Cut Your API Latency by 80%
Five days of backend performance engineering: profiling hot code paths, eliminating N+1 queries, optimising serialisation, and validating every improvement under realistic load with k6 or Locust.
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Backend performance optimisation is the highest-leverage performance work available to most engineering teams. A 5-day engagement focused on profiling and targeted optimisation typically produces 4–10x latency improvements and 3–5x throughput gains — returns that are rarely available from infrastructure scaling alone.
The foundation is flame graph analysis: a visual representation of where CPU time is spent in your application under realistic load. Flame graphs reveal the specific functions consuming disproportionate resources — hot string formatters, inefficient JSON serialisation, connection acquisition delays — with a precision that metric dashboards cannot provide. Every optimisation we make is validated by a corresponding change in the flame graph.
N+1 query elimination is the single highest-impact optimisation in the majority of API backends. A single API endpoint making one database query per result row generates N+1 queries under load, producing non-linear latency growth that no amount of horizontal scaling can fix. We identify and eliminate every N+1 pattern in your critical paths and validate the fix with EXPLAIN ANALYZE and load testing.
Engagement Phases
Profiling & Root Cause Analysis
We run language-native profilers (pprof for Go, py-spy for Python, async-profiler for JVM) against production-like load to build flame graphs for your critical API paths. We identify hot functions, blocking I/O, inefficient serialisation, and query patterns generating N+1 problems.
Optimisation Implementation
We implement fixes directly: query refactoring with EXPLAIN ANALYZE validation, N+1 elimination via eager loading or batching, serialisation optimisation, connection pool tuning, and strategic caching with correct invalidation. We work in your codebase and submit changes via pull request.
Load Validation & Handoff
We run k6 or Locust load tests comparing before and after P50/P95/P99 latency and throughput. We produce a benchmark report documenting each optimisation, its measured impact, and a performance testing runbook your team can use to validate future changes.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| P99 latency | 800 ms | 120 ms |
| N+1 queries | 47 | 0 |
| Throughput | 1,200 req/s | 4,800 req/s |
Tools We Use
Frequently Asked Questions
Do you write code or just recommend changes?
We write code. All optimisations are implemented as pull requests in your repository, reviewed by your team before merge. We write in the language your service uses — Go, Python, Node.js, Java, Ruby — and follow your code style and review process.
What if the bottleneck is architectural — not a fixable code issue?
Architectural issues (synchronous processing that should be async, a monolith that needs decomposition) are identified in the profiling phase and included in the roadmap. For a 5-day engagement, we focus on changes implementable within the sprint while clearly documenting the architectural work for a follow-on phase.
How do you handle caching without causing consistency bugs?
We design caching strategy before implementation: TTL selection based on data change frequency, invalidation triggers (write-through, event-driven, or TTL-only), and cache key design to avoid collisions. We also add cache metrics so the team can monitor hit rate and detect staleness issues. The consistency bugs typically come from cache implementations without a clear invalidation strategy.
Your P99 Deserves Better
Book a free 30-minute performance scope call with our engineers. We review your latency profile, identify the most impactful optimization target, and scope a sprint to fix it.
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