Dev tools · alternatives · 2026

24 alternatives to Deploy ML Models on a Budget

Deploy a ML inference service on a budget in less than 10 lines of code. - ebhy/budgetml

Deploy ML Models on a Budget is a development tool for machine learning engineers and data scientists looking to reduce infrastructure costs. Launched in 2021, the tool helps users deploy machine learning models… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Deploy ML Models on a Budget launched in 2021; newer entries below may have overtaken it.

1
Inferless▲749
Deploy any machine learning models in minutes
2025 · inferless.com · its alternatives →
2
Compile ML models into dependency-free source code for easy deployment▲37
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…
2023 · waveworks.dk · its alternatives →
3
Fullstack ML – From Notebooks to Deployment▲198
Contribute to xadrianzetx/fullstack.ai development by creating an account on GitHub.
2019 · github.com · its alternatives →
4
Plexe▲273
Build and deploy ML models in English
Oct 2025 · plexe.ai · its alternatives →
5
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 →
6
Pip install inference, open source computer vision deployment▲71
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com · its alternatives →
7
Modelbit▲125
Heroku for Data Science, from the founders of Periscope Data
2023 · modelbit.com · its alternatives →
8
Model Zoo▲74
Deploy your machine learning model in a single line of code.
2020 · its alternatives →
9MZ
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 →
10
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 →
11MM
MLEM – ML model deployment tool▲24
Hi, I'm one of the project creators. MLEM is a tool that helps you deploy your ML models. It’s a Python library + Command line tool. 1. MLEM can package an ML model into a Docker image or a Python package, and deploy it to, for example, Heroku. 2. MLEM saves all model metadata to a human-readable text file: Python environment, model methods, model input & output data schema and more. 3. MLEM helps you turn your Git repository into a Model Registry with features like ML model lifecycle management. Our philosophy is that MLOps tools should be built using the Unix approach - each tool solves a…
2022 · its alternatives →
12
Banana▲235
Serverless GPUs for Machine Learning inference
2022 · banana.dev · its alternatives →
13
MindsDB▲235
In-database machine learning
2022 · its alternatives →
14HP
High-performance ahead-of-time compiler for Machine Learning▲83
2018 · scss.tcd.ie · its alternatives →
15SM
SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances▲157
2021 · spotml.io · its alternatives →
16CI
Convect – Instant Serverless Deployment of ML Models▲89
2021 · convect.ml · its alternatives →
17
Gradio 5.0▲224
connect nodes to build AI pipelines, powered by Hugging Face
2024 · gradio.app · its alternatives →
18
Cortex▲81
Deploy, manage, and scale machine learning applications
2019 · 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 →
20DM
Deploy Machine Learning Models with Django▲6
Deploy Machine Learning models with Django
2019 · deploymachinelearning.com · its alternatives →
21
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 →
22
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 →
23
Layer AI▲124
Metadata store for production ML
2022 · layer.ai · its alternatives →
24
Bodywork▲58
Deploy machine learning into production 🚀
2021 · 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 →