Vertex AI Pricing & Plans 2026

Name: Vertex AI
Brand: Vertex AI
Price: 300 USD
Availability: InStock

Complete pricing guide for Vertex AI. Compare all plans, analyze costs, and find the perfect tier for your needs.

Not sure if free is enough? See our Free vs Paid comparison →
Still deciding? Read our full verdict on whether Vertex AI is worth it →

🆓Free Tier Available

💎3 Paid Plans

⚡No Setup Fees

Choose Your Plan

Free Trial / Free Tier

$300 credit for 90 days + ongoing limited free tier

✓$300 in Google Cloud credits for new customers
✓Limited free monthly usage of AutoML, Pipelines, and select Vertex AI components
✓Google AI Studio free tier for Gemini prototyping (separate product)
✓Full access to Model Garden for evaluation

Start Free →

Pay-as-you-go (Generative AI)

Per 1K tokens / per character

✓Gemini 1.5 Flash, 1.5 Pro, and 2.0 billed per input/output token
✓Imagen, Veo, and Chirp billed per image/second/request
✓Third-party models (Claude, Llama, Mistral) billed per token at partner rates
✓Grounding with Google Search billed per grounded request
✓Embeddings billed per 1K input characters

Start Free Trial →

Pay-as-you-go (Custom ML)

Per node-hour / per prediction

✓Custom Training billed per machine-type node-hour (CPU, GPU, TPU)
✓Online Prediction billed per node-hour while endpoint is deployed
✓Batch Prediction billed per node-hour of compute
✓AutoML billed per node-hour of training and per 1K predictions
✓Feature Store, Pipelines, and Model Monitoring billed per usage

Start Free Trial →

Provisioned Throughput & Enterprise

Custom / committed-use contracts

✓Reserved Gemini capacity with predictable latency and throughput
✓Committed Use Discounts (CUDs) for 1 or 3-year commitments
✓Volume and enterprise agreement pricing via Google Cloud sales
✓Premium support tiers with defined SLAs
✓Private Offer pricing through Google Cloud Marketplace

Start Free Trial →

Pricing sourced from Vertex AI · Last verified March 2026

Feature Comparison

Features	Free Trial / Free Tier	Pay-as-you-go (Generative AI)	Pay-as-you-go (Custom ML)	Provisioned Throughput & Enterprise
$300 in Google Cloud credits for new customers	✓	✓	✓	✓
Limited free monthly usage of AutoML, Pipelines, and select Vertex AI components	✓	✓	✓	✓
Google AI Studio free tier for Gemini prototyping (separate product)	✓	✓	✓	✓
Full access to Model Garden for evaluation	✓	✓	✓	✓
Gemini 1.5 Flash, 1.5 Pro, and 2.0 billed per input/output token	—	✓	✓	✓
Imagen, Veo, and Chirp billed per image/second/request	—	✓	✓	✓
Third-party models (Claude, Llama, Mistral) billed per token at partner rates	—	✓	✓	✓
Grounding with Google Search billed per grounded request	—	✓	✓	✓
Embeddings billed per 1K input characters	—	✓	✓	✓
Custom Training billed per machine-type node-hour (CPU, GPU, TPU)	—	—	✓	✓
Online Prediction billed per node-hour while endpoint is deployed	—	—	✓	✓
Batch Prediction billed per node-hour of compute	—	—	✓	✓
AutoML billed per node-hour of training and per 1K predictions	—	—	✓	✓
Feature Store, Pipelines, and Model Monitoring billed per usage	—	—	✓	✓
Reserved Gemini capacity with predictable latency and throughput	—	—	—	✓
Committed Use Discounts (CUDs) for 1 or 3-year commitments	—	—	—	✓
Volume and enterprise agreement pricing via Google Cloud sales	—	—	—	✓
Premium support tiers with defined SLAs	—	—	—	✓
Private Offer pricing through Google Cloud Marketplace	—	—	—	✓

Is Vertex AI Worth It?

