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Cloud AI Platforms — AWS vs Azure vs GCP

What this is: a map of the three major clouds' AI stacks — what each service is, and the equivalent tool on the other two clouds. Read the overview here, then dive into the per-cloud pages: AWS · Azure · GCP.

The Two Layers Every Cloud Has

Each provider splits its AI offering into the same two layers — learn this and you can read any of the three:

  1. Generative-AI platform — managed access to foundation models (LLMs) with agents, RAG, guardrails, and evaluation. AWS Bedrock · Azure AI Foundry + Azure OpenAI · Google Vertex AI.
  2. Classic ML platform — build/train/deploy your own models end to end. AWS SageMaker · Azure Machine Learning · Google Vertex AI (training side).

Plus a shelf of pre-built AI services (vision, speech, language, document, search) that need no model training.

The Cross-Cloud Equivalents Table

The single most useful reference — "I know X on one cloud, what's it called on the others?"

Capability AWS Azure Google Cloud
GenAI platform Amazon Bedrock Azure AI Foundry Vertex AI
Hosted LLM API Bedrock (Claude, Nova, Llama…) Azure OpenAI Service (GPT-4o, o-series) Vertex AI / Gemini API
Own-model ML platform SageMaker AI Azure Machine Learning Vertex AI (training/pipelines)
Model catalog / hub Bedrock + SageMaker JumpStart Azure AI Foundry Model Catalog Vertex AI Model Garden
Build agents Bedrock AgentCore / Agents Azure AI Foundry Agent Service Vertex AI Agent Builder
RAG / knowledge base Bedrock Knowledge Bases Azure AI Search + Foundry Vertex AI Search / RAG Engine
Vector search Amazon OpenSearch / Aurora pgvector Azure AI Search (vector) Vertex AI Vector Search
Safety / guardrails Bedrock Guardrails Azure AI Content Safety Vertex safety filters / Model Armor
Model evaluation Bedrock Evaluations Azure AI Foundry evaluation Vertex Gen AI Evaluation Service
Enterprise assistant Amazon Q Microsoft 365 Copilot / Copilot Studio Gemini for Google Workspace
Document extraction (OCR) Amazon Textract Azure AI Document Intelligence Google Document AI
Vision Amazon Rekognition Azure AI Vision Vertex AI Vision / Vision API
Speech-to-text / TTS Amazon Transcribe / Polly Azure AI Speech Google Speech-to-Text / Text-to-Speech
Language (NLP) Amazon Comprehend Azure AI Language Cloud Natural Language AI
Managed search Amazon Kendra Azure AI Search Vertex AI Search
SQL-native ML Amazon Redshift ML (via Azure ML) BigQuery ML

How to Choose (the 2026 reality)

  • Model quality gaps are small (single-digit to ~15%); platform fit is the durable decision. The integration you already have — IAM, VPC, identity, audit, billing — outweighs which model is marginally ahead this quarter.
  • Default to your existing cloud. On AWS already? Bedrock. Microsoft shop with Entra ID + 365? Azure. Data in BigQuery? Vertex.
  • Model exclusives that can force a choice: GPT/o-series only on Azure OpenAI; Gemini only on Vertex; the broadest multi-vendor catalog (Claude, Llama, Mistral, Cohere, Nova) on Bedrock.
Pick When
AWS Already on AWS; want the widest model catalog + deepest IAM/VPC control
Azure Microsoft/Entra/365 estate; need GPT-4o/o-series; enterprise productivity + Copilot
GCP Data-gravity in BigQuery; want Gemini, long context, native multimodality, Google-Search grounding

The QA / Testing Angle

Whichever cloud, the testable surfaces are the same (this is why the AI Test Strategy is cloud-agnostic): the model/endpoint (quality + latency + cost), the RAG pipeline (retrieval + faithfulness), guardrails (injection/PII), and the gateway (routing, budgets, failover — see Enterprise LLM Gateway Architecture). Each cloud's managed evaluation service (Bedrock/Azure/Vertex) is your release-gate tool.

Where to Go Next