Vertex AI Pricing Review + Features and an Alternative | 2026

Vertex AI pricing starts at $21.25/hour per custom training node, but your total cost depends on model usage, pipelines, and your region. If Vertex AI pricing seems unclear, this guide explains what to expect. 

We’ll walk you through service types and what’s included in the free tier, and the following points about Vertex AI: 

• Vertex AI pricing and billing structure
  
• What’s included in the free tier
• What makes Vertex AI’s pricing confusing
  
• A comparison with Lindy (for productivity use cases)

Let’s walk through how Vertex AI pricing works. 

Vertex pricing

Vertex AI free tier and discounts

The Vertex AI free tier lets new users try services like online prediction (5 GB per month) and limited custom training hours at no cost. New accounts also get $300 in Google Cloud credits valid for 90 days, which they con use on Vertex AI and other Google Cloud services. Free-tier benefits vary by service and may change at any time. 

Here are the other benefits on the Vertex AI free tier:

• Vertex AI Express Mode: You can use core tools like Vertex AI Studio and Agent Builder with limited quotas (up to 10 agent engines, 90 days of usage) without enabling billing.

• Free access to services in preview: Includes Vertex AI Pipelines, TensorBoard, and Generative AI APIs like Gemini, all free while in preview with 100% discounted pricing.

• 10,000 free queries/month on Vertex AI Search: Vertex AI Search offers a free tier with limited services, but 10,000 queries/month. However, it doesn’t include add-ons like Core Generative Answers. Google bills add-ons separately or applies different usage limits. Check the official pricing documentation for the latest usage limits.

• Bonus credits through special programs: Google offers extra credits for universities, research teams, and early-stage startups via the Google for Startups Cloud Program. These credits reduce early-stage spending, but they fail to prevent overages once your workload exceeds the quota.

New accounts get $300 in Google Cloud credits, which expire after 90 days if unused. Since Vertex AI has no monthly spending caps, track your usage to avoid charges once you run out of credits. Also, many “preview” services included in the free tier may later transition to paid pricing without much notice.

Why does Vertex AI pricing confuse users?

Google Vertex AI pricing confuses users due to its token-based billing, variable rates, and multiple model usage types. Vertex uses the following AI pricing tools, which can confuse many users:

Token-based pricing model:

Google Vertex AI uses a token-based pricing model for generative tasks like chat, summarization, and code completion. However, the model determines the charging rate per token. For example, Gemini 1.5 Pro employs a distinct rate structure, dividing costs between input and output tokens.

Google allows free access to models like PaLM 2 and Bison while they remain in preview. Others, like Gemini’s GA versions, may switch to full pricing at any time. Users must also consider token window limits and how these affect token count and latency.

Confusion around “free usage” vs total monthly spend

Vertex AI prominently markets its free tier and preview access, but the “free usage” it actually covers varies across services. Google offers 100% discounted pricing for generative AI during the preview. Others, however, provide a limited quota of free monthly usage, such as 10,000 queries on Vertex AI Search. 

Google excludes features like Core Generative Answers and data augmentation from these limits. Free experimentation often turns into real charges once teams exceed quotas or lose preview access.

Many users mistake “free to start” for completely free deployment. Since Google doesn’t offer budget caps, a workload spike can lead to unexpected charges. Google provides no budget safeguard, so sudden usage often increases the monthly spend. Teams discover overages only after receiving unexpected billing charges.

Per-endpoint pricing vs shared infrastructure

One of the most overlooked aspects of Vertex AI is that it charges differently depending on the deployment method. If you deploy a model to a dedicated endpoint, you’re billed by the hour, even if the endpoint is idle. 

Batch predictions or shared infrastructure save money but often reduce speed, limit version control, and require extra monitoring. The model you use, the deployment method you choose, and the frequency at which you scale instances all affect the total cost. 

However, Google’s pricing tools don’t clarify how the deployment method impacts billing during setup.

To make matters even more confusing, Vertex AI doesn’t provide a unified dashboard that shows how switching between deployment modes affects billing. As a result, product owners and ML teams often discover they’ve been paying for unused uptime, or that their shared deployment can’t meet SLAs.

Hard to estimate costs unless you already know usage patterns

Vertex AI requires teams to predict usage in terms of token volume, compute hours, QPS (queries per second), and storage. Each of these usage types may vary significantly by workload. 

You must estimate prompts, training time, and usage to forecast costs, but usage patterns are hard to predict. Vertex AI lacks guided estimators for common scenarios, so teams often rely on test runs or third-party tools to track spending. 