✅ Why Choose Vertex AI

• Native access to Google's Gemini foundation models and 150+ models in Model Garden, providing cutting-edge generative AI capabilities unavailable on competing platforms
• Deep integration with the Google Cloud ecosystem including BigQuery ML, Dataflow, Cloud Storage, and Looker — enabling seamless data-to-model pipelines without data movement
• Access to Google's custom TPU v5e accelerators for high-performance, cost-efficient training of large models, a hardware advantage no other cloud provider offers
• Comprehensive MLOps tooling with Vertex AI Pipelines, Feature Store, Model Registry, model monitoring, and Experiments — supporting the full ML lifecycle from prototype to production
• AutoML enables non-ML-experts to build competitive models across tabular, image, text, and video data with minimal code, lowering the barrier to entry for AI adoption
• Strong responsible AI tooling including explainability, bias detection, model evaluation, and data drift monitoring built directly into the platform

⚠️ Consider This

• Significant vendor lock-in to Google Cloud: models trained on Vertex AI, pipelines using Vertex Pipelines, and features stored in Feature Store are not easily portable to AWS or Azure
• Complex, multi-dimensional pricing across training, prediction, storage, and API calls makes cost estimation and budgeting challenging — unexpected bills are a common user complaint
• Steep learning curve for the full platform: while individual services are well-documented, understanding how AutoML, custom training, pipelines, endpoints, and monitoring fit together requires substantial investment
• Smaller community and third-party ecosystem compared to AWS SageMaker — fewer tutorials, Stack Overflow answers, and third-party integrations available
• Some features lag behind competitors in maturity: for example, real-time feature serving and experiment tracking have historically been less polished than dedicated tools like Tecton or MLflow

What Users Say About Vertex AI

👍 What Users Love

✓Native access to Google's Gemini foundation models and 150+ models in Model Garden, providing cutting-edge generative AI capabilities unavailable on competing platforms
✓Deep integration with the Google Cloud ecosystem including BigQuery ML, Dataflow, Cloud Storage, and Looker — enabling seamless data-to-model pipelines without data movement
✓Access to Google's custom TPU v5e accelerators for high-performance, cost-efficient training of large models, a hardware advantage no other cloud provider offers
✓Comprehensive MLOps tooling with Vertex AI Pipelines, Feature Store, Model Registry, model monitoring, and Experiments — supporting the full ML lifecycle from prototype to production
✓AutoML enables non-ML-experts to build competitive models across tabular, image, text, and video data with minimal code, lowering the barrier to entry for AI adoption
✓Strong responsible AI tooling including explainability, bias detection, model evaluation, and data drift monitoring built directly into the platform
✓Vertex AI Studio provides an intuitive UI for prompt engineering, model tuning, and grounding — accelerating generative AI application development

👎 Common Concerns

⚠Significant vendor lock-in to Google Cloud: models trained on Vertex AI, pipelines using Vertex Pipelines, and features stored in Feature Store are not easily portable to AWS or Azure
⚠Complex, multi-dimensional pricing across training, prediction, storage, and API calls makes cost estimation and budgeting challenging — unexpected bills are a common user complaint
⚠Steep learning curve for the full platform: while individual services are well-documented, understanding how AutoML, custom training, pipelines, endpoints, and monitoring fit together requires substantial investment
⚠Smaller community and third-party ecosystem compared to AWS SageMaker — fewer tutorials, Stack Overflow answers, and third-party integrations available
⚠Some features lag behind competitors in maturity: for example, real-time feature serving and experiment tracking have historically been less polished than dedicated tools like Tecton or MLflow
⚠Documentation can be fragmented across Vertex AI, AI Platform (legacy), and individual service pages, making it difficult to find authoritative guidance for specific workflows

Pricing FAQ

How does Vertex AI pricing work?

Vertex AI uses a pay-as-you-go model with separate pricing per service. Foundation models like Gemini are billed per 1K input/output tokens (or per character for older PaLM models), while custom training is billed per machine-type node-hour. Predictions are billed per node-hour for online endpoints or per 1K records for batch jobs. AutoML, Feature Store, Pipelines, and Model Monitoring each have their own rate cards. Google offers a $300 free trial credit for new Google Cloud customers and a limited free tier for some Vertex AI components.

Can I use non-Google models like Claude or Llama on Vertex AI?

Yes. Vertex AI Model Garden offers first-party access to Anthropic's Claude family, Meta's Llama, Mistral, AI21, and many open-source models. These can be invoked through the same Vertex AI SDK and API as Gemini, with unified billing, IAM, logging, and VPC controls — making it a convenient single pane for multi-model deployments.

What is the difference between Vertex AI and Google AI Studio?

