E-commerce storefront showing peak-performance dashboard
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Retail & eCommerce · Product Engineering

Rebuilding a DTC Retailer's Storefront for Peak-Season Scale

A headless commerce platform that turned promo-day crashes into the brand's best-performing days, handling 3x peak traffic while maintaining 99.9% uptime.

Peak traffic capacity increase

3x

Timeline

6 months

Industry

retail-ecommerce

Type

Platform rebuild

✦ About Our Partner

Our partner in this project is DTC Apparel Brand, a retail-ecommerce company building at scale.

Company Overview

Direct-to-consumer retail, $40M+ annual revenue, national fulfillment

Project Timeline

6 months

Engagement Type

Platform rebuild

Discover the challenge and how we solved it

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01

A Growth Story That Broke Its Own Platform

A successful DTC apparel brand was growing rapidly but couldn't survive its own success. Every major promotion drove catastrophic traffic spikes that crashed the storefront.

The brand does $40M+ in annual online revenue with a growth model driven by paid social and high-profile collaborations. But success came with a problem: their monolithic storefront buckled every time a promotion drove 10x traffic—slow page loads, cart failures, and checkout timeouts turned the brand's biggest sales moments into its worst customer experiences.

Worse, the tightly coupled frontend and backend meant every catalog or merchandising change required a full deployment. Marketing was constantly blocked waiting on engineering just to update a landing page.

Key Metrics

$40M+

Annual Revenue

10x baseline

Peak Traffic Spike

$220K

Prior Outage Cost

Days to weeks

Deployment Cycle

02 — Challenge & Goals

A monolithic platform that worked fine at baseline traffic completely failed under promotional load. Every threshold of success became a threshold of risk.

Understanding both the business and technical landscape

Business & Operations

The team had tried to absorb promo-day traffic by manually over-provisioning servers ahead of each launch, but the real bottleneck wasn't server capacity—it was database lock contention on the checkout path. No amount of extra compute could fix it.

Technical Constraints

The storefront's tightly coupled frontend and backend meant any catalog or merchandising change required engineering involvement. The checkout path was the critical bottleneck, and it had to sustain 10x traffic while maintaining sub-2-second page loads.

Success Goals

1

10x Traffic Capacity

Survive promo-day traffic spikes without checkout slowdowns

2

Marketing Autonomy

Publish catalog and merchandising changes without engineering releases

3

Sub-2s Page Loads

Fast product, cart, and checkout experiences at any traffic level

4

Real-Time Inventory

Accurate inventory and fulfillment visibility at checkout

04 — Discovery & Strategy

Engineering Discovery

We analyzed two years of traffic patterns and promo-day outages to understand exactly where the platform broke and why.

1

Traffic Pattern Analysis

Finding

Promotions drove 10x traffic spikes in minutes, with checkout being the critical bottleneck. Database lock contention on inventory updates was the real culprit, not server capacity.

Decision

Decouple the storefront from the checkout service. Move to a headless commerce model where catalog and product updates don't require checkout redeployment.

Outcome

Inventory updates and product changes became independent of checkout stability.

2

Outage Root Cause Review

Finding

Two promo-day outages in the prior year both involved database lock contention on the checkout path during peak traffic. The root cause wasn't compute, it was data access patterns.

Decision

Build a dedicated checkout service with optimized data access patterns and separate scaling from the catalog and product systems.

Outcome

Checkout could scale independently to handle 10x traffic without impacting product browsing.

3

Merchandising Workflow Analysis

Finding

Marketing was blocked waiting on engineering for every catalog update. Turns out they needed to publish changes in hours, not days.

Decision

Implement a headless architecture where marketing can publish catalog and merchandising changes via CMS API without engineering involvement.

Outcome

Time-to-publish dropped from days to hours. Marketing gained autonomy.

4

Inventory & Fulfillment Sync

Finding

Inventory was synced batch-wise to the storefront, sometimes causing overselling at checkout. Real-time inventory was critical.

Decision

Build a real-time inventory sync from fulfillment centers with event-driven architecture so checkout always sees accurate availability.

Outcome

Overselling was eliminated. Fulfillment accuracy improved to 99.95%.

5

Load Testing & Capacity Planning

Finding

Load testing confirmed 10x traffic spikes would still overload the monolithic platform even with extra servers.

Decision

Design for 15x traffic capacity with independent scaling for catalog, checkout, and fulfillment services.

Outcome

Platform sustained 3x peak traffic with 99.9% uptime. Room for growth.

05 - System Architecture

Headless Commerce at Scale

A decoupled architecture where catalog, checkout, inventory, and fulfillment services scale independently, and marketing can update content without engineering.

1

Storefront & Content

Fast, static-first React storefront pulling product data from headless CMS API. Marketing updates catalog and merchandising without deployments.

React 19Next.jsTypeScriptTailwind CSS
2

Headless Commerce API

Product catalog, search, inventory availability, and merchandising API. Built for high read throughput with aggressive caching and CDN distribution.

Node.jsExpressGraphQLElasticsearch
3

Checkout Service

Dedicated checkout with optimized data access patterns. Scales independently from catalog. Handles payment, tax, shipping, and order creation.

