The Most Used AI Tools from Google You Should Know About

Google has been at the forefront of artificial intelligence, offering a wide range of tools that cater to developers, enterprises, and researchers alike. Here’s a curated list of the most used AI tools from Google and what makes them stand out in 2025, along with their pricing and more helpful info.

1. Google Vertex AI

Description: Vertex AI is a unified ML platform that allows users to build, deploy, and scale ML models faster using Google Cloud infrastructure.

Use Cases: Predictive analytics, custom ML models, AutoML pipelines.

Integration: Easily integrates with BigQuery, Cloud Storage, and TensorFlow.

Ease of Use: Intermediate to advanced.

Pros:

• End-to-end ML lifecycle support
• Built-in MLOps tools
• Integrates well with Google Cloud ecosystem

Cons:

• Steeper learning curve
• Can get expensive at scale

Pricing: Pay-as-you-go. Charges vary by training hours, prediction requests, and model deployment time. Pricing details

2. Dialogflow (CX & ES)

Description: A natural language understanding tool used to design and integrate conversational interfaces into mobile apps, web apps, devices, and bots.

Use Cases: Chatbots, customer service automation, voice assistant development.

Integration: Supports Google Assistant, Slack, Messenger, and more.

Ease of Use: Beginner-friendly.

Pros:

• Easy to use UI
• Prebuilt agents
• Multi-language support

Cons:

• ES version limited in complexity
• CX version more costly

Pricing:

• Dialogflow ES: Free up to a limit
• Dialogflow CX: From $20 per 100 sessions/month Pricing details

3. Firebase ML

Description: Firebase ML is a mobile-centric machine learning SDK for Android and iOS that allows developers to use both custom and ready-made models.

Use Cases: Text recognition, face detection, language identification, translation.

Integration: Seamless with Firebase backend services.

Ease of Use: Beginner-friendly.

Pros:

• Optimized for mobile
• Offline support
• Auto model downloading

Cons:

• Limited compared to full ML suites

Pricing:

• Free tier available
• Charges apply for Cloud-based ML APIs Pricing details

4. TensorFlow

Description: An end-to-end open-source machine learning platform for everyone, from research to production.

Use Cases: Custom model development, ML research, training large neural networks.

Integration: Deeply integrates with Keras, TPUs, TensorBoard.

Ease of Use: Intermediate to advanced (steep for beginners).

Pros:

• Powerful and flexible
• Huge community and ecosystem
• Scalable for big models

Cons:

• Can be complex for beginners

Pricing: Free and open-source.

5. AutoML

Description: A part of Vertex AI, AutoML allows users to train high-quality custom models with minimal effort and machine learning expertise.

Use Cases: Image classification, sentiment analysis, document AI.

Integration: Integrates with Vertex AI and Google Cloud Storage.

Ease of Use: Beginner to intermediate.

Pros:

• No-code model training
• Great for non-technical users
• Easy deployment

Cons:

• Less control over training
• Can be costly for large datasets

Pricing: Based on compute and storage usage. Pricing details

6. Google Colab

Description: A free Jupyter notebook environment that runs entirely in the cloud, with optional access to GPUs.

Use Cases: Prototyping ML models, teaching, experimentation.

Integration: Built on top of Google Drive; integrates with GitHub.

Ease of Use: Beginner to advanced.

Pros:

• Free tier available
• No setup required
• GPU/TPU support

Cons:

• Session timeouts on free tier
• Limited GPU availability

Pricing:

• Free
• Colab Pro: $9.99/month (priority access to GPUs)
• Colab Pro+: $49.99/month (more RAM, faster GPUs) Pricing details

Quick Comparison Table