Performance Engineering
Performance isn't "add more caching"it's principles that deliver real speed.
Systems rarely slow down because you ran out of CPU. They degrade when hidden bottlenecks surface: queries amplify, cache misses cascade, and tail latency becomes the real user experience. This hub covers what actually breaks first, proven patterns, and how to build systems that stay fast.
Legacy support content. For active AI services, start with Latency & Serving or LLM Audit.
Bottleneck-first
Find what the system is waiting on.
Proven patterns
12 patterns that deliver consistent speed.
Evidence-based
What slows down first, not what sounds good.
Legacy reading
Archive guide from the older system-engineering set. Use for context, then route execution via AI Audit and Optimization Sprint.
Core concepts
Understanding what actually slows down first and how to think about performance, scalability, and reliability.
Architecture Scalability: What Actually Breaks First at 10x Traffic
Systems rarely collapse because you ran out of servers. They break when hidden constraints surface: pools exhaust, dependencies amplify tail latency, and data hotspots turn growth into incidents.
Scalability vs Performance vs Reliability: The Practical Difference
These terms get used interchangeably. In production they fail differently, use different metrics, and require different fixes. Here's a practical way to separate them and diagnose what you're actually dealing with.
Scalable Architecture Principles: 9 Rules That Survive Real Load
Scalable architecture isn't 'add more servers.' It's a set of principles that keep systems predictable as traffic, data, and complexity grow. These nine rules show up repeatedly in architectures that survive production load.
Patterns & practices
Proven patterns that repeatedly show up in systems that deliver consistent speed—what each pattern solves, when to use it, and when it backfires.
Caching Patterns for Scalable Systems: Edge → Reverse Proxy → Redis
Caching isn't a performance trick. It's where you choose to terminate load. This practical guide covers layered caching (Edge → Reverse Proxy → Redis), how each layer fails, and how to prevent cache stampedes from becoming outages.
Observability for Scalability: Find the Real Bottleneck
Observability for scalability isn't collecting more telemetry. It's a constraint-first workflow to find what limits growth before p99 latency, outages, and cost explode. This guide shows a practical metrics → traces → logs playbook you can use under real load.
Production performance baseline: how to build one you can trust
A production baseline isn't a snapshot—it's a statistical model you can trust. This guide shows how to build baselines that account for traffic patterns, time-of-day effects, and variance, so you can detect real regressions instead of chasing noise.
Latency distributions in practice: reading P50/P95/P99 without fooling yourself
Percentiles tell you where users actually experience latency—but only if you read them correctly. This practical guide explains how to interpret P50, P95, and P99 distributions, avoid common pitfalls, and use them to find real bottlenecks.
Finding the constraint chain: a step-by-step walkthrough on real systems
Bottlenecks don't exist in isolation—they form chains. This step-by-step walkthrough shows how to map constraint chains in real production systems, from initial symptoms to root causes, using traces, metrics, and structured isolation.
Queueing Symptoms: When Latency Is Mostly Waiting Time
If P99 explodes while CPU looks fine, you're often queueing: requests waiting on pools, locks, workers, I/O, or downstream limits. This playbook shows how to spot queueing symptoms, prove waiting vs work, fix the constraint safely, and verify the knee moved right.
Saturation Signals: CPU Is Not the Only Ceiling
Tail latency and timeouts often come from waiting—not compute. This playbook shows saturation signals beyond CPU (pools, queues, locks, I/O, network, runtime, downstream limits), how to prove the real bottleneck, and how to verify you moved the ceiling.
Workload Replay for Validation: Designing a Safe Before/After Test
Most performance work fails at verification. This guide shows how to design a safe workload replay: capture a representative request mix, replay with guardrails, compare P50/P95/P99 and saturation signals, and produce an evidence pack you can trust.
AI & LLM performance
LLM pipelines with P95 latency, timeouts, or cost spikes? We baseline, instrument, and optimize—with before/after proof.
Latency & Serving
P95, timeout, throughput, caching for LLM pipelines.
LLM Audit
Audit framework, baselines, troubleshooting for LLM/RAG.
AI Production Audit
Baseline cost/conv, TTFT, quality. Get prioritized fixes.
AI Optimization
Reduce inference cost, improve latency at scale.
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