Node.jsPostgreSQLRedisStripe API
4

Inventory & Fulfillment

Real-time inventory sync from fulfillment centers. Event-driven so checkout always sees accurate availability. Prevents overselling.

KafkaStream ProcessingEvent StorePostgreSQL
5

Global CDN & Caching

Storefront cached at edge globally. Static product content cached aggressively. Dynamic pricing and promotions cached per-session.

CloudFrontVarnishRedisCache Layers

System Design Philosophy

Each layer serves a specific purpose and can be scaled or updated independently. The separation of concerns ensures that fraud scoring (layers 2-4) can be as complex and slow as needed without affecting the checkout experience. Checkout always stays fast because it's completely decoupled from the analysis pipeline.

06 — What We Built

Platform Architecture

The platform comprises four major systems: a fast storefront decoupled from checkout, a headless commerce API for catalog and search, a dedicated checkout service, and real-time inventory sync.

Each system is designed to operate independently while contributing to a unified fraud detection and prevention strategy.

⚡

Fast Storefront with Static-First Rendering

System 1

🔍

Decoupled Checkout Service

System 2

📊

Marketing Autonomy via Headless CMS

System 3

🛡️

Real-Time Inventory Sync

System 4

1

Fast Storefront with Static-First Rendering

React storefront with static product pages, server-side rendering, and CDN distribution. Page loads under 2 seconds even at peak traffic.

  • ✓Sub-2s page loads at 10x traffic
  • ✓Static-first with dynamic updates
  • ✓Global CDN distribution
  • ✓Search-engine optimized
Fast-loading storefront interface
2

Decoupled Checkout Service

Dedicated checkout service with optimized checkout flow, payment processing, tax calculation, and shipping integration.

  • ✓Sub-200ms checkout latency
  • ✓Handles 10x peak traffic
  • ✓Real-time payment processing
  • ✓Fraud detection integration
Checkout interface design
3

Marketing Autonomy via Headless CMS

Marketing publishes catalog, promotions, and merchandising changes via API without engineering involvement. Changes live in hours, not days.

  • ✓Marketing-controlled catalog updates
  • ✓Promotion scheduling and automation
  • ✓A/B testing via content variants
  • ✓Hours to publish, not days
Headless CMS content management interface
4

Real-Time Inventory Sync

Event-driven inventory updates from fulfillment centers prevent overselling and ensure accurate availability at checkout.

  • ✓Real-time inventory updates
  • ✓Zero overselling events
  • ✓99.95% fulfillment accuracy
  • ✓Multi-warehouse visibility
Inventory management dashboard

07 — Delivery & Engineering

How We Delivered

1

Discover

Traffic pattern analysis and outage root-cause review

Output: Headless architecture roadmap and capacity targets

2

Design

Headless commerce API design and checkout service specs

Output: Architecture diagrams and API contracts

3

Build

Parallel development of storefront, checkout, inventory sync

Output: Headless platform with decoupled services ready for load testing

4

Validate

Load testing at 10x peak traffic and failover testing

Output: QA sign-off with 99.9% uptime confirmed at scale

5

Deploy

Gradual traffic ramp and promo-day launch

Output: Production platform handling 10x peak traffic

Strategic Decisions

Key Engineering Decisions

1

Challenge

Database lock contention on checkout path during 10x traffic spikes. Over-provisioning servers didn't help because the bottleneck was data access patterns, not compute.

Decision

Build a dedicated checkout service with optimized queries and caching. Decouple from catalog system so product updates don't impact checkout.

Outcome

Checkout can handle 15x peak traffic. Inventory updates and product changes are independent.

2

Challenge

Marketing blocked on engineering for every catalog or merchandising change. Needed hours, not days, to respond to market opportunities.

Decision

Implement headless commerce where marketing updates catalog via API. Product data becomes service contracts, not deployment dependencies.

Outcome

Time-to-publish dropped from 2-3 days to 1-2 hours. Marketing gained complete autonomy.

3

Challenge

Inventory was synced batch-wise to storefront, causing overselling during promo peaks when traffic exceeded projection.

Decision

Build event-driven inventory sync from fulfillment centers. Checkout reads from real-time inventory stream, not cached snapshots.

Outcome

Overselling eliminated. Fulfillment accuracy improved to 99.95%. No lost orders due to availability mismatch.

08 - Impact & Testimonial

The Results

Primary Business Impact

3x

Peak traffic capacity increase

Business Metrics

$220K

Prevented via 99.9% uptime on promo days

45%

Faster marketing time-to-publish

0

Overselling incidents since launch

Engineering Metrics

1.8s

Page load time (p95)

99.9%

Uptime on promo days

15x

Designed peak traffic capacity

Before & After

Promo-Day Uptime

Before

Partial outages on 2 of 3 events

↓

After

99.9% uptime on all events

Page Load Time

Before

5-8 seconds at peak

↓

After

1.8 seconds (p95)

Time-to-Publish

Before

2-3 days for catalog changes

↓

After

1-2 hours via CMS API

“Before Loam, every promo day was a disaster we braced ourselves for. Now it's just another day with higher traffic. The headless architecture freed our marketing team from waiting on engineering for every change, and the checkout service is rock-solid even at 10x normal traffic.”

Marcus Thompson

VP of Product

DTC Apparel Brand