Hex는 팀을 위해 설계된 AI 기반 분석 워크스페이스입니다. Python 및 SQL용 노트북, 대화형 데이터 앱, 셀프 서비스 탐색 기능을 단일 협업 플랫폼에 통합하여 더 빠르고 데이터 기반의 의사 결정을 가능하게 합니다.
Streamlit은 개발자와 데이터 과학자가 머신러닝 및 데이터 과학을 위한 아름다운 맞춤형 웹 앱을 몇 분 만에 구축하고 공유할 수 있게 해주는 오픈 소스 Python 프레임워크입니다. Streamlit Community Cloud는 이러한 공개 애플리케이션을 배포, 관리하고 전 세계와 공유할 수 있는 무료 플랫폼을 제공하여 협업 혁신 환경을 조성합니다.
제품 개요
Hex 제품 개요
Hex는 팀을 위해 설계된 AI 기반 분석 워크스페이스입니다. Python 및 SQL용 노트북, 대화형 데이터 앱, 셀프 서비스 탐색 기능을 단일 협업 플랫폼에 통합하여 더 빠르고 데이터 기반의 의사 결정을 가능하게 합니다.
Streamlit 제품 개요
Streamlit은 개발자와 데이터 과학자가 머신러닝 및 데이터 과학을 위한 아름다운 맞춤형 웹 앱을 몇 분 만에 구축하고 공유할 수 있게 해주는 오픈 소스 Python 프레임워크입니다. Streamlit Community Cloud는 이러한 공개 애플리케이션을 배포, 관리하고 전 세계와 공유할 수 있는 무료 플랫폼을 제공하여 협업 혁신 환경을 조성합니다.
Detailed feature comparison
Hex vs Streamlit monthly traffic
Compare Hex and Streamlit by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the Hex vs Streamlit monthly traffic comparison, Hex currently shows 600.6K visits and Streamlit shows 918.5K; Streamlit has about 1.5 times the visible traffic of Hex, an absolute difference of about 317.8K visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
Hex monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 522K 월 방문
- 2026/1: 602.8K 월 방문
- 2026/2: 579.1K 월 방문
- 2026/3: 660.6K 월 방문
- 2026/4: 585.6K 월 방문
- 2026/5: 600.6K 월 방문
주요 지역
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 77.44% | 465.1K |
| 🇨🇦Canada | 8.61% | 51.7K |
| 🇬🇧United Kingdom | 6.11% | 36.7K |
| 🇪🇸Spain | 4.14% | 24.9K |
| 🇲🇽Mexico | 3.7% | 22.2K |
트래픽 소스
| Source type | Percentage | Traffic |
|---|---|---|
| 직접 | 88.79% | 533.3K |
| 리퍼럴 | 8.56% | 51.4K |
| 이메일 | 2.65% | 15.9K |
검색 키워드
Streamlit monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 501.6K 월 방문
- 2026/1: 717.2K 월 방문
- 2026/2: 702.3K 월 방문
- 2026/3: 847.5K 월 방문
- 2026/4: 862.8K 월 방문
- 2026/5: 918.5K 월 방문
주요 지역
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 44.68% | 410.4K |
| 🇮🇳India | 25.68% | 235.9K |
| 🇰🇷Korea, Republic of | 12.59% | 115.6K |
| 🇬🇧United Kingdom | 8.9% | 81.7K |
| 🇵🇰Pakistan | 8.15% | 74.9K |
트래픽 소스
| Source type | Percentage | Traffic |
|---|---|---|
| 직접 | 57.82% | 531.1K |
| 리퍼럴 | 40.37% | 370.8K |
| 이메일 | 1.81% | 16.6K |
검색 키워드
Usage comparison
Compare the core capabilities of Hex and Streamlit
Hex Core features
Streamlit Core features
Use cases
Hex Use cases
Streamlit Use cases
Hex vs Streamlit:In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth Hex vs Streamlit comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Hex is primarily listed under “데이터 과학”, while Streamlit is primarily listed under “데이터 시각화”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (Hex: 데이터 과학; Streamlit: 데이터 시각화); Monthly visits (Hex: 600.6K; Streamlit: 918.5K); Monthly growth (Hex: 2.6%; Streamlit: 6.5%); Favorites (Hex: 128; Streamlit: 125); Website (Hex: hex.tech; Streamlit: share.streamlit.io). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the Hex vs Streamlit monthly traffic comparison, Hex currently shows 600.6K visits and Streamlit shows 918.5K; Streamlit has about 1.5 times the visible traffic of Hex, an absolute difference of about 317.8K visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
If public market visibility is an important first-pass criterion, investigate Streamlit first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Product positioning, use cases, and roles
Hex and Streamlit currently overlap in shared categories: 로우코드 노코드; shared tags: 대시보드, 데이터 과학, 데이터 시각화, 기계 학습 및 파이썬. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Hex's unique categories/tags are 데이터 과학, 협업, AI 비서, 비즈니스 인텔리전스, 데이터 분석, 노트북, 보고서 및 SQL; Streamlit's are 데이터 시각화, 앱 빌더, 개발자 도구, 오픈 소스 및 웹 앱. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
What ratings, comments, and favorites can tell you
Hex has no verified rating, 0 comments, 128 favorites, and 130 likes;Streamlit has no verified rating, 0 comments, 125 favorites, and 125 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate Hex first
Put Hex on the priority trial list when the task aligns with “데이터 과학” and especially 데이터 과학, 협업, AI 비서, 비즈니스 인텔리전스, 데이터 분석 및 노트북. This follows recorded positioning and does not imply unlisted capabilities are absent.
Hex also currently records: pricing is freemium, product type is website, 600.6K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
When to evaluate Streamlit first
Put Streamlit on the priority trial list when the task aligns with “데이터 시각화” and especially 데이터 시각화, 앱 빌더, 개발자 도구, 오픈 소스 및 웹 앱. This follows recorded positioning and does not imply unlisted capabilities are absent.
Streamlit also currently records: pricing is freemium, product type is website, 918.5K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
How to validate the recommendation before deciding
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in Hex and Streamlit, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.