Google AI Studio (aistudio.google.com) is a free, consumer-friendly playground aimed at developers prototyping with Gemini via a simple API key. Vertex AI is the enterprise platform: it adds IAM-based authentication, VPC Service Controls, data residency, audit logging, provisioned throughput, fine-tuning, MLOps tooling, and SLAs. Production workloads should use Vertex AI; quick prototyping can start in AI Studio.

Does Vertex AI support fine-tuning and custom models?

Yes. Vertex AI supports supervised fine-tuning for Gemini and select open models, distillation, RLHF, and adapter-based tuning methods. You can also bring custom containers and train from scratch on GPUs or TPUs using Vertex AI Training, then register and deploy via the Model Registry and Endpoints.

Is Vertex AI suitable for regulated industries like healthcare or finance?

Yes. Vertex AI is covered by Google Cloud's compliance portfolio including HIPAA BAA, FedRAMP High, PCI DSS, ISO 27001/27017/27018, SOC 1/2/3, and regional data residency options. Customer data is not used to train Google's foundation models by default, and VPC Service Controls plus CMEK provide network isolation and customer-managed encryption keys.

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More about Vertex AI

Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Vertex AI Pricing & Plans 2026

Complete pricing guide for Vertex AI. Compare all plans, analyze costs, and find the perfect tier for your needs.

🆓Free Tier Available

💎3 Paid Plans

⚡No Setup Fees

Choose Your Plan

Free Trial / Free Tier

$300 credit for 90 days + ongoing limited free tier

✓$300 in Google Cloud credits for new customers
✓Limited free monthly usage of AutoML, Pipelines, and select Vertex AI components
✓Google AI Studio free tier for Gemini prototyping (separate product)
✓Full access to Model Garden for evaluation

Start Free →

Pay-as-you-go (Generative AI)

Per 1K tokens / per character

✓Gemini 1.5 Flash, 1.5 Pro, and 2.0 billed per input/output token
✓Imagen, Veo, and Chirp billed per image/second/request
✓Third-party models (Claude, Llama, Mistral) billed per token at partner rates
✓Grounding with Google Search billed per grounded request
✓Embeddings billed per 1K input characters

Start Free Trial →

Pay-as-you-go (Custom ML)

Per node-hour / per prediction

✓Custom Training billed per machine-type node-hour (CPU, GPU, TPU)
✓Online Prediction billed per node-hour while endpoint is deployed
✓Batch Prediction billed per node-hour of compute
✓AutoML billed per node-hour of training and per 1K predictions
✓Feature Store, Pipelines, and Model Monitoring billed per usage

Start Free Trial →

Provisioned Throughput & Enterprise

Custom / committed-use contracts

✓Reserved Gemini capacity with predictable latency and throughput
✓Committed Use Discounts (CUDs) for 1 or 3-year commitments
✓Volume and enterprise agreement pricing via Google Cloud sales
✓Premium support tiers with defined SLAs
✓Private Offer pricing through Google Cloud Marketplace

Start Free Trial →

Pricing sourced from Vertex AI · Last verified March 2026

Feature Comparison

Features	Free Trial / Free Tier	Pay-as-you-go (Generative AI)	Pay-as-you-go (Custom ML)	Provisioned Throughput & Enterprise
$300 in Google Cloud credits for new customers	✓	✓	✓	✓
Limited free monthly usage of AutoML, Pipelines, and select Vertex AI components	✓	✓	✓	✓
Google AI Studio free tier for Gemini prototyping (separate product)	✓	✓	✓	✓
Full access to Model Garden for evaluation	✓	✓	✓	✓
Gemini 1.5 Flash, 1.5 Pro, and 2.0 billed per input/output token	—	✓	✓	✓
Imagen, Veo, and Chirp billed per image/second/request	—	✓	✓	✓
Third-party models (Claude, Llama, Mistral) billed per token at partner rates	—	✓	✓	✓
Grounding with Google Search billed per grounded request	—	✓	✓	✓
Embeddings billed per 1K input characters	—	✓	✓	✓
Custom Training billed per machine-type node-hour (CPU, GPU, TPU)	—	—	✓	✓
Online Prediction billed per node-hour while endpoint is deployed	—	—	✓	✓
Batch Prediction billed per node-hour of compute	—	—	✓	✓
AutoML billed per node-hour of training and per 1K predictions	—	—	✓	✓
Feature Store, Pipelines, and Model Monitoring billed per usage	—	—	✓	✓
Reserved Gemini capacity with predictable latency and throughput	—	—	—	✓
Committed Use Discounts (CUDs) for 1 or 3-year commitments	—	—	—	✓
Volume and enterprise agreement pricing via Google Cloud sales	—	—	—	✓
Premium support tiers with defined SLAs	—	—	—	✓
Private Offer pricing through Google Cloud Marketplace	—	—	—	✓

Is Vertex AI Worth It?

