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Dev Korea

Tech stack

The languages, frameworks, and platforms developers in and around Korea actually use.

Starts with 2 cross-tabs, or jump to the 5 individual answers ↓

Cross-tab

The languages that pay best are the ones fewest people use

Median compensation by programming language used

Go sits at a median of ₩100–120M and Bash at ₩80–90M, while JavaScript, TypeScript and HTML/CSS all land at ₩50–60M. Python, SQL and Java sit in between at ₩60–70M. Rows share respondents: almost everyone here uses several languages, so this ranks the people who use a language, not the language itself.

Median total compensation

median answer middle half of the group

Go

₩100–120M · n=10

Bash/Shell

₩80–90M · n=30

Python

₩60–70M · n=45

SQL

₩60–70M · n=30

Java

₩60–70M · n=13

JavaScript

₩50–60M · n=36

TypeScript

₩50–60M · n=39

HTML/CSS

₩50–60M · n=34
Under ₩20M ₩400M+

Every answer is one step, so a position is a rank on the scale rather than a point on a linear axis.

Language tracks the job, not the paycheque: Go and Bash cluster around backend and infrastructure work at foreign employers, where the pay gap already showed up. Go rests on ten pay answers and its middle half spans ₩70–80M to ₩120–140M, so read the ordering rather than the gaps. Languages with fewer than ten pay answers, Kotlin and Rust among them, are not shown.

10 groups with fewer than 10 answers are not shown (93 respondents).

Groups differ in more than the split shown, so read these as descriptions, not causes.

Cross-tab

Job hunters write Python, the employed write TypeScript

Languages used, by whether people are working

76% of students and job-seekers use Python against 59% of people currently working, while TypeScript runs the other way: 40% against 54%. That fits what each group spends its days on, coursework and machine-learning portfolios on one side, product and web work on the other.

Use Python

Currently working

59% · n=76

Studying or job-hunting

76% · n=55

Use TypeScript

Currently working

54% · n=76

Studying or job-hunting

40% · n=55

Each gap is around 15 points with margins near ±12, so this is a lean, not a rule.

Groups differ in more than the split shown, so read these as descriptions, not causes.

Every question in this chapter

Programming languages

131 answered · multiple answers allowed

Python

87 · 66%

JavaScript

71 · 54%

HTML/CSS

66 · 50%

TypeScript

63 · 48%

SQL

58 · 44%

Bash/Shell

43 · 33%

Java

26 · 20%

C++

20 · 15%

Go

14 · 11%

Kotlin

13 · 10%

C#

13 · 10%

C

12 · 9%

Other

10 · 8%

Rust

9 · 7%

Swift

9 · 7%

PHP

7 · 5%

Dart

7 · 5%

Elixir

2 · 2%

Scala

1 · 1%

Web & backend frameworks

131 answered · multiple answers allowed

React

65 · 50%

Next.js

48 · 37%

FastAPI

45 · 34%

Spring / Spring Boot

25 · 19%

Express

22 · 17%

None

22 · 17%

NestJS

21 · 16%

Django

20 · 15%

Other

17 · 13%

Flask

12 · 9%

Vue.js

12 · 9%

jQuery

11 · 8%

HTMX

7 · 5%

Angular

6 · 5%

Svelte

3 · 2%

Laravel

2 · 2%

Nuxt

2 · 2%

Other frameworks & libraries

131 answered · multiple answers allowed

NumPy

45 · 34%

Pandas

39 · 30%

PyTorch

34 · 26%

None

31 · 24%

OpenCV

26 · 20%

Scikit-learn

25 · 19%

TensorFlow

23 · 18%

Other

23 · 18%

React Native

20 · 15%

Flutter

18 · 14%

Apache Kafka

16 · 12%

.NET

13 · 10%

RabbitMQ

11 · 8%

Cloud platforms

131 answered · multiple answers allowed

AWS

58 · 44%

Google Cloud

37 · 28%

Vercel

30 · 23%

Cloudflare

27 · 21%

None

27 · 21%

Supabase

26 · 20%

Azure

25 · 19%

Firebase

19 · 15%

Other

11 · 8%

Heroku

7 · 5%

Hetzner

5 · 4%

DigitalOcean

4 · 3%

Naver Cloud (NCP)

4 · 3%

Social media

131 answered · multiple answers allowed

LinkedIn

117 · 89%

Instagram

88 · 67%

KakaoTalk

84 · 64%

X/Twitter

48 · 37%

Facebook

39 · 30%

Reddit

38 · 29%

Threads

14 · 11%

TikTok

13 · 10%

Other

13 · 10%

Mastodon

5 · 4%

Bluesky

4 · 3%

None

4 · 3%