How I Reduced My Docker Image Size by 90% Using Multi-Stage Builds

Vipin Yadav — Wed, 28 Jan 2026 17:06:58 +0000

Optimizing Docker Images: From Gigabytes to Megabytes

Docker images bloating to several gigabytes? Your container deployments taking forever? Multi-stage builds are the key to shrinking images, as seen in this transition from 1.2GB to 120MB.

The Problem

Standard Dockerfile builds include everything: build tools, dev dependencies, source files, and compilation artifacts. Your production container doesn't need any of that.

Before (1.2GB):

FROM node:18
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
RUN npm run build
CMD ["node", "dist/server.js"]

The Solution: Multi-Stage Builds

Multi-stage builds allow the use of multiple FROM statements. Each stage can copy artifacts from previous stages while leaving behind everything else.

After (120MB):

# Stage 1: Build
FROM node:18 AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
RUN npm run build

# Stage 2: Production
FROM node:18-alpine
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY package*.json ./
USER node
CMD ["node", "dist/server.js"]

Key Improvements

Alpine Base Image
node:18 ≈ 900MB
node:18-alpine ≈ 110MB
Production Dependencies Only
npm ci --only=production excludes devDependencies.
No TypeScript, testing frameworks, or linters in the production layer.
Selective Copying
Only copies dist/, node_modules/, and necessary config files.

Real-World Examples

1. Go Application

Go benefits significantly as the entire compiler is excluded from the final image.
Result: 800MB → 15MB

# Build stage
FROM golang:1.21 AS builder
WORKDIR /app
COPY go.mod go.sum ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux go build -o main .

# Production stage
FROM alpine:latest
RUN apk --no-cache add ca-certificates
WORKDIR /root/
COPY --from=builder /app/main .
CMD ["./main"]

2. Python with pip

FROM python:3.11 AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --user -r requirements.txt

FROM python:3.11-slim
WORKDIR /app
COPY --from=builder /root/.local /root/.local
COPY . .
ENV PATH=/root/.local/bin:$PATH
CMD ["python", "app.py"]

3. Java with Maven

FROM maven:3.9-openjdk-17 AS builder
WORKDIR /app
COPY pom.xml .
RUN mvn dependency:go-offline
COPY src ./src
RUN mvn package -DskipTests

FROM openjdk:17-jdk-slim
COPY --from=builder /app/target/*.jar app.jar
CMD ["java", "-jar", "app.jar"]

Best Practices & Performance

Optimization Tips

Order Layers by Change Frequency: Place COPY package*.json before COPY . to leverage layer caching.
Use .dockerignore: Exclude node_modules, .git, and environment files.
Combine Commands: Use && and \ to reduce the number of layers in a single stage.

Performance Impact

Metric	Impact
Build Time (Subsequent)	60% Faster
Push/Pull Time	~85-90% Faster
Disk Space	90% Reduction
Memory Usage	40% Reduction

Debugging Multi-Stage Builds

You can build or inspect specific stages using the --target flag:

# Build specific stage for debugging
docker build --target builder -t myapp:debug .

# Inspect intermediate layers
docker history myapp:builder

Conclusion

The initial investment in refactoring your Dockerfile pays dividends in performance, cost, and developer experience. Start with your largest images first—that's where you'll see the biggest wins.

What size reduction did you achieve? Share your results below!

#docker #devops #containers #performance

How I Built LaunchTrack: An AI Career Coach with Next.js and Gemini API

Vipin Yadav — Fri, 16 Jan 2026 04:30:00 +0000

Hey everyone! 👋

I'm excited to share my latest project - **LaunchTrack**, an AI-powered career coaching platform. In this post, I'll walk you through how I built it and the technologies I used.

## 🚀 What is LaunchTrack?

LaunchTrack is an AI career coach that helps job seekers with:

- Personalized job search support
- Interview preparation
- Resume analysis
- Career advice powered by AI

**Live Demo:** [launchtrack.vercel.app](https://launchtrack.vercel.app)
**GitHub:** [github.com/vipinyadav01/aicareercoach](https://github.com/vipinyadav01/aicareercoach)

## 🛠️ Tech Stack

- **Frontend:** Next.js 14 (App Router)
- **AI:** Google Gemini API
- **Database:** Prisma + PostgreSQL
- **Authentication:** Clerk
- **Styling:** Tailwind CSS + Shadcn UI
- **State Management:** Zustand
- **Background Jobs:** Inngest

## 💡 Key Features

### 1. AI-Powered Career Advice

Using Google Gemini API, users get personalized career guidance based on their profile and goals.

### 2. Resume Analysis

Upload your resume and get instant AI feedback on how to improve it.

### 3. Interview Preparation

Practice with AI-generated interview questions tailored to your target role.

### 4. PWA Support

Works offline and can be installed as a mobile app!

## 🔧 Architecture Overview

┌─────────────────────────────────────────────┐
│ Next.js App │
├─────────────────────────────────────────────┤
│ Pages │ API Routes │ Server Actions │
├─────────────────────────────────────────────┤
│ Prisma ORM │
├─────────────────────────────────────────────┤
│ PostgreSQL │ Gemini API │ Inngest │
└─────────────────────────────────────────────┘


## 🎯 Challenges & Solutions

### Challenge 1: Rate Limiting Gemini API
**Solution:** Implemented request queuing with Inngest background jobs.

### Challenge 2: Streaming AI Responses
**Solution:** Used Next.js Edge Runtime with streaming responses.

### Challenge 3: PWA Offline Support
**Solution:** Configured next-pwa with custom service worker caching.

## 📊 Results

- ⚡ **Lighthouse Score:** 95+
- 📱 **PWA:** Installable on all devices
- 🔒 **Security:** A+ rating

## 🔗 Links

- **Portfolio:** [devxvipin.me](https://devxvipin.me)
- **GitHub:** [github.com/vipinyadav01](https://github.com/vipinyadav01)
- **LinkedIn:** [linkedin.com/in/vipinyadav01](https://linkedin.com/in/vipinyadav01)

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Thanks for reading! Let me know if you have any questions in the comments.

Follow me for more web development content! 🚀

#nextjs #ai #webdev #javascript

Forem: Vipin Yadav