Performance
This document defines the performance strategy, objectives, engineering standards, monitoring practices, and optimization guidelines for MHMD Studio. Performance is a core product feature and should be considered throughout design, development, deployment, and maintenance.
Document Metadata
| Property | Value |
|---|---|
| Document | Performance |
| Version | 1.0 |
| Status | Active |
| Owner | Mohammed El Maachi |
| Last Updated | July 2026 |
Purpose
Performance directly influences:
- User experience
- Accessibility
- Search visibility
- Conversion
- Reliability
- Infrastructure costs
Every feature should be evaluated for its performance impact before release.
Performance Philosophy
The platform should feel:
- Fast
- Responsive
- Lightweight
- Stable
- Predictable
Optimization should focus on measurable improvements rather than perceived complexity.
Core Principles
Performance should be:
- Measured
- Continuous
- Automated
- Documented
- Verified
- Observable
Never optimize without evidence.
Performance Goals
Target:
- Lighthouse Performance ≥ 95
- Accessibility ≥ 95
- Best Practices ≥ 95
- SEO ≥ 95
These scores are guides rather than the sole measure of quality.
Core Web Vitals
Target values:
| Metric | Target |
|---|---|
| Largest Contentful Paint (LCP) | ≤ 2.5 seconds |
| Interaction to Next Paint (INP) | ≤ 200 ms |
| Cumulative Layout Shift (CLS) | ≤ 0.1 |
| First Contentful Paint (FCP) | ≤ 1.8 seconds |
| Time to First Byte (TTFB) | ≤ 800 ms |
Monitor these continuously in production.
Rendering Strategy
Prefer:
- Static Site Generation (SSG)
- Incremental Static Regeneration (ISR)
- React Server Components
Use client-side rendering only when interactivity requires it.
Server rendering should remain the default.
JavaScript Strategy
Deliver only the JavaScript required for the current page.
Guidelines:
- Minimize client components.
- Remove unused dependencies.
- Tree-shake production builds.
- Avoid unnecessary hydration.
Less JavaScript generally results in better performance.
Code Splitting
Split code by:
- Route
- Feature
- Dynamic component
Load functionality only when needed.
Bundle Size
Establish bundle budgets.
Review large dependencies before adoption.
Avoid importing entire libraries when only small portions are required.
Bundle growth should be monitored over time.
Images
Requirements:
- Next.js Image optimization
- Responsive sizing
- Modern formats (AVIF, WebP)
- Lazy loading below the fold
- Meaningful alt text
Images should never be larger than necessary.
Fonts
Use:
- Self-hosted fonts
- Variable fonts where appropriate
- Font subsetting
- Limited font families
Avoid unnecessary font requests.
CSS
Guidelines:
- Remove unused styles.
- Prefer utility classes.
- Minimize custom CSS.
- Avoid excessive specificity.
CSS should remain predictable and maintainable.
Animations
Animations should:
- Use GPU-friendly properties.
- Prefer transform and opacity.
- Avoid layout-triggering properties.
Respect reduced-motion preferences.
API Performance
Target API response times:
| Endpoint Type | Target |
|---|---|
| Cached | ≤ 100 ms |
| Standard Read | ≤ 300 ms |
| Write Operation | ≤ 500 ms |
| AI Request Initiation | ≤ 500 ms |
Long-running tasks should execute asynchronously whenever practical.
Database Performance
Optimize:
- Index usage
- Query execution
- Pagination
- Joins
- Connection pooling
Avoid unnecessary database round trips.
Caching
Use caching at multiple layers.
Examples:
- Browser cache
- CDN cache
- Server cache
- API cache
- Database query cache (where appropriate)
Caching strategies should be documented.
Content Delivery Network
Serve static assets through a CDN.
Cache:
- Images
- Fonts
- JavaScript
- CSS
- Documents
Reduce latency for global users.
Lazy Loading
Lazy load:
- Images
- Videos
- Heavy components
- Large datasets
- Non-critical scripts
Critical content should load immediately.
Prefetching
Prefetch:
- Likely navigation targets
- Frequently accessed resources
- Static assets
Avoid unnecessary network requests.
Third-Party Scripts
Every third-party script must justify its inclusion.
Evaluate:
- Performance impact
- Privacy implications
- Maintenance
- Business value
Remove unused integrations.
AI Performance
AI features should:
- Stream responses when appropriate.
- Cache repeated requests.
- Display progress feedback.
- Support cancellation.
Users should never be left without feedback during long operations.
Memory Management
Avoid:
- Memory leaks
- Unnecessary object retention
- Excessive client-side state
Review long-lived processes regularly.
Background Tasks
Long-running operations should execute outside the request-response cycle whenever possible.
Examples:
- AI processing
- Image optimization
- Report generation
- Email delivery
Monitoring
Monitor:
- Core Web Vitals
- Lighthouse
- API latency
- Error rates
- Database queries
- Bundle size
- Cache hit rates
Monitoring should occur in production as well as development.
Regression Prevention
Performance should be tested continuously.
Changes introducing measurable regressions should be investigated before release.
Performance budgets should be enforced in CI whenever practical.
Mobile Performance
Optimize specifically for:
- Slow networks
- Limited memory
- Older devices
- Battery usage
Mobile users should receive the same quality of experience.
Accessibility And Performance
Performance improvements should never reduce accessibility.
Similarly, accessibility enhancements should be implemented efficiently.
Both objectives should reinforce one another.
Performance Checklist
Before deployment verify:
- Images optimized.
- Fonts optimized.
- JavaScript minimized.
- Code split appropriately.
- Caching configured.
- API latency reviewed.
- Database queries optimized.
- Lighthouse targets achieved.
- Core Web Vitals monitored.
- Third-party scripts reviewed.
Future Improvements
Potential future enhancements include:
- Edge rendering
- Distributed caching
- Image CDN optimization
- Predictive prefetching
- AI-assisted performance analysis
- Automated performance regression detection
Performance practices should evolve with the platform.
Final Principle
Performance is not a one-time optimization effort.
It is an ongoing engineering discipline that should be measured, monitored, and improved throughout the lifetime of the platform.