#performance-checklist.md

Version: 1.0.0

Target Models

Mistral Medium 3.5
Mistral Large 3
Mistral Small 4
Mistral Family
Future Mistral Models

#Purpose

This checklist defines the minimum performance requirements for software systems before production deployment, feature release, or major architectural changes.

It applies to

SaaS Platforms
AI Applications
APIs
Backend Services
Frontend Applications
Mobile Applications
Enterprise Software
Cloud Infrastructure
Developer Platforms

Performance is not making software faster.

Performance is ensuring systems consistently deliver responsive, efficient, predictable, and scalable user experiences while using infrastructure resources responsibly.

Fast software builds trust.

Slow software destroys it.

#Core Principle

High Performance

Low Latency

High Throughput

Efficient Resources

Scalability

Reliability

Observability

Performance should be engineered.

Never optimized by guesswork.

#Performance Workflow

Define Targets

↓

Measure Baseline

↓

Identify Bottlenecks

↓

Optimize

↓

Validate

↓

Stress Test

↓

Monitor

↓

Improve

#1. Performance Objectives

□ Response time targets defined

□ Throughput targets defined

□ Availability targets documented

□ Scalability goals established

□ Resource budgets defined

□ Business SLAs documented

□ Customer expectations identified

□ Success metrics approved

Performance begins with measurable objectives.

#2. Baseline Measurement

□ Current latency measured

□ API response times recorded

□ Database performance measured

□ Frontend loading measured

□ AI inference time measured

□ Memory usage recorded

□ CPU utilization measured

□ Network latency documented

Optimize from facts.

Not assumptions.

#3. Frontend Performance

□ First Contentful Paint optimized

□ Largest Contentful Paint acceptable

□ Interaction responsiveness verified

□ JavaScript optimized

□ CSS optimized

□ Images compressed

□ Fonts optimized

□ Bundle size reviewed

Users judge quality within seconds.

#4. Backend Performance

□ API latency acceptable

□ Database connections optimized

□ Parallel processing reviewed

□ Request handling efficient

□ Background jobs optimized

□ Queue latency acceptable

□ Service communication optimized

□ Serialization efficient

Backend performance drives user experience.

#5. Database Performance

□ Indexes reviewed

□ Slow queries optimized

□ Query plans analyzed

□ N+1 queries eliminated

□ Transactions optimized

□ Connection pooling configured

□ Lock contention minimized

□ Database caching reviewed

The database should never become the bottleneck.

#6. Caching

□ Browser caching configured

□ CDN caching enabled

□ API caching implemented

□ Database caching reviewed

□ Object caching configured

□ Cache invalidation verified

□ Cache hit ratio measured

□ Cache expiration appropriate

Cache what is expensive.

Not everything.

#7. Network Optimization

□ Compression enabled

□ HTTP keep-alive configured

□ HTTP/2 or HTTP/3 enabled

□ Payload size minimized

□ CDN configured

□ DNS optimized

□ TLS optimized

□ Static assets distributed

Networks contribute significantly to latency.

#8. Resource Utilization

□ CPU usage acceptable

□ Memory usage stable

□ Storage utilization monitored

□ Disk I/O optimized

□ Network utilization reviewed

□ Thread usage analyzed

□ Connection limits reviewed

□ Resource leaks eliminated

Efficient software scales better.

#9. Scalability

□ Horizontal scaling verified

□ Vertical scaling evaluated

□ Stateless architecture preferred

□ Load balancing configured

□ Auto-scaling tested

□ Queue scaling validated

□ Database scaling reviewed

□ AI workload scaling considered

Performance should improve with additional resources.

#10. AI Performance (If Applicable)

□ Prompt latency acceptable

□ Model inference optimized

□ Context size reviewed

□ Token usage optimized

□ Tool execution efficient

□ Streaming verified

□ Fallback models configured

□ AI costs monitored

AI performance affects both speed and cost.

#11. Load Testing

□ Expected load tested

□ Peak load tested

□ Concurrent users simulated

□ API throughput measured

□ Database stress tested

□ Queue capacity validated

□ Infrastructure limits identified

□ Results documented

Systems should perform under realistic load.

#12. Stress Testing

□ Resource exhaustion tested

□ Failure behavior observed

□ Recovery validated

□ Rate limiting verified

□ Traffic spikes simulated

□ Dependency failures tested

□ Graceful degradation confirmed

□ Stability maintained

Stress reveals hidden weaknesses.

#13. Reliability Under Load

□ Error rate acceptable

□ Timeouts configured

□ Retries validated

□ Circuit breakers verified

□ Queue stability maintained

□ Worker resilience confirmed

□ Memory stability maintained

□ Recovery verified

Performance without reliability has little value.

#14. Monitoring

□ Latency monitored

□ Throughput monitored

□ CPU monitored

□ Memory monitored

□ Disk monitored

□ Database monitored

□ Queue monitored

□ AI latency monitored

Performance should be continuously observable.