✅ Why Choose Vertex AI

• Native access to Google's Gemini foundation models and 150+ models in Model Garden, providing cutting-edge generative AI capabilities unavailable on competing platforms
• Deep integration with the Google Cloud ecosystem including BigQuery ML, Dataflow, Cloud Storage, and Looker — enabling seamless data-to-model pipelines without data movement
• Access to Google's custom TPU v5e accelerators for high-performance, cost-efficient training of large models, a hardware advantage no other cloud provider offers
• Comprehensive MLOps tooling with Vertex AI Pipelines, Feature Store, Model Registry, model monitoring, and Experiments — supporting the full ML lifecycle from prototype to production
• AutoML enables non-ML-experts to build competitive models across tabular, image, text, and video data with minimal code, lowering the barrier to entry for AI adoption
• Strong responsible AI tooling including explainability, bias detection, model evaluation, and data drift monitoring built directly into the platform

⚠️ Consider This

• Significant vendor lock-in to Google Cloud: models trained on Vertex AI, pipelines using Vertex Pipelines, and features stored in Feature Store are not easily portable to AWS or Azure
• Complex, multi-dimensional pricing across training, prediction, storage, and API calls makes cost estimation and budgeting challenging — unexpected bills are a common user complaint
• Steep learning curve for the full platform: while individual services are well-documented, understanding how AutoML, custom training, pipelines, endpoints, and monitoring fit together requires substantial investment
• Smaller community and third-party ecosystem compared to AWS SageMaker — fewer tutorials, Stack Overflow answers, and third-party integrations available
• Some features lag behind competitors in maturity: for example, real-time feature serving and experiment tracking have historically been less polished than dedicated tools like Tecton or MLflow

What Users Say About Vertex AI

👍 What Users Love

✓Native access to Google's Gemini foundation models and 150+ models in Model Garden, providing cutting-edge generative AI capabilities unavailable on competing platforms
✓Deep integration with the Google Cloud ecosystem including BigQuery ML, Dataflow, Cloud Storage, and Looker — enabling seamless data-to-model pipelines without data movement
✓Access to Google's custom TPU v5e accelerators for high-performance, cost-efficient training of large models, a hardware advantage no other cloud provider offers
✓Comprehensive MLOps tooling with Vertex AI Pipelines, Feature Store, Model Registry, model monitoring, and Experiments — supporting the full ML lifecycle from prototype to production
✓AutoML enables non-ML-experts to build competitive models across tabular, image, text, and video data with minimal code, lowering the barrier to entry for AI adoption
✓Strong responsible AI tooling including explainability, bias detection, model evaluation, and data drift monitoring built directly into the platform
✓Vertex AI Studio provides an intuitive UI for prompt engineering, model tuning, and grounding — accelerating generative AI application development

👎 Common Concerns

⚠Significant vendor lock-in to Google Cloud: models trained on Vertex AI, pipelines using Vertex Pipelines, and features stored in Feature Store are not easily portable to AWS or Azure
⚠Complex, multi-dimensional pricing across training, prediction, storage, and API calls makes cost estimation and budgeting challenging — unexpected bills are a common user complaint
⚠Steep learning curve for the full platform: while individual services are well-documented, understanding how AutoML, custom training, pipelines, endpoints, and monitoring fit together requires substantial investment
⚠Smaller community and third-party ecosystem compared to AWS SageMaker — fewer tutorials, Stack Overflow answers, and third-party integrations available
⚠Some features lag behind competitors in maturity: for example, real-time feature serving and experiment tracking have historically been less polished than dedicated tools like Tecton or MLflow
⚠Documentation can be fragmented across Vertex AI, AI Platform (legacy), and individual service pages, making it difficult to find authoritative guidance for specific workflows