# Free Machine Learning and AI Platforms: The Complete Developer Guide

> Explore free machine learning and AI platforms for developers. Train models and run AI workloads with Google Colab, Paperspace, Deepnote, and more. Find top tools in ripienaar/free-for-dev.

- Repository: [R.I.Pienaar/free-for-dev](https://github.com/ripienaar/free-for-dev)
- Tags: getting-started
- Published: 2026-02-25

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**Developers can train models and run AI workloads at no cost using Google Colab, Paperspace, and Deepnote for compute, Weights & Biases and Comet ML for experiment tracking, and OpenRouter or Azure Cognitive Services for LLM inference, all documented in the ripienaar/free-for-dev repository.**

The ripienaar/free-for-dev repository curates a comprehensive catalog of SaaS services offering permanent free tiers for developers. Within the **"APIs, Data, and ML"** and **"Generative AI"** sections of the [`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md), you’ll find numerous **free machine learning and AI platforms** that provide GPU compute, experiment tracking, and model hosting without requiring a credit card.

## Free Machine Learning Platforms for Model Training

### Google Colab: Free GPU-Accelerated Jupyter Notebooks

**Google Colab** offers a free Jupyter-notebook environment with access to a **Tesla K80 GPU** and **12 GB of RAM**, making it ideal for rapid prototyping and small-scale model training. As documented in the [`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md) under the **"APIs, Data, and ML"** section, Colab requires no local setup and runs entirely in the cloud.

### Paperspace: Free Compute for AI Development

**Paperspace** provides free compute resources for AI projects, including public projects, **5 GB of storage**, and access to basic CPU and GPU instances. This platform is suitable for training small models or running inference notebooks without local hardware constraints.

### Deepnote: Collaborative Cloud-Based Notebooks

**Deepnote** delivers cloud-based Python notebooks with real-time collaboration features. The free tier includes **unlimited personal projects**, **5 GB of RAM**, and **2 vCPU**, providing a robust environment for data science and machine learning experimentation.

## Free AI Platforms for Experiment Tracking and MLOps

### Weights & Biases: Comprehensive MLOps Suite

**Weights & Biases (W&B)** provides a full MLOps suite including experiment tracking, dataset versioning, and model registry. According to the repository's [`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md), the free tier supports personal projects with **100 GB of storage**, making it accessible for individual developers and small teams.

### Comet ML: Experiment Tracking for Individuals

**Comet ML** specializes in experiment tracking with metrics, graphs, and model versioning. The platform offers unlimited experiments for **individuals and academic users**, allowing comprehensive logging of machine learning workflows at no cost.

### Arize AI: Model Monitoring and Observability

**Arize AI** focuses on model monitoring and observability for production ML systems. The free tier supports **up to two models** and includes drift detection and performance alerts, essential for maintaining model reliability.

## Free Generative AI and LLM Platforms

### OpenRouter: Unified Access to Open-Source LLMs

**OpenRouter** provides access to numerous open-source large language models including DeepSeek, Llama, and Moonshot. As listed in the **"Generative AI"** section of the repository, the free tier offers a quota of requests per month, enabling developers to prototype LLM-driven features without incurring costs.

### Azure Cognitive Services: AI APIs for Vision and Language

**Azure Cognitive Services** offers a suite of AI APIs covering Vision, Speech, Language, and Search capabilities. The free tier includes limited transactions per month for each service, allowing integration of pre-trained AI models into applications.

### Arize AX: Generative AI Engineering Platform

**Arize AX** is an end-to-end AI engineering platform specifically for generative AI, offering monitoring, tracing, and evaluation. The free product includes **25,000 spans and 1 GB of ingestion per month**, supporting observability for LLM applications.

## Repository Structure and Key Files

The **ripienaar/free-for-dev** repository organizes these services in specific files that define the knowledge base and contribution workflow:

- **[`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md)** — Contains the central catalog with **"APIs, Data, and ML"** and **"Generative AI"** sections (approximately lines 250–350) where all free ML/AI platforms are documented.
- **[`AGENTS.md`](https://github.com/ripienaar/free-for-dev/blob/main/AGENTS.md)** and **[`CLAUDE.md`](https://github.com/ripienaar/free-for-dev/blob/main/CLAUDE.md)** — Define the repository’s policy on AI-generated contributions, providing context for any future pull requests.
- **[`.github/PULL_REQUEST_TEMPLATE.md`](https://github.com/ripienaar/free-for-dev/blob/main/.github/PULL_REQUEST_TEMPLATE.md)** — Provides the PR checklist that includes an AI tick box, useful when contributing new free-tier entries.

