Software Alternatives, Accelerators & Startups

Supervisely VS CloudCLI

Compare Supervisely VS CloudCLI and see what are their differences

Supervisely logo Supervisely

Supervisely helps people with and without machine learning expertise to create state-of-the-art...

CloudCLI logo CloudCLI

Shared cloud environments for AI coding agents. Run Claude Code, Cursor CLI, Codex, and Gemini CLI from any device, API, or automation tool.
Visit Website
  • Supervisely Landing page
    Landing page //
    2023-08-06
  • CloudCLI CloudCLI Dashboard
    CloudCLI Dashboard //
    2026-04-01
  • CloudCLI CloudCLI Web IDE
    CloudCLI Web IDE //
    2026-04-01
  • CloudCLI Opening your dev environment on VSCode
    Opening your dev environment on VSCode //
    2026-04-01
  • CloudCLI Opening an environment on your mobile
    Opening an environment on your mobile //
    2026-04-01

Most engineering teams run AI coding agents on individual laptops. Close the lid, lose the session. When a new developer joins, they spend hours recreating the same setup.

CloudCLI gives your team shared cloud environments where AI agents run 24/7. Every developer gets their own isolated container, but the team shares MCP servers, context files, and configurations across all projects. Onboarding takes minutes.

Sessions can be started through a full REST API, so workflows in Linear, Jira, or n8n can trigger background coding agents programmatically. A ticket gets filed, an agent starts coding, the developer reviews the PR in the morning.

The web UI and mobile interface include a file explorer, git explorer, and full shell access. Review PRs on your iPad, make fixes from your phone, then pick up in VS Code over SSH.

Unlike GitHub Codespaces, CloudCLI is purpose-built for agentic development. Claude Code, Cursor CLI, Codex, and Gemini CLI come pre-installed. Sessions survive laptop closure. Teams bring their own API keys with no vendor lock-in.

Built on an open-source core (AGPL-3, 9,000+ GitHub stars). Self-host for data sovereignty or use the managed service from โ‚ฌ7/month.

CloudCLI

$ Details
paid Free Trial โ‚ฌ7.0 / Monthly
Platforms
Web Mobile
Startup details
Country
Netherlands
State
Zuid Holland
Founder(s)
Simos Mikelatos
Employees
1 - 9

Supervisely features and specs

  • Comprehensive Toolset
    Supervisely offers a wide range of tools for image annotation, data management, and deep learning model training, providing a one-stop solution for computer vision projects.
  • Collaborative Platform
    It supports team collaboration with features for sharing projects, annotating data, and reviewing work, making it easier for teams to work together.
  • High Customizability
    Supervisely allows users to create custom plugins and automation scripts, offering flexibility to tailor the platform according to specific project needs.
  • Extensive Dataset Support
    The platform supports a wide variety of data formats and types, including images, videos, and 3D data, making it versatile for different applications.
  • Integrated Machine Learning
    Supervisely integrates machine learning capabilities, enabling users to train models directly on the platform and test them using their own annotated data.

Possible disadvantages of Supervisely

  • Cost
    Supervisely can be expensive, particularly for small teams or individual users, as it primarily targets enterprise customers.
  • Complexity
    Due to the breadth of features and tools, there may be a steep learning curve for new users, making it more challenging to get started quickly without adequate training.
  • Performance Issues
    Some users may experience performance issues, particularly when handling very large datasets or running multiple simultaneous tasks.
  • Cloud Dependency
    While a cloud-based platform offers accessibility advantages, it also means that users are dependent on internet connectivity and may face latency or downtime problems.
  • Limited Offline Features
    Supervisely's offline functionality is limited, which can be a drawback for users who need to work in environments with restricted or unreliable internet access.

CloudCLI features and specs

  • Multi-Agent Support
    Run Claude Code, Cursor CLI, OpenAI Codex, and Gemini CLI side by side. Bring your own API keys. No vendor lock-in.
  • Git Integration
    Manage branches, view commit history, and browse files with syntax highlighting directly from the browser or mobile app.
  • Persistent Cloud Sessions
    agents keep running 24/7. Close your laptop, switch devices, or walk away entirely and your session survives with full context intact
  • Web UI & Mobile App
    Chat with agents, browse files, manage git branches, and monitor sessions from a browser or phone. No VS Code required.
  • Cross-Device Sync
    Start planning a feature on your phone, pick up the same session in VS Code at your desk, or kick off from a Linear ticket and continue in your IDE.
  • Plugin Ecosystem
    Extend your workflow with plugins and MCP integrations. Customize how your agents work to fit your team's process.
  • Shared Team Environments
    Every developer gets their own isolated container while the team shares MCP servers, context files, and configurations. Onboard new developers in minutes, not hours.
  • API-Driven Session Management
    Start, stop, and manage environments through a full API. Trigger coding agents programmatically from Linear, Jira, n8n, or any automation tool.

