Vertex AI
Data science and machine learning platforms
Generative AI infrastructure software
Machine learning software
MLOps platforms
Agentic AI software
AI agent builders software
Generative AI software
Large language model operationalization (LLMOps) software
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Low-code machine learning platforms software
- Features
- Ease of use
- Ease of management
- Quality of support
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What is Vertex AI
Vertex AI is a managed machine learning and generative AI platform on Google Cloud that supports building, training, tuning, deploying, and monitoring models. It targets data scientists, ML engineers, and platform teams that need a centralized environment for model development and production operations. The product combines managed training/inference, feature management, pipelines, model registry, and evaluation/monitoring with access to Google-hosted foundation models and tooling for prompt engineering and RAG workflows. It is designed to integrate with Google Cloud data, security, and governance services for enterprise deployment.
End-to-end managed ML lifecycle
Vertex AI provides managed components for training, hyperparameter tuning, model registry, deployment endpoints, and monitoring in a single platform. This reduces the amount of custom infrastructure required to operationalize models compared with assembling separate tools. It supports both custom models and hosted foundation models, enabling teams to standardize workflows across classical ML and generative AI. Centralized artifacts and lineage help teams move from experimentation to production with fewer handoffs.
Strong integration with Google Cloud
The platform integrates tightly with Google Cloud IAM, VPC networking, logging/monitoring, and key management, which can simplify enterprise security and operations. It connects with common Google Cloud data services (for example, data warehousing and object storage) to support feature creation, training data access, and batch/online inference. This integration can reduce data movement and operational friction when an organization already runs on Google Cloud. It also supports managed compute options for training and serving within the same cloud environment.
Generative AI and LLMOps tooling
Vertex AI includes tooling for working with foundation models, prompt management, evaluation, and deployment of generative AI applications. It supports common patterns such as retrieval-augmented generation (RAG) through integrations and building blocks for connecting models to enterprise data. Managed endpoints and monitoring help operationalize LLM-based services with consistent deployment controls. These capabilities make it suitable for teams that need both ML and generative AI operations under one governance model.
Google Cloud dependency and lock-in
Vertex AI is primarily designed for Google Cloud, and many capabilities assume use of Google Cloud services for identity, networking, and data. Organizations with multi-cloud or on-prem requirements may need additional tooling or architectural work to achieve portability. Migrating pipelines, endpoints, and model operational processes to another environment can be non-trivial. This can be a constraint for teams seeking vendor-neutral deployment patterns.
Cost management can be complex
Usage-based pricing across training compute, storage, endpoints, and generative AI consumption can make total cost harder to predict. Teams often need governance controls, quotas, and monitoring to avoid unexpected spend, especially for always-on endpoints and LLM inference. Cost optimization may require tuning instance types, autoscaling, batching, and caching strategies. Smaller teams may find budgeting and chargeback more demanding than with simpler, fixed-scope tools.
Learning curve for full platform
While the platform supports low-code and managed workflows, production use typically requires familiarity with Google Cloud concepts (IAM, networking, service accounts, logging) and MLOps practices. Teams may need to choose among multiple overlapping options (pipelines, notebooks, managed training, custom containers) and establish internal standards. Advanced use cases (custom serving, compliance controls, complex CI/CD) can require additional engineering effort. This can slow adoption for organizations without dedicated ML platform expertise.
Plan & Pricing
Pricing model: Pay-as-you-go (usage-based)
Free tier / free operations:
- Agent Engine runtime monthly free tier: first 180,000 vCPU-seconds (50 vCPU hours) and first 360,000 GiB-seconds (100 GiB-hours) free per month. (Agent Engine free tier applies to runtime only).
- Memory Bank: first 1,000 memories returned per month free.
- Some Vertex AI resource-management operations and Model Registry metadata storage have free/no-cost behaviors (see notes).
Example costs (selected, official Vertex AI items):
-
Agent Engine (runtime):
- vCPU: $0.0864 per vCPU-hour after the free tier (billing described per 3,600 seconds).
- RAM: $0.009 per GiB-hour after the free tier.
- (Code Execution): compute $0.0864 per vCPU-hour; memory $0.009 per GiB-hour.
- Sessions: $0.25 per 1,000 events stored.
- Memory Bank: $0.25 per 1,000 memories stored / month; $0.50 per 1,000 memories returned (first 1,000 returns free / month). .
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Generative AI (Gemini and related models — token/modality-based pricing examples):
- Gemini 3 / Gemini 3.1 Pro (Preview) — modality/token pricing examples (per 1M tokens or equivalent):
- Input (text/image/video/audio): $2 / 1M tokens (<=200K input tokens) up to $4 / 1M tokens (>200K) for live/interactive rates; text output (response/reasoning): $12 / 1M tokens (<=200K) up to $18 / 1M tokens (>200K). Flex/Batch examples: input $1 / 1M tokens; text output $6 / 1M tokens. Image output (example): $60 / 1M tokens (or $120 in some live tiers) — see official table for context and exact mode.
- Gemini 3 Flash (Preview) — lower-cost Flash tier example: input $0.5 / 1M tokens; text output $3 / 1M tokens (live rates shown on the official page).
- Other Gemini 2.x and Flash/Lite variants have distinct per-1M-character / per-image / per-second rates (see official table for many variants and long-context rates).
- Grounding / grounding-related features: Grounding with Google Search & Web Grounding for Enterprise includes 5,000 search queries per month free (across Gemini 3 models); excess search queries billed (e.g., $14 / 1,000 search queries for some Gemini 3 grounding; other grounding prices vary by model and grounding type). Grounding-with-your-data examples: $2.50 per 1,000 prompts/requests (varies by date and model)..
- Gemini 3 / Gemini 3.1 Pro (Preview) — modality/token pricing examples (per 1M tokens or equivalent):
-
GPUs / Machine types (selected examples shown on Vertex pricing page):
- A2 series examples: a2-highgpu-1g ~ $4.2244949 / hour; a2-highgpu-8g ~ $33.7959592 / hour (region-dependent).
- A4X / G2 / other accelerator-series example rates are listed per-hour on the official page (region-specific).
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Model Monitoring: $3.50 per GB for data analyzed (model monitoring charges apply on top of other charges).
-
Vertex AI Model Registry: no cost for storing model entries (costs incurred when deploying endpoints / predictions).
Discounts / enterprise options:
- Committed Use Discounts (CUDs), reservations, and enterprise contracts apply (Vertex AI may split charges across GCE SKUs and Vertex AI management SKUs to allow CUDs). Contact sales for custom/enterprise pricing quotes.
Notes / caveats:
- Prices are listed in USD on the official pages and are region-dependent; some SKUs vary by region.
- Some features were in Preview with special pricing (discounted or preview-free) — check the model-specific table for Preview vs. Flex/Batch vs. Live pricing.
- Google Cloud account-level Free Trial (Welcome credit) and the Google Cloud Free Tier are available and can be used with Vertex AI (see separate Google Cloud Free Trial details).
(Primary official sources used: Vertex AI pricing page and Vertex AI generative-AI pricing page on cloud.google.com; Google Cloud Free Trial documentation.)
Seller details
Google LLC
Mountain View, CA, USA
1998
Subsidiary
https://cloud.google.com/deep-learning-vm
https://x.com/googlecloud
https://www.linkedin.com/company/google/