## Practical Code Examples

### Training a Model in Google Colab

The following snippet demonstrates loading data and training a simple model in **Google Colab**, utilizing the free GPU tier:

```python

# In a Colab notebook cell

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load Iris dataset (built-in)

df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')
X = df.drop('species', axis=1)
y = df['species']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

pred = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, pred))

```

*This code runs entirely in the free Colab VM with GPU/CPU resources and requires no local setup.*

### Logging Experiments with Weights & Biases

This example shows how to track machine learning experiments using the **Weights & Biases** free tier:

```python

# pip install wandb   # run once in the notebook or local env

import wandb
import numpy as np
import sklearn.datasets as datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

wandb.init(project="free-ml-demo", reinit=True)   # creates a free run

X, y = datasets.load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

clf = RandomForestClassifier(n_estimators=100, max_depth=5)
clf.fit(X_train, y_train)

pred = clf.predict(X_test)
acc = accuracy_score(y_test, pred)

wandb.log({"accuracy": acc, "n_estimators": 100, "max_depth": 5})
print("Logged accuracy:", acc)

```

*The run appears in your free W&B dashboard, showing metrics, model artifacts, and system information.*

### Calling LLMs via OpenRouter

This snippet demonstrates accessing open-source large language models through **OpenRouter**'s free tier:

```python
import requests, json

url = "https://openrouter.ai/api/v1/chat/completions"
payload = {
    "model": "meta-llama/Meta-Llama-3-8B-Instruct",
    "messages": [{"role": "user", "content": "Explain the bias-variance trade-off in 2 sentences."}],
}
headers = {
    "Authorization": "Bearer YOUR_OPENROUTER_API_KEY",   # free tier key from OpenRouter dashboard

    "Content-Type": "application/json",
}
resp = requests.post(url, headers=headers, data=json.dumps(payload))
print(resp.json()["choices"][0]["message"]["content"])

```

*OpenRouter’s free tier provides a limited number of requests per month, perfect for prototyping LLM-driven features.*

## Summary

- **Google Colab**, **Paperspace**, and **Deepnote** provide free GPU and CPU compute for training models without local hardware.
- **Weights & Biases**, **Comet ML**, and **Arize AI** offer comprehensive experiment tracking and model monitoring under generous free tiers.
- **OpenRouter**, **Azure Cognitive Services**, and **Arize AX** enable access to large language models and AI APIs with limited but functional free quotas.
- All services are documented in the **[`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md)** of the **ripienaar/free-for-dev** repository within the **"APIs, Data, and ML"** and **"Generative AI"** sections.

## Frequently Asked Questions

### What are the best free machine learning and AI platforms for beginners?

**Google Colab** is the most accessible entry point for beginners, offering a free Jupyter environment with GPU access and no setup required. For those needing experiment tracking, **Comet ML** provides unlimited experiments for individual users, making it easy to log metrics and compare model versions without cost.

### How much GPU compute is available on free machine learning and AI platforms?

**Google Colab** provides free access to Tesla K80 GPUs with 12 GB of RAM, while **Paperspace** offers basic GPU instances with 5 GB of storage for public projects. **Deepnote** allocates 5 GB of RAM and 2 vCPU for cloud-based notebook execution, sufficient for small to medium-sized datasets and model training.

### Can I use free machine learning and AI platforms for commercial projects?

Most free tiers, including **Weights & Biases** (100 GB storage) and **Arize AI** (up to two models), permit commercial use within their free tier limits, though you should verify specific license terms for each platform. **OpenRouter** and **Azure Cognitive Services** also allow commercial prototyping within their free request quotas, making them suitable for MVPs and early-stage products.

### Where are these free machine learning and AI platforms documented in the repository?

All platforms are cataloged in the **[`README.md`](https://github.com/ripienaar/free-for-dev/blob/main/README.md)** file of the **ripienaar/free-for-dev** repository, specifically within the **"APIs, Data, and ML"** section (approximately lines 250–350) and the **"Generative AI"** section. The repository also includes **[`AGENTS.md`](https://github.com/ripienaar/free-for-dev/blob/main/AGENTS.md)** and **[`CLAUDE.md`](https://github.com/ripienaar/free-for-dev/blob/main/CLAUDE.md)** for contribution guidelines, and **[`.github/PULL_REQUEST_TEMPLATE.md`](https://github.com/ripienaar/free-for-dev/blob/main/.github/PULL_REQUEST_TEMPLATE.md)** for submission standards.