Wk 1
Anchor on service level objectives
Define latency, error rate & throughput targets
- Critical path map
- SLO & error budgets
- Anchor flow
Back to Performance Engineering
Consulting · Performance assessment
APM + full-stack tuning
The Performance Assessment Engine
A structured consulting engagement to evaluate every layer of your stack—and every observability tool in it. APM traces, metrics, profiles, and load tests correlated into a prioritized tuning roadmap backed by evidence.
Tune every layer, brick by brick.
APM + full-stack tuningWeb to OS correlationEvidence-backed roadmap
Assessment at a glance
Layers we assess
WebAppRuntimeDBOS
AI performance loop
Observetraces · metrics · profiles · RUM
Analyzeanomalies · layer correlation
Optimizeweb · app · runtime · db · os
Validateload test · canary · SLO check
Full-stack scope
One assessment model across every layer
Performance is a stack problem. We correlate signals from browser to disk so fixes are targeted, provable, and tied to your SLOs—not siloed point optimizations.
Web
App
Runtime
DB
OS
Web → App → Runtime → Database → OS & filesystem
Your assessment path
Five phases · one view · tuning roadmap
Baseline to backlog in 2–4 weeks. Every phase has clear deliverables—no long discovery cycle.
StartBaseline & pain points
2–4 week engagementTuning roadmapDone
Wk 1–2
Map observability
Inventory your tool stack
- Tool scorecard
- Coverage gaps
- Trace quality
Wk 2–3
Layer baselines
Measure every hop
- Layer report
- Bottlenecks
- Tail latency
Wk 2–3
Score tools
Signal quality review
- Per-tool scores
- Config fixes
- Consolidation
Wk 3–4
Tuning roadmap
Prioritized & provable
- Ranked backlog
- Load-test plan
- Exec readout
Service levels → observability → baselines → tooling → roadmap · one continuous path
Outcomes
What the assessment delivers
2–4 wks
Assessment window
Focused consulting with weekly readouts
5 layers
Full-stack scope
Web through OS & filesystem
Trace-backed
Root-cause evidence
Not averages or guesswork
Objective-first
Business-aligned
Every finding tied to critical paths & SLOs
Layer-by-layer
What we assess at each layer
Every layer has distinct signals and tools. We evaluate each one—and how well they connect—so your tuning roadmap is precise and provable.
Web
Map critical user journeys to waterfall traces and asset delivery—establish where front-end latency originates before tuning bundles or CDN rules.
- RUM & synthetic monitoring
- CDN / edge analytics
- Core Web Vitals baselines
- Lighthouse CI gates
Application
Correlate p95 latency to endpoints, dependencies, and releases—N+1 patterns and queue backlogs with trace-backed evidence.
- OpenTelemetry / Jaeger traces
- APM service maps
- Flame graphs & profiling
- API gateway metrics
Runtime
Evaluate GC pauses, heap sizing, and worker saturation against spike traffic—runtime knobs that move tail latency.
- JVM / .NET / Node metrics
- GC logs & heap analysis
- Container limits
- Thread & connection pools
Database
Tie slow queries and lock waits to application spans—prioritize schema, index, and pool changes on critical paths.
- Query plans & slow-query logs
- Index & statistics health
- Lock / deadlock monitors
- Replication lag
OS & filesystem
When app and DB are clean but latency remains—disk schedulers, mount options, and network stack correlated with APM host metrics.
- Host CPU / I/O / network
- Volume & filesystem latency
- Kernel & TCP tuning
- NUMA / CPU affinity
Observability consulting
Every tool in your stack—assessed for signal quality
We work with the tools you already run. The goal is better correlation and actionability—not a rip-and-replace mandate.
APM & tracing
Datadog · Dynatrace · New Relic · Tempo · Honeycomb
Span coverage, sampling strategy, service map completeness, trace-to-log correlation.
Metrics & dashboards
Prometheus · Grafana · CloudWatch · Azure Monitor · GCP Ops
SLO definitions, alert noise ratio, golden signals per layer, baseline drift detection.
Profiling & analysis
Pyroscope · async-profiler · .NET diagnostics · eBPF
Production-safe profiling cadence, hot-path ID, linkage to release diffs.
Load & chaos validation
k6 · Gatling · Locust · JMeter · Gremlin
Traffic shape fidelity vs production, pass/fail gates, proof fixes hold at peak.
Database observability
pg_stat_statements · Performance Schema · AWR · DMVs
Query plan regression, index hygiene, pool sizing, capacity forecasting.
Log correlation
Splunk · ELK · Loki · CloudWatch Logs
Trace ID propagation, error budget burn, deployment-correlated log spikes.
What we need from you
- Executive sponsor and one coordinator for workshops and data access.
- Owners for platform/SRE, application teams, and database administration.
- Read-only access to APM, metrics, and staging—or anonymized exports under NDA.
- One business-critical flow as the assessment anchor (checkout, login, batch job, etc.).
What your company receives
- Full-stack performance snapshot with trace-backed root-cause notes per layer.
- Observability stack scorecard: tool coverage, gaps, and configuration recommendations.
- Prioritized optimization backlog tied to SLO impact—not a generic maturity PDF.
- Load-test and validation plan with pass/fail gates before production rollout.
- Executive readout and optional implementation SOW for ongoing tuning.
Performance engineering
Ready to assess your stack?
From assessment to always-on tuning—measure, optimize, validate under load, and repeat until SLOs hold at peak across web, app, runtime, DB, and OS.
ObserveAnalyzeOptimizeValidateOperationalize