AI · alternatives · 2026

24 alternatives to Compile ML models into dependency-free source code for easy deployment

I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…

This tool converts machine learning models into standalone source code without external dependencies, making it easier to deploy models on mobile apps, smartwatches, and microcontroller firmware. Users can submit Keras… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Compile ML models into dependency-free source code for easy deployment launched in 2023; newer entries below may have overtaken it.

1
Deploy ML Models on a Budget▲117
Deploy a ML inference service on a budget in less than 10 lines of code. - ebhy/budgetml
2021 · github.com · its alternatives →
2
Inferless▲749
Deploy any machine learning models in minutes
2025 · inferless.com · its alternatives →
3
MakeML▲274
Train Neural Networks without a line of code
2019 · makeml.app · its alternatives →
4HP
High-performance ahead-of-time compiler for Machine Learning▲83
2018 · scss.tcd.ie · its alternatives →
5
Igel – A CLI tool to run machine learning without writing code▲405
a delightful machine learning tool that allows you to train, test, and use models without writing code - nidhaloff/igel
2020 · github.com · its alternatives →
6
Fullstack ML – From Notebooks to Deployment▲198
Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
2019 · github.com · its alternatives →
7
Machine learning automation from creating to using models in production▲72
a delightful machine learning tool that allows you to train, test, and use models without writing code - GitHub - nidhaloff/igel at v0.4.0
2021 · github.com · its alternatives →
8
Plexe▲273
Build and deploy ML models in English
Oct 2025 · plexe.ai · its alternatives →
9
ML From Scratch – free online textbook▲259
2020 · dafriedman97.github.io · its alternatives →
10
ML Showcase▲214
A curated collection of machine learning projects
2017 · its alternatives →
11
Model Zoo▲74
Deploy your machine learning model in a single line of code.
2020 · its alternatives →
12
Truss – Serve any ML model without boilerplate code▲68
The simplest way to serve AI/ML models in production - basetenlabs/truss
2022 · github.com · its alternatives →
13
Gradio 5.0▲224
connect nodes to build AI pipelines, powered by Hugging Face
2024 · gradio.app · its alternatives →
14
MLConsole – web app to train ML models, for free and client-side▲86
ML Console is a free tool to build an AI model without writing a single line of code. Simply pick a column to predict, and let us handle the rest.
2021 · mlconsole.com · its alternatives →
15
I built a tensor library from scratch in C++/CUDA▲119
Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
2025 · github.com · its alternatives →
16
KeaML Deployments▲96
Deploy machine learning models with one line of code
2023 · keaml.com · its alternatives →
17
Cortex▲81
Deploy, manage, and scale machine learning applications
2019 · its alternatives →
18
ML Blocks – Deploy multimodal AI workflows without code▲112
Hey everyone, ML Blocks is a node-based workflow builder to create multi-modal AI workflows without writing any code. You connect blocks that call various visual models like GPT4v, Segment Anything, Dino etc. along with basic image processing blocks like resize, invert color, blur, crop, and several others. The idea is to make it easier to deploy multi-step image processing workflows, without needing to spin up endless custom OpenCV cloud functions to glue together AI models. Usually, even if you're using cloud inference servers like Replicate, you still need to write your own image…
2024 · mlblocks.com · its alternatives →
19
Build Machine Learning Web-Service with Python and Django▲67
My Machine Learning Web Service. Contribute to pplonski/my_ml_service development by creating an account on GitHub.
2020 · github.com · its alternatives →
20
Plexe – ML Models from a Prompt▲130
Hey HN! We’re Vaibhav and Marcello. We’re building Plexe (https://github.com/plexe-ai/plexe), an open-source agent that turns natural language task descriptions into trained ML models. Here’s a video walkthrough: https://www.youtube.com/watch?v=bUwCSglhcXY. There are all kinds of uses for ML models that never get realized because the process of making them is messy and convoluted. You can spend months trying to find the data, clean it, experiment with models and deploy to production, only to find out that your project has been binned for taking so long.…
2025 · github.com · its alternatives →
21
Baseten – Build ML-powered applications▲112
Hi HN, Three months ago, I took a job at Baseten to help craft and document an application builder that lets data scientists build full-stack, production-ready applications around their ML models without worrying about containers, Flask, or React. From my first day, everyone was focused on what would happen today: opening up our public beta. I’m super excited to see what you build with Baseten. If you want to take Baseten for a full-speed test drive, follow along with this tutorial, where you can build and deploy an application in 20 minutes:…
2022 · baseten.co · its alternatives →
22BP
Build predictive models with no prior ML experience▲70
TLDR: Product developers need tools designed for them to build ML models. We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding My name is Tommy DANGerous (or Tommy Dang) and I’m the CEO and co-founder at Mage. I worked at Airbnb for over 5 years as a product developer building features for guests. Mage is a web-based tool for building, training, and deploying ML models that make predictions based off your data. Training and using ML models in production typically requires working knowledge of building data pipelines, algorithms,…
2022 · its alternatives →
23
I'm building a simple way to package, collaborate, and deploy ML models▲9
Heya HN, after spending +1 year building an ML-driven analytics product (that didn't pan out unfortunately), I've pivoted to solving a problem my team and I found while building the previous product … why the hell is it so hard to move a model from a Jupyter notebook, to a development server, then to a production pipeline!? To solve this my team and I started the open source KitOps project under the Apache 2 license. KitOps includes the Kit CLI that uses a Kitfile manifest to create ModelKits: 1. The kit CLI packages your model, datasets, code, and configuration into an OCI compliant…
2024 · kitops.ml · its alternatives →
24MZ
Model Zoo: Deploy your machine learning model in a single line of code▲34
Informasi Akurat, Berita Terupdate – Hanya di Sini
2020 · modelzoo.dev · 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 →