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AI · alternatives · 2026

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24 alternatives to Drag and drop neural nets

Drag and Drop Neural Nets is an AI tool that allows users to build neural networks through a visual drag-and-drop interface rather than writing code. Launched in 2016, it simplifies the process of creating machine… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Drag and drop neural nets launched in 2016; newer entries below may have overtaken it.

  1. 1
    Lobe▲1,236

    Visual tool for building custom deep learning models

    2018 · github.com · its alternatives →

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    The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.

    2020 · github.com · its alternatives →

  4. 4
    Lobe 2.0▲483

    Machine learning made easy

    2020 · github.com · its alternatives →

  5. 5
    Dragon▲101

    A real-time drag and drop project management tool.

    2020 · real-time-dragon.herokuapp.com · its alternatives →

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    An action based drag and drop container for Svelte - isaacHagoel/svelte-dnd-action

    2021 · github.com · its alternatives →

  7. 7

    Build AI Models Visually Without Code

    2025 · its alternatives →

  8. 8

    Design and build AI architectures

    2024 · neuralhub.ai · its alternatives →

  9. 9

    A few months ago, I started making video clips with stable diffusion and noticed that the tools to do this were too complicated for everyday people. That's why I built neural frames. Enjoy.

    2023 · neuralframes.com · its alternatives →

  10. 10

    Build internal tools and backend operations software with AI

    2024 · its alternatives →

  11. 11
    DROP▲422

    I'm the founder of DROP. I've been plugging away at this project for over six months now. We managed to secure funding, which was a huge relief. You see, I had a previous startup called cowtransfer.com. It was a file transfer gig, kind of like WeTransfer. I had a good run with it, but in 2021, I decided to sell it and move on. Now, jumping back into the digital assets arena was like starting from scratch. I kept asking myself, what does the AI creative industry really hunger for? How can AI swoop in and make their creative content production process a breeze? I mean, it's like trying to…

    2025 · drop.space · its alternatives →

  12. 12AV

    Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by train

    2016 · jalammar.github.io · its alternatives →

  13. 135N
  14. 14

    2020 · colab.research.google.com · its alternatives →

  15. 15

    Build models in clicks using drag and drop

    2025 · scalifiai.com · its alternatives →

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    PixelFlowfree tier, from $10/mo▲34

    Drag, Drop, Deploy: Image and Video GenAI workflows

    2024 · segmind.com · its alternatives →

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    FlashML▲15

    Drag & Drop Machine Learning Models w/ PyTorch Flexibility.

    2020 · its alternatives →

  19. 19
    DragGAN▲192

    Interactive point-based manipulation of images

    2023 · vcai.mpi-inf.mpg.de · its alternatives →

  20. 20

    A preconfigured Deep Learning drag and drop platform

    2017 · signalbox.ai · its alternatives →

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    Play BangBang against a Neural Network. Contribute to victorqribeiro/bangBang development by creating an account on GitHub.

    2022 · github.com · its alternatives →

  23. 23

    The most simple neural network plotter

    2018 · github.com · its alternatives →

  24. 24

    I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…

    2024 · github.com · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →