Amazon Bedrock vs Google Vertex AI: pricing, features and which to choose

Amazon Bedrock is part of Amazon Web Services and became generally available in 2023. It is built for developers and enterprises that want to use large language models inside their AWS accounts, with the same identity, networking, logging and compliance controls they use for other AWS services. Data sent to Bedrock stays in the customer's AWS environment and, according to AWS, is not used to train the underlying models. Customers range from startups to banks, healthcare companies and governments, and Bedrock is a common choice for teams that already run their applications on AWS and want one bill, one security model and private network access to models.

The model catalog is Bedrock's main draw. AWS lists models from AI21 Labs, Amazon, Anthropic, Cohere, DeepSeek, Google's Gemma, Luma AI, Meta, MiniMax, Mistral AI, Moonshot AI, NVIDIA, OpenAI, Qwen, Stability AI, TwelveLabs, Writer, xAI and Z.AI. That includes Anthropic's Claude family, Amazon's own Nova models for text, speech, images and video, Meta's Llama, OpenAI's GPT and open-weight gpt-oss models, and image models from Stability AI. Developers call them through the Converse API with the same code, so swapping models takes a parameter change. The Playground in the console lets teams compare prompts across models before writing code.

Bedrock's strengths are a broad model catalog under one API, AWS security, private networking and compliance, data that stays in your account, and mature tooling for RAG, guardrails and agents through AgentCore. Its weaknesses are AWS console complexity, pricing that spreads across many services, regional differences in model availability and slower access to some newest models than each lab's own API. Google Vertex AI is the Google Cloud equivalent. Microsoft Foundry serves Azure customers with OpenAI, Claude and thousands of other models. Hugging Face hosts open models. OpenRouter routes across providers with one key. Choose Bedrock if your stack already runs on AWS.

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.

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.