Analysis of Supervisely

Overall verdict

  • Overall, Supervisely is a good platform for computer vision projects due to its versatility and ease of use. It offers a complete ecosystem that caters to various stages of the machine learning pipeline, making it an efficient choice for both beginners and experienced practitioners.

Why this product is good

  • Supervisely is considered a robust platform for its comprehensive suite of tools designed for computer vision tasks. It provides capabilities for data labeling, neural network training, and deployment. Its user-friendly interface, collaborative features, and support for a wide range of formats and integrations make it appealing to both individual developers and enterprise teams.

Recommended for

  • Data scientists looking for a comprehensive tool for computer vision.
  • Companies needing a collaborative environment for AI projects.
  • Researchers who require a platform with extensive format support and integrations.
  • Developers wanting an easy-to-use interface for data annotation and model training.

Analysis of CloudCLI

Overall verdict

  • CloudCLI appears to be a niche AI-powered command-line tool aimed at developers who want to interact with cloud services or AI models directly from the terminal, but there is limited independent, verifiable information available about its performance, reliability, and long-term support, so it should be evaluated cautiously and tested on a small scale before committing to it for critical workflows.

Why this product is good

  • Offers a command-line interface that can speed up developer workflows without needing to switch to a GUI or browser
  • Potentially integrates AI capabilities directly into scripting and automation pipelines
  • May reduce context-switching for developers already comfortable working in terminal environments
  • Could support faster prototyping if the tool's claimed features work as advertised

Recommended for

  • Developers who prefer terminal-based workflows over GUI tools
  • Teams experimenting with AI-assisted coding or cloud automation who want to test lightweight CLI tools
  • Early adopters comfortable with newer, less-established products
  • Users who need lightweight AI integration into existing shell scripts or CI/CD pipelines

Supervisely videos

๐Ÿ› ๏ธBasic annotation overview - Supervisely

More videos:

  • Review - Cars annotation in Supervisely: Polygons vs. AI powered tool
  • Tutorial - Yolo v3 Tutorial #2 - Object Detection Training Part 1 - Create a Supervisely Cluster

CloudCLI videos

No CloudCLI videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Supervisely and CloudCLI)
Image Annotation
100 100%
0% 0
Developer Tools
0 0%
100% 100
Data Labeling
100 100%
0% 0
Productivity
0 0%
100% 100

Questions & Answers

As answered by people managing Supervisely and CloudCLI.

Which are the primary technologies used for building your product?

CloudCLI's answer:

CloudCLI is built with a modern JavaScript/TypeScript stack:

  • Frontend: React with Vite for fast builds, Tailwind CSS for styling, and CodeMirror for the in-browser code editor with syntax highlighting
  • Backend: Node.js powering the server and session management
  • Infrastructure: Docker for containerized cloud sessions, with support for self-hosting
  • Mobile: A dedicated mobile app for managing sessions on the go

The entire codebase is open source under AGPL-3 and available on GitHub.

Why should a person choose your product over its competitors?

CloudCLI's answer:

Compared to tools like GitHub Codespaces, CloudCLI is purpose-built for agentic development rather than traditional coding. Here's what sets it apart:

  • AI-agent-first: While competitors give you a cloud IDE, CloudCLI gives your AI agents a persistent home in the cloud. Your agents keep working even when your laptop is closed.
  • Open-source web UI and mobile app: No other CDE ships with both a browser-based UI and a native mobile app for managing sessions on the go. And it's all open source.
  • Cross-device continuity: Start planning on your phone, continue in VS Code at your desk, or kick off from a Linear ticket. Your session context carries over seamlessly.
  • Multi-agent support: Run Claude Code, Cursor CLI, OpenAI Codex, and Gemini CLI from one platform instead of managing separate setups.
  • Affordable: Starting at โ‚ฌ7/month for the managed service, or self-host for free with Docker.

What makes your product unique?

CloudCLI's answer:

CloudCLI is one of the only cloud development environments built specifically for AI coding agents. Where Codespaces and Gitpod give you a cloud editor, CloudCLI gives your agents a persistent home that stays alive 24/7. What makes it particularly valuable for teams: shared MCP servers and environment configs mean every developer starts from the same baseline. A full REST API means sessions can be triggered from automation tools, not just opened manually. Background agents can run overnight and produce PRs for review in the morning. And the entire platform is open source (AGPL-3) so teams can self-host on their own infrastructure.

How would you describe the primary audience of your product?

CloudCLI's answer:

CloudCLI is built for engineering teams that use AI coding agents as part of their daily workflow. This includes teams adopting agentic development practices with tools like Claude Code, Cursor CLI, or Codex who need shared environments where MCP servers, context files, and configurations stay consistent across every developer. It also serves engineering managers looking to integrate AI agents into existing workflows through API-driven automation with tools like Linear, Jira, and n8n. Solo developers and open-source contributors who want persistent remote access from any device are also a core audience, along with organizations that need to self-host for data sovereignty or regulatory compliance.

What's the story behind your product?

CloudCLI's answer:

CloudCLI started as an open-source project to solve a problem every developer using AI coding agents hits: your agent ties up your terminal and stops working when your laptop sleeps. We built a cloud-native environment where agents run persistently, paired with an open-source web UI so anyone could manage sessions from a browser or phone. As teams started adopting it, the focus shifted to shared environments, where team-wide MCP servers, configurations, and context files could be maintained in one place instead of duplicated across every developer's machine. The project grew to 9,000+ GitHub stars organically with no marketing. Today CloudCLI offers both a free self-hosted option and a managed cloud service starting at โ‚ฌ7/month.

User comments

Share your experience with using Supervisely and CloudCLI. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Supervisely seems to be more popular. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Supervisely mentions (6)

  • Way to label yolov7 images fast
    Another annotation tool that integrates prediction and training within the application is supervisely supervisely.com., unfortunately it's pretty expensive unless you are satisfied with the community version. I saw that they have an integration for owl-vit, which might be helpful for annotation of animals. https://ecosystem.supervisely.com/apps/serve-owl-vit. Source: about 3 years ago
  • 65 Blog Posts to Learn Data Science
    Hello world. This tutorial is a gentle introduction to building modern text recognition system using deep learning in 15 minutes. It will teach you the main ideas of how to use Keras and Supervisely for this problem. This guide is for anyone who is interested in using Deep Learning for text recognition in images but has no idea where to start. - Source: dev.to / over 3 years ago
  • Bounding Box for Text Annotation
    If they were videos, I would have suggested trying supervise.ly as it has a very good tracking functionality. Source: almost 4 years ago
  • CVAT alternatives for video frame annotation
    Hi, I'm exactly in the same boat like you are. I looked around for a while and the better solutions I found was supervise.ly and CVAT for video annotation. The pricetag on supervisely is pretty high, so I analyzed CVAT for a couple days and was positively surprised. Source: almost 4 years ago
  • Accessing 2022 Machine Learning Imagery from WPI's Photo Album
    Under the WPI Photo Ambum section of the page for FRC field photos (https://www.firstinspires.org/robotics/frc/playing-field#WPIPhotos), they have a section of machine learning imagery. However, this link goes to supervise.ly, the website they use for machine learning. I created an account to attempt to download the images, however, whenever I try to 'clone' the project, it stalls at 0% and gives me an error... Source: about 4 years ago
View more

CloudCLI mentions (0)

We have not tracked any mentions of CloudCLI yet. Tracking of CloudCLI recommendations started around Mar 2026.

What are some alternatives?

When comparing Supervisely and CloudCLI, you can also consider the following products

Labelbox - Build computer vision products for the real world

GitHub Codespaces - GItHub Codespaces is a hosted remote coding environment by GitHub based on Visual Studio Codespaces integrated directly for GitHub.

Universal Data Tool - Machine learning, data labeling tool, computer vision, annotate-images, classification, dataset

Gitpod - One click dev environment for GitHub

CrowdFlower - Enterprise crowdsourcing for micro-tasks

Qoder IDE - Qoder is an AI-powered agentic coding platform and IDE that automates complex software development tasks using autonomous AI agents.