates technical debt and security risks.

At Xano, they limit AI use to:

• Internal tools (non-mission-critical)
• Proof-of-concept work
• Boilerplate generation by senior engineers

Junior engineers are explicitly required to write more code manually to develop their skills. "People need to be able to learn. There's a wonderful role that AI plays in literacy, but to have it just generate for you blindly... I think it's a danger."

Spec-Driven Development as the Future

Looking ahead, Chandran believes the backend must evolve to be more spec-driven. "Tools like ours are gonna have to be more beholden to specs, and figuring out how we can kind of make that a first-class citizen in terms of the way that back-ends are built and created."

This means:

• Explicit contracts: API specifications that agents can understand
• Scoped APIs: Endpoints designed for specific component needs
• Observable systems: Clear visibility into how agents process requests

The goal is creating a "middle ground" between raw cloud consoles and pure AI generation—a control plane where developers maintain understanding and control while leveraging automation.

Practical Recommendations

For frontend developers still working in isolation, Chandran suggests:

1. Build backend literacy: Use low-code tools or frameworks to understand backend primitives
2. Talk to backend engineers: Learn their scaling and performance concerns
3. Study system design: Understand caching, indexing, and request lifecycles
4. Think full-stack: Consider the complete user experience, not just the interface
5. Experiment measuredly: Use AI for exploration, but own the production code

The core message: Speed without understanding is dangerous. Whether building traditional applications or AI-generated interfaces, frontend engineers who understand backend constraints create better, more reliable systems. Those who don't are writing checks that will eventually bounce—hard.

Chandran's final warning: "The ones that kind of enter into it headfirst without thinking through all of these dynamics, there's gonna be an issue."

The future belongs to developers who can bridge the frontend-backend divide, whether through componentization, spec-driven design, or simply understanding how their code actually works at every layer of the stack.