Many engineers overlook hidden costs like egress fees and idle compute. Without usage benchmarks, scaling often leads to unexpected bills.

Lindy vs. Vertex AI: Simpler pricing, faster results

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Key takeaways: Who each platform is for

Product managers, engineers, and founders choose Vertex AI when they need flexible model deployment and native integration with Google Cloud. However, non-technical professionals who handle sales, marketing, and administrative tasks choose Lindy. The platform lets them automate real business tasks without writing code. 

Let’s examine which use cases call for Vertex AI and which suit Lindy.

Use Vertex AI if you:

• Are a tech-savvy team: You work with ML engineers or data scientists who can handle APIs, quota management, and resource provisioning. Vertex AI gives you full control of your modelling. However, you need familiarity with model tuning, prompt engineering, and pipeline orchestration across tools such as BigQuery and AutoML.

• Need to fine-tune your models: Vertex gives you complete control over model behavior, output accuracy, and compliance. It supports supervised fine-tuning, adapter-based techniques (e.g., LoRA), and RLHF pipelines.

• Want cloud-scale infrastructure: You prefer to manage models across multiple endpoints and avoid being tied to a single vendor’s workflow. Vertex AI supports hybrid workloads, open model deployment, and pricing transparency at the infrastructure level. 

Use Lindy if you:

• Don’t have development experience: Lindy lets you launch AI agents without touching a line of code. Its drag-and-drop interface, prebuilt workflows, and natural language prompts make setup simple, even for non-technical users

• Want AI to automate real work like meeting notes, email follow-ups: Instead of prototyping interactions or research summaries, Lindy automates actual workflows. Lindy connects to tools like Gmail, Slack, Zoom, and Google Calendar. This lets you summarize meetings and send follow-ups. 

• Need to deploy AI workflows quickly: You want to build an AI agent today, and Lindy provides the platform to create it. You can adjust workflows on the fly and get immediate feedback on how agents perform in production.

Vertex AI suits engineers who need full control over models and infrastructure. Lindy is for non-technical users who want fast, no-code automation.

How Lindy simplifies AI for teams

Lindy helps teams deploy automation without developers or complex workflows. Instead of prompt engineering, teams can use Lindy’s prebuilt templates to spin up task-specific AI agents for recording meetings or lead qualification. 

Lindy’s free tier includes 400 monthly tasks, enough to test automations like email follow-ups, meeting summaries, and Slack messages. Each task reflects a real agent action, helping individuals and small teams validate workflows without upfront cost. 

Paid plans offer up to 5,000 monthly task automations. They support continuous ops across sales, support, and internal systems with no complex setup. Lindy combines Zapier-style automation with ChatGPT-like reasoning, offering a cost-efficient alternative for teams comparing generative AI pricing models. Agents analyze context, skip irrelevant actions, and get things done end-to-end.

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Lindy: A practical Vertex alternative 

If you’re exploring Vertex AI pricing and want a faster alternative for business automation, try Lindy. Unlike complex platforms that require coding knowledge, Lindy delivers scalable, context-aware AI that executes real-world business tasks without hidden costs or manual upkeep.

Here’s how Lindy can help your workflows:

• Out-of-the-box email support: Route messages from email, live chat, or a dedicated inbox. Lindy auto-responds with context-aware answers, reducing inbound support volume without extra infrastructure.

• Always-on coverage: Deployed like a managed model endpoint, Lindy offers 24/7 uptime with zero cold starts, ideal for global teams.

• Integrates across your stack: With support for tools like Stripe, Gmail, HubSpot, and over 2,500 other third-party platforms, Lindy can sync data and execute tasks across the tools you already use.

Try Lindy today for free.

Frequently asked questions

What is Vertex AI? 

Vertex AI is Google Cloud’s fully managed machine learning platform. It lets teams build, train, deploy, and scale models. Google designed Vertex for engineers and data scientists, as it integrates with tools like BigQuery, AutoML, and pipelines for end-to-end ML workflows.

How much does Vertex AI cost per month?

Your workload, model type, and deployment method directly affect your monthly costs. There’s no built-in monthly cap, so actual spend depends on traffic, job duration, and service configuration, making forecasting tricky without clear usage patterns. 

You pay for what you use with Vertex AI, not a flat rate. Thus, you pay for compute (e.g., $20/hour for training), model predictions, storage, and tokens used in generative AI tasks. 

Is Vertex AI free to use?

Vertex AI includes a free tier with limited training hours, 5 GB of online prediction, and access to preview services like Gemini and Pipelines. New Google Cloud users get a one-time allowance of $300 in credits valid for 90 days, usable across Vertex AI and other services.