I'm Prashant Gadge

Monolithic frontend application serving multiple product lines was causing deployment bottlenecks. A single team's deployment could break unrelated features, and the 4MB+ bundle size was severely impacting load times. Cross-team collaboration was nearly impossible due to tight coupling.

• 10+ engineering teams working on the same codebase
• Legacy jQuery components mixed with modern React
• Strict security and compliance requirements
• Need to maintain backward compatibility during migration
• Limited infrastructure budget for additional services

Designed and implemented a micro-frontend architecture using Module Federation. Created a shell application that orchestrates multiple independently deployable micro-frontends. Built a shared component library using Web Components (Lit) for cross-framework compatibility. Established a unified CI/CD pipeline with automated testing and deployment for each micro-frontend.

Monorepo vs Multi-repo: Chose Nx monorepo for better code sharing and tooling consistency, accepting the complexity of larger repository size in exchange for simplified dependency management and unified CI/CD.

Performance vs Flexibility: Implemented runtime federation for maximum team autonomy, accepting the initial network overhead. Mitigated this with intelligent caching strategies and prefetching.

Web Components vs Framework Components: Chose Web Components for shared UI to enable framework-agnostic adoption, accepting the learning curve and slightly larger bundle sizes in exchange for true interoperability.

Inconsistent UI across 15+ enterprise applications was causing user confusion and increasing support costs. Each team was building their own versions of common components, leading to massive code duplication and maintenance overhead. No centralized source of truth for design decisions existed.

• Applications built with different frameworks (React, Angular, Vue)
• Legacy applications couldn't be immediately rewritten
• Design team needed direct control over component styling
• Tight deadlines with multiple concurrent projects
• Resistance to change from established teams

Built a comprehensive design system using Web Components (Lit) for framework-agnostic distribution. Created a token-based design system with Figma integration for designer-developer collaboration. Established a component governance process with automated accessibility testing. Built Storybook documentation with interactive examples and usage guidelines. Implemented semantic versioning with automated changelog generation.

Framework-Specific vs Framework-Agnostic: Chose Web Components for maximum compatibility, accepting the initial complexity and learning curve. This decision paid off as it allowed adoption across all teams regardless of their framework choice.

Strict Governance vs Flexibility: Implemented a tiered governance model with core components strictly controlled and allowing team-specific extensions. This balance maintained consistency while enabling team autonomy.

Big Bang vs Incremental Rollout: Chose incremental migration starting with new projects, accepting longer time to full adoption in exchange for lower risk and ability to refine the system based on real feedback.

Core Web Vitals were consistently failing across the platform. LCP was 4.2s, CLS was 0.25, and FID was 180ms. This was directly impacting SEO rankings and user engagement. No systematic approach to performance monitoring or optimization existed across teams.

• Legacy codebase with performance anti-patterns
• Third-party scripts causing significant delays
• Large image assets without optimization
• No performance budget enforcement
• Multiple teams with varying performance awareness

Implemented comprehensive code splitting with route-based and component-based lazy loading. Created an image optimization pipeline with WebP conversion and responsive images. Established performance budgets with CI/CD enforcement. Built a real-user monitoring (RUM) solution to track Core Web Vitals in production. Optimized bundle size with tree-shaking and dead code elimination. Implemented resource hints (preload, prefetch, preconnect) strategically.

Build Time vs Runtime Performance: Accepted longer build times for aggressive optimization strategies including advanced code splitting and compression. This trade-off was justified as build time happens once while runtime performance affects every user.

Feature Richness vs Performance: Implemented progressive enhancement patterns, ensuring core functionality works immediately while nice-to-have features load asynchronously. This maintained feature completeness while dramatically improving initial load.

Third-Party Functionality vs Performance: Implemented strategic loading of third-party scripts (deferred loading, script attribution), accepting some functional delay for critical third-party features in exchange for overall performance gains.