How Slack Cut Frontend Build Times by 60%

A Case Study in DevXP, CI/CD Optimization, and Developer Joy

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In large engineering orgs speed to deploy can be the difference between flow and frustration. At Slack, the DevXP team spotted a subtle but costly inefficiency in their CI/CD pipeline: frontend builds were running even when no frontend code had changed.

The result? Wasted compute, slower merges, and gigabytes of redundant data clogging up AWS S3.

Let’s break down how they turned this silent productivity killer into a 60% efficiency gain without writing any brand-new tools.

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🚀 TL;DR

Slack engineers optimized their E2E testing pipeline by skipping unnecessary frontend builds. They used git diff to detect relevant code changes, reused recent assets via an internal CDN, and cut build frequency by 60% saving time, compute, and terabytes of cloud storage.

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Here’s what we’ll cover:

1. What Happened

2. The Fix - Don’t Build What You Don’t Need

3. What Made This Hard?

4. The Results

5. Lessons For Devops and infra teams

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1️⃣ 🛠️ What Happened?

Slack’s main monorepo runs full end-to-end tests before merging any code. This covers everything from backend logic to the frontend UI. But there was a catch:

Every PR triggered a full frontend build. Even if the changes were in backend-only files.

🕐 Each frontend build took ~5 minutes
📦 Each artifact was nearly 1GB
📈 Multiply that by hundreds of PRs per day

You get:
❌ Redundant builds
❌ Slower pipelines
❌ Expensive cloud usage

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2️⃣ 🧠 The Fix: Don’t Build What You Don’t Need

Instead of overhauling their system, Slack made two smart tweaks:

Step 1: Check Before You Build
They used git diff to detect whether a PR touched any frontend files. If not, the pipeline skipped the build.

Step 2: Reuse What You Already Have
They fetched a recent, still-in-production build from AWS S3 and served it via an internal CDN. This meant:

• No new build

• No storage bloat

• No time wasted

  

⚡ Average decision time: under 3 seconds
📉 Build frequency: down 60%

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3️⃣ 🧱 What Made This Hard?

Scaling this across a monorepo with 100,000+ files came with challenges: