Software Alternatives, Accelerators & Startups

neptune.ai VS DevBox

Compare neptune.ai VS DevBox and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

neptune.ai logo neptune.ai

Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

DevBox logo DevBox

Everyday utilities for the everyday developer
  • neptune.ai Landing page
    Landing page //
    2023-08-24

Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.

Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code

Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

  • DevBox Landing page
    Landing page //
    2023-05-18

neptune.ai

Website
neptune.ai
$ Details
freemium
Platforms
Python
Release Date
2018 April
Startup details
Country
Poland
State
Mazowieckie
City
Warsaw
Founder(s)
Piotr Niedzwiedz
Employees
10 - 19

DevBox

Pricing URL
-
$ Details
-
Platforms
-
Release Date
-

neptune.ai features and specs

  • Experiment Tracking
    Neptune.ai provides comprehensive tools for tracking machine learning experiments, which helps in organizing and managing multiple experiments efficiently.
  • Collaboration Features
    The platform offers collaboration features that allow multiple team members to contribute and monitor the progress of ongoing projects.
  • Integration Capability
    Neptune.ai integrates well with popular machine learning libraries and tools, enabling seamless workflow integration into existing processes.
  • Interactive Dashboard
    It provides a user-friendly interface and interactive dashboard for visualizing and analyzing experiment results, which aids in better decision-making.
  • Model Registry
    Neptune.ai includes a model registry feature that facilitates the management and deployment of machine learning models.

Possible disadvantages of neptune.ai

  • Pricing
    Some users might find the pricing model expensive, especially for small teams or individual users, although they offer a free tier with limited features.
  • Learning Curve
    New users might experience a learning curve when getting started with Neptune.ai due to the rich set of features and capabilities.
  • Limited Offline Access
    The platform primarily functions online, which limits its usability in environments with restricted internet access.
  • Integration Complexity
    While the platform offers numerous integrations, setting them up might be complex and time-consuming for users unfamiliar with such processes.
  • Technical Support
    Some users have reported that the response time for technical support could be improved, especially for immediate assistance needs.

DevBox features and specs

  • Streamlined Setup
    DevBox offers a streamlined setup process that helps developers get their environment running quickly without the hassle of configuring complex project settings.
  • Cross-Platform Support
    It supports multiple operating systems, allowing developers to work seamlessly across Windows, MacOS, and Linux.
  • Cloud Integration
    DevBox integrates well with cloud platforms, enabling easy deployment and testing of applications in scalable environments.
  • Pre-Built Environments
    Provides pre-built development environments which save time in configuration and ensure consistency across different development teams.
  • Collaboration Features
    DevBox includes collaboration tools that facilitate teamwork, making it easier to share settings and work in real-time with others.

Possible disadvantages of DevBox

  • Limited Customization
    Some users may find the customization options limited compared to manually setting up development environments, which could restrict specific needs or preferences.
  • Dependency on Internet Connection
    As DevBox relies on cloud-based solutions, a stable internet connection is essential, which might be a limitation in areas with poor network coverage.
  • Cost
    The subscription model or usage fees could be a concern for individual developers or smaller teams with limited budgets.
  • Learning Curve
    While DevBox simplifies some processes, new users might encounter a learning curve to fully understand and utilize its features effectively.
  • Potential Performance Bottlenecks
    Depending on the configuration and network speed, there might be performance issues, especially when working with large-scale projects or heavy computational tasks.

neptune.ai videos

Machine Learning Experiment Management with Neptune.ai - How to start

DevBox videos

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

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Category Popularity

0-100% (relative to neptune.ai and DevBox)
Data Science And Machine Learning
Developer Tools
0 0%
100% 100
Data Science Notebooks
100 100%
0% 0
Productivity
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare neptune.ai and DevBox

neptune.ai Reviews

  1. anonymous for now
    Easy to use, not overdone, good for model management and collab

    Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group

DevBox Reviews

We have no reviews of DevBox yet.
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Social recommendations and mentions

Based on our record, neptune.ai seems to be more popular. It has been mentiond 24 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.

neptune.ai mentions (24)

  • Understanding the MLOps Lifecycle
    Some tools for model validation include Neptune AI, Kolena, and Censius. - Source: dev.to / 5 months ago
  • A step-by-step guide to building an MLOps pipeline
    Experiment tracking tools like MLflow, Weights and Biases, and Neptune.ai provide a pipeline that automatically tracks meta-data and artifacts generated from each experiment you run. Although they have varying features and functionalities, experiment tracking tools provide a systematic structure that handles the iterative model development approach. - Source: dev.to / 11 months ago
  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Neptune.ai - Log, store, display, organize, compare, and query all your MLOps metadata. Free for individuals: 1 member, 100 GB of metadata storage, 200h of monitoring/month. - Source: dev.to / about 1 year ago
  • Show HN: A gallery of dev tool marketing examples
    Hi I am Jakub. I run marketing at a dev tool startup https://neptune.ai/ and I share learnings on dev tool marketing on my blog https://www.developermarkepear.com/. Whenever I'd start a new marketing project I found myself going over a list of 20+ companies I knew could have done something well to “copy-paste” their approach as a baseline (think Tailscale, DigitalOCean, Vercel, Algolia, CircleCi, Supabase,... - Source: Hacker News / over 1 year ago
  • How to structure/manage a machine learning experiment? (medical imaging)
    There are a lot of tools out there for experiment tracking (eg neptune.ai), but I'm really not sure whether that sort of thing is over the top for what I need to do. Source: over 1 year ago
View more

DevBox mentions (0)

We have not tracked any mentions of DevBox yet. Tracking of DevBox recommendations started around Aug 2021.

What are some alternatives?

When comparing neptune.ai and DevBox, you can also consider the following products

Comet.ml - Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

Flox - Manage and share development environments with all the frameworks and libraries you need, then publish artifacts anywhere. Harness the power of Nix.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

devenv - Fast, Declarative, Reproducible, and Composable dev envs

Weights & Biases - Developer tools for deep learning research

Podman - Simple debugging tool for pods and images