#15. Performance Budgets

□ Page size budget defined

□ API latency budget defined

□ CPU budget defined

□ Memory budget defined

□ Build size budget defined

□ Query budget defined

□ Infrastructure budget defined

□ AI cost budget defined

Budgets prevent gradual degradation.

#16. Regression Prevention

□ Performance benchmarks stored

□ CI performance validation configured

□ Performance alerts enabled

□ Historical comparisons available

□ Regression thresholds defined

□ Automated profiling available

□ Build comparisons reviewed

□ Optimization history documented

Prevent performance from slowly degrading.

#17. Security Impact

□ Encryption overhead acceptable

□ Authentication efficient

□ Authorization optimized

□ Logging optimized

□ Rate limiting efficient

□ Security scanning acceptable

□ WAF impact reviewed

□ Monitoring overhead acceptable

Security should protect performance.

Not unnecessarily reduce it.

#18. Documentation

□ Performance targets documented

□ Bottlenecks documented

□ Optimization decisions documented

□ Load testing documented

□ Infrastructure limits documented

□ Scaling strategy documented

□ Monitoring documented

□ Performance playbooks available

Documentation improves future optimization.

#19. Performance Review

Review

□ Bottlenecks

□ Customer feedback

□ Monitoring data

□ Infrastructure trends

□ Scaling effectiveness

□ AI performance

□ Cost efficiency

□ Lessons learned

Performance is never finished.

#20. Continuous Improvement

Continuously improve

Latency

↓

Throughput

↓

Efficiency

↓

Scalability

↓

Resource Usage

↓

Reliability

↓

Customer Experience

↓

Operational Cost

Small optimizations compound significantly over time.

#Performance Quality Attributes

Evaluate

Responsiveness

Efficiency

Scalability

Reliability

Availability

Resource Utilization

Observability

Cost Efficiency

#Performance Questions

Before approval ask

Does the system meet performance objectives?

↓

Can it handle expected peak traffic?

↓

Can it scale without architectural redesign?

↓

Are bottlenecks identified and monitored?

↓

Are resources used efficiently?

↓

Can performance regressions be detected automatically?

↓

Would experienced Performance Engineers confidently approve this system?

#Severity Levels

Critical

System unavailable

Extreme latency

Database failure

Memory exhaustion

Infrastructure collapse

AI timeout failures

Major

Slow APIs

High resource usage

Queue congestion

Scaling failures

Performance regression

Poor caching

Medium

Bundle optimization

Query improvements

Monitoring enhancements

Configuration tuning

Minor

Documentation improvements

Dashboard updates

Additional benchmarks

Future optimization

#Performance Checklist

✓ Performance objectives defined

✓ Baseline measured

✓ Frontend optimized

✓ Backend optimized

✓ Database optimized

✓ Caching configured

✓ Network optimized

✓ Resource utilization reviewed

✓ Scalability validated

✓ AI performance reviewed (if applicable)

✓ Load testing completed

✓ Stress testing completed

✓ Reliability verified

✓ Monitoring enabled

✓ Performance budgets established

✓ Regression prevention configured

✓ Security impact reviewed

✓ Documentation complete

✓ Performance review completed

✓ Continuous improvement established

#Anti-Patterns

Avoid

Optimizing without measurement

Premature optimization

Ignoring database performance

No caching strategy

Large frontend bundles

Blocking synchronous operations

Ignoring load testing

Scaling before profiling

Ignoring performance budgets

Treating hardware upgrades as optimization

Optimizing microseconds while ignoring seconds

Treating performance as a one-time activity

#Definition of Done

Performance review is complete when

Clear performance objectives, service-level expectations, and measurable success criteria have been established.
Frontend, backend, database, network, infrastructure, and AI workloads consistently meet defined latency, throughput, and responsiveness targets.
Resource utilization is efficient, predictable, and capable of supporting expected and peak production workloads.
Load testing, stress testing, scalability validation, and failure testing confirm stable behavior under realistic operating conditions.
Monitoring, profiling, benchmarking, and alerting provide continuous visibility into performance health and regressions.
Performance budgets, automated validation, and historical benchmarking prevent gradual degradation over time.
Performance optimizations improve customer experience while maintaining reliability, security, maintainability, and operational simplicity.
Documentation clearly records performance goals, bottlenecks, optimization decisions, testing methodologies, infrastructure limits, and scaling strategies.
The engineering team can confidently identify, diagnose, and resolve performance issues before they affect customers.
The system consistently delivers fast, reliable, scalable, and cost-efficient performance that supports long-term business growth and exceptional user experience.

Exceptional performance is almost invisible.

Users simply experience a product that responds instantly, scales effortlessly, remains stable under heavy demand, and continues delivering consistent performance as the business, infrastructure, and customer base grow over time.