Performance Engineering
Slow apps don't just frustrate users —
they cost you revenue
I profile your system end-to-end, find the real bottleneck, and ship fixes alongside your team. Real results: 4s → 0.5s page loads. 2× checkout conversion. 60% lower cloud bill.
Sound familiar?
Symptoms of a system that needs a tune-up
Page loads creep over 3 seconds
Conversion drops every extra second. Mobile users bounce. Lighthouse scores red. The team has tried "fixes" but the numbers do not move.
API response times balloon under load
p50 looks fine, p99 is awful. The database is the suspect, but nobody can prove which query, which index, which connection pool.
Cloud spend grows faster than traffic
Hardware was thrown at the slowness. It bought a few months. Now you have an expensive system that is still slow.
No clear bottleneck — just "the whole thing is slow"
Frontend blames backend. Backend blames DB. DB blames the network. Nobody has a profile that proves anything.
How I work
Profile → prioritise → fix
1. Scan — free 30-min call
Walk me through your stack and the symptom. I tell you the three most likely culprits, the data we would need to confirm, and rough impact range. No commitment.
2. Audit — 1-3 weeks
Read-only access to code, infra, and APM. I profile end-to-end: frontend (LCP/TBT/CLS), backend (CPU/IO/DB), data layer (slow queries, indexes), infra (autoscaling, caching, CDN). Output: prioritised fix list with expected impact and effort.
3. Fix — alongside your team
Optional fix sprint. I ship the high-impact changes with your engineers: query tuning, caching layers, code-level perf, infra adjustments. Measured before/after, not vibes-based.
What I profile
Full-stack performance, not surface metrics
Core Web Vitals (LCP/INP/CLS), bundle size, render-blocking resources, hydration cost, CDN config.
Hot-path profiling, async patterns, GC pressure, thread pools, p99 latency, connection management.
Slow query log analysis, missing indexes, N+1 queries, connection pooling, replica strategy.
Redis/ElastiCache hit rates, cache invalidation, CDN configuration, HTTP cache headers.
Right-sizing, autoscaling triggers, load balancer config, cross-AZ traffic, network bottlenecks.
Sustained load + spike tests on staging. Find the cliff before production users do.
Case studies
Real engagements, measurable results
B2C · GovTech · Millions of users/month
From 4-10s page loads to 0.5s — and 2× more checkouts
Challenge
Citizen-facing platform with millions of monthly users. Page load times 4-10s. Checkout drop-off hurting revenue. Stack issues spread across frontend, backend, and infrastructure — no single owner.
Solution
Full-stack audit: frontend bundle & rendering optimisation, backend query tuning & caching, infra right-sizing and CDN tuning. Phased rollout with A/B measurement at each step.
Result
- ✓ Page load 4-10s → 0.5s (20× faster)
- ✓ 2× checkout conversion
- ✓ 2-3 month engagement
B2B SaaS · 50k users
From 3s to 80ms response — while cutting cloud cost in half
Challenge
SaaS platform hitting 3s page loads at peak. Team had thrown hardware at the problem. Cloud spend spiralling, users complaining, no clear bottleneck.
Solution
Performance audit revealed N+1 queries, missing indexes, and an oversized Kubernetes cluster. Implemented targeted fixes over 3 weeks. Zero downtime during the migration.
Result
- ✓ Response time 3s → 80ms
- ✓ Cloud spend cut 60%
- ✓ 3-week engagement, zero downtime
Engagement options
Three ways to work together
Free
30-min scan
$0
- ✓ Top 3 suspected bottlenecks
- ✓ What data to collect next
- ✓ No commitment
Fixed scope
Audit sprint
1-3 weeks
- ✓ End-to-end profiling
- ✓ Prioritised fix list with impact
- ✓ Reproducer test harness
- ✓ Architecture & query review
- ✓ 30-day Slack follow-up
Hands-on
Audit + fix sprint
From 4 weeks
long-term if needed
- ✓ Everything in Audit sprint
- ✓ Pair with your team on fixes
- ✓ PRs, not handoff docs
- ✓ Before/after measurement
Common questions
What stacks do you work with?
Backend: Java, Kotlin, Python (also Node.js, PHP). Data: PostgreSQL, MySQL, Redis, Mongo, Kafka, RabbitMQ. Infra: AWS, Azure, Kubernetes, Terraform, bare-metal Linux. Frontend perf is mostly framework-agnostic — I look at the network waterfall, render path, and bundle, not the framework.
How much access do you need?
Audit-only: read access to code, APM (Datadog/New Relic/Grafana/CloudWatch), and the database (read replica or slow query log is fine). Fix sprint: scoped write access via your existing PR workflow.
What if we have no APM?
Common situation. Part of the audit is setting up basic profiling that survives the engagement — usually OpenTelemetry plus the cheapest viable APM. You walk away with permanent observability, not a one-off report.
How fast will we see results?
Quick wins (missing indexes, N+1 queries, cache misses) often land in week 1. Bigger architectural changes (caching layers, query refactors, infra) take 2-6 weeks depending on rollout pace.
Can performance work really move conversion?
Yes — see the GovTech case above (2× checkout conversion). Page load is one of the strongest controllable conversion levers for B2C and longer signup flows. Every 100ms shaved off LCP is measurable revenue for sites with real traffic.
Do you do load testing?
Yes. Synthetic load (k6, Gatling, JMeter) on staging that mirrors production traffic patterns. Find the cliff before your users do.
Stop guessing where the slowness lives
30 minutes, screen-share, top 3 suspects named. No slide deck, no obligation.