Google Vertex AI Review, Pricing & Alternatives (2026)

Google launched Vertex AI in May 2021 to unify its machine learning services for training, deploying and monitoring models. With the rise of generative AI it became Google Cloud's main platform for enterprises using Gemini and other foundation models, and in 2026 Google began presenting it as the Gemini Enterprise Agent Platform, which its documentation describes as an evolution of Vertex AI. It serves data scientists, machine learning engineers and application developers at companies that run on Google Cloud, who need enterprise security, data residency, governance and integration with BigQuery and other Google data services rather than a consumer API key.

Model Garden offers more than 200 models: Google's Gemini, Imagen, Veo and Chirp models, partner models such as Anthropic's Claude family, and open models such as Gemma and Llama that can be deployed or tuned. Developers prototype prompts in a studio console, ground answers with Google Search or their own data, tune models and evaluate them with the Gen AI evaluation service. RAG Engine connects private data to models, and Vector Search provides a managed vector database. For classic machine learning, Vertex AI still offers custom training on GPUs and TPUs, AutoML, Pipelines, Feature Store, a model registry, monitoring and Colab Enterprise notebooks.

For agents, the Agent Development Kit is an open-source, model-agnostic framework in Python and Java for building multi-agent systems with tools, guardrails and streaming audio and video. Agent Studio is a low-code canvas for designing agents, and Agent Garden provides samples. Agents deploy to a managed runtime, previously called Agent Engine, with sessions, a memory bank, code execution sandboxes and evaluation. Teams can also bring agents built with LangChain, LangGraph, CrewAI or AG2, and connect them through MCP and the A2A protocol. Governance features include agent identities in IAM, Agent Gateway, Model Armor for runtime protection, tracing and a registry, and agents can be published to Gemini Enterprise for employees.

Vertex AI has no subscription. You pay for the models, tools, compute and storage you use, and new Google Cloud customers receive $300 in free credits. Model usage is billed per token, with standard pay-as-you-go, discounted Flex rates for latency-tolerant jobs, Priority rates for higher reliability and Provisioned Throughput subscriptions for guaranteed capacity. Prices vary widely by model; Google's pricing page lists some recent Gemini Flash models at introductory rates through the end of 2026. Agent infrastructure is billed by vCPU hours, memory and storage. Custom training, notebooks and Pipelines carry their own charges, with Pipelines starting at $0.03 per run.

Vertex AI's strengths are first-party access to Gemini, a broad Model Garden including Claude, deep integration with BigQuery and Google Cloud security, a strong open-source agent framework and full MLOps tooling. Its weaknesses are complexity, frequent product renaming, pricing that is hard to forecast and the need for a Google Cloud account and engineering skills. Amazon Bedrock and Azure AI Foundry are the equivalent platforms on AWS and Microsoft Azure. Google AI Studio offers simpler Gemini API access for prototypes. Hugging Face hosts open models, and Databricks suits teams centered on lakehouse data. Choose Vertex AI if your company runs on Google Cloud and needs governed, production AI.