Performance Optimization
Find the slow part.Fix it properly.
Performance problems do not appear — they accumulate. We profile the actual system under realistic load, fix the highest-impact bottlenecks, and give you the visibility to catch the next issue before your users do.
What we provide
Headroom for demand spikes without emergency fixes at the worst possible moment.
- Performance profiling and bottleneck identification — end to end
- Database query optimisation — execution plans, indexing strategy, N+1 elimination
- API latency analysis and caching strategy — Redis, Azure Cache, CDN
- Front-end bundle analysis and Core Web Vitals optimisation (LCP, CLS, INP)
- Azure infrastructure right-sizing and auto-scaling configuration
- Load and stress testing — k6, Azure Load Testing
- Application Insights query performance monitoring
- Connection pool optimisation and database concurrency tuning
Why Theerrv
Measure before fixing
We profile the actual system under realistic load before touching a line of code. Optimising what is fast does not help.
End to end
Database queries, API latency, front-end rendering and infrastructure sizing — we look at the full stack, not just the layer we built.
Visibility after the engagement
Every optimisation project ends with dashboards, performance budgets and alert thresholds so regressions surface as warnings, not as customer complaints.
Our approach
Discovery → Architecture → Development → Testing → Deployment → Support
Baseline and profiling
Establish current performance metrics across the stack. Run load tests against realistic user scenarios. Capture where time and resources are actually going.
Root cause analysis
Trace slow paths end to end — SQL execution plans, API request waterfalls, front-end rendering traces, infrastructure CPU and memory profiles. Confirm causes before prescribing fixes.
Fix in order of impact
Fixes applied starting with the highest-impact bottleneck, each measured against the baseline to confirm improvement. Avoid optimising things that are already fast.
Caching and infrastructure
Redis caching layers, CDN configuration, connection pooling, index additions and query rewrites — applied where the profiling shows the return is real.
Load testing validation
Re-run load tests after each significant change to confirm improvement and identify the next bottleneck in the queue.
Monitoring and performance budgets
Application Insights dashboards, performance budget alerts (P95 latency threshold, Core Web Vitals targets) and an agreed review cadence.
Technologies
The stack we use to deliver performance optimization — chosen for longevity and maintainability, not trend.
- k6
- Azure Load Testing
- Application Insights
- Redis / Azure Cache for Redis
- Azure CDN / Front Door
- SQL Server Profiler / pg_stat
- Lighthouse / Web Vitals
- dotTrace / PerfView
- Chrome DevTools
Industries & use cases
E-commerce and Retail
- Checkout and product page performance under peak load
- Search and filter query optimisation
- Image delivery and Core Web Vitals improvement
SaaS Platforms
- API latency reduction for large customer data sets
- Tenant isolation and noisy-neighbour mitigation
- Database connection pool management under concurrent load
High-Traffic APIs
- P95 latency reduction for real-time endpoints
- Caching strategy for expensive aggregation queries
- Rate-limit and back-pressure design
Internal Tools and Dashboards
- Slow report query optimisation
- Dashboard load time reduction
- Large data export performance improvement
Common questions
Our application is slow. Where do we start?
Start with measurement, not assumption. The most common mistake is optimising the wrong thing — developers guess that the front-end is slow, but the database is the real bottleneck, or vice versa. We run an Application Insights trace, a SQL query profiler session and a Lighthouse run on the same user journey simultaneously, and the data tells us where to focus. This takes a day, and it prevents weeks of work on the wrong problem.
Can you optimise a database without access to the application code?
Yes. Database-level optimisation — adding or rewriting indexes, rewriting slow queries, adjusting statistics, configuring connection pool settings — can be done with access to the database and its execution plans, without requiring changes to the application. We do this regularly as a standalone engagement when the application team cannot be involved or when the bottleneck is clearly at the data layer.
What is a Core Web Vital and why does it affect search rankings?
Core Web Vitals are three Google-defined metrics: LCP (how fast the main content loads), CLS (how much the layout shifts while loading), and INP (how fast the page responds to user input). Google uses them as ranking signals — a page that performs well on these metrics ranks higher than an identical page that performs poorly. For most business websites, improving LCP from poor to good produces a measurable improvement in organic search position.
How much performance improvement is realistic?
In our experience, the first round of optimisation on an un-profiled application produces 40–70% improvement in the slowest queries and 30–50% reduction in API P95 latency. Front-end work typically moves Core Web Vitals from poor to good for most pages. The gains diminish with subsequent rounds, which is why we focus on the highest-impact changes first and set realistic expectations for what each phase will deliver.
Related services
Start here
Tell us what theoperation is costing you.
A first conversation is a conversation, not a pitch. Describe how the work runs today and we will tell you plainly whether software is the right answer — and what it would take.
Or reach us directly at info@theerrv.com