AI · alternatives · 2026

24 alternatives to Google CLOUD AUTOML

Train custom ML models with minimum effort and expertise

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Google CLOUD AUTOML launched in 2018; newer entries below may have overtaken it.

1
Obviously AI AutoML▲276
The fastest, most precise no-code AI in the world.
2020 · obviously.ai · its alternatives →
2
Pocket AutoML▲84
Experiment with a deep learning model right on your device
2021 · play.google.com · its alternatives →
3
MakeML▲274
Train Neural Networks without a line of code
2019 · makeml.app · its alternatives →
4
ZenML▲84
Create reproducible machine learning pipelines
2020 · github.com · its alternatives →
5
Web App with GUI for AutoML on Tabular Data▲41
AutoML Web App - build Machine Learning pipeline in automatic way with Graphical User Interface (GUI). You can run app locally! - mljar/automl-app
2023 · github.com · its alternatives →
6SM
SpotML – Managed ML Training on Cheap AWS/GCP Spot Instances▲157
2021 · spotml.io · its alternatives →
7
Pretrained AI▲144
Integrate pretrained machine learning models in minutes.
2021 · pretrained.ai · its alternatives →
8
OrchestraML▲82
From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app · its alternatives →
9
ScoopML▲81
An AI assistant for machine learning engineers
2020 · its alternatives →
10
I trained a deep learning model to build sites for me▲397
2019 · zecoda.com · its alternatives →
11
Layer AI▲124
Metadata store for production ML
2022 · layer.ai · its alternatives →
12
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 →
13
Provide a CSV and a target field, generate a model and code to run it▲172
Provide an input CSV and a target field to predict, generate a model + code to run it. - minimaxir/automl-gs
2019 · github.com · its alternatives →
14
Sambot▲69
AutoML assistant that does some predictions automatically
2022 · chromewebstore.google.com · its alternatives →
15
SapientML – Generative AutoML for Tabular Data▲82
Generative AutoML for Tabular Data. Contribute to sapientml/sapientml development by creating an account on GitHub.
2023 · github.com · its alternatives →
16
automl-docker▲68
Open-source, easily create ready-to-use ML models for NLP
2022 · github.com · its alternatives →
17
TensorPool▲192
The easiest way to use cloud GPUs
2025 · tensorpool.dev · its alternatives →
18
Semiring AI▲94
Create powerful ML models from 5 data samples
2023 · semiring.ai · its alternatives →
19
Algorithmia▲154
Automate DevOps for AI/ML with the AI Layer
2014 · datarobot.com · its alternatives →
20
TuneTrain.ai▲79
Do you know that Small Language Models (SLM) can outperform LLMs if trained (fine-tuned) on domain specific datasets, like your companies knowledge base? The issue: fine-tuning a SLM usually requires a dataset of 10k - 100k records, which is huge. I created a platform for applying "human in the loop" augmentation techniques on your small dataset, so that you can start with maybe 100 records and build-up quickly huge datasets and launch a training without prior knowledge. I implemented 2 techniques, based on LLM distillation, however: more to come. HN, what Do you think? Do you see value in…
2025 · tunetrain.ai · its alternatives →
21
DataMacaw▲67
Train and run your AI models easily at low cost in the cloud
2022 · aws.amazon.com · its alternatives →
22
Google Cloud TPUs▲114
Build and train machine learning models with Google
2017 · blog.google · its alternatives →
23
iko.ai▲76
Train, track, deploy, & monitor your machine learning models
2021 · its alternatives →
24
voltaML▲64
Accelerate your ML/DL models in production by upto 10X.
2022 · voltaml.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 →