Azure AI Foundry pulls 1,700+ models; Bedrock, Vertex AI trail in catalog size

The three platforms emerged from different corporate starting points and retain those strategic DNA strands. Amazon Bedrock was built as a native AWS service, which means security teams already familiar with AWS IAM, VPC networking, CloudTrail logging, and KMS encryption see Bedrock as just another AWS service—no separate audit needed. Microsoft Azure AI Foundry, conversely, is the rebrand of Azure AI Studio and Azure OpenAI Service rolled into one surface, and it inherits both Microsoft's productivity ecosystem (365, Teams, Copilot, Sentinel) and, crucially, exclusive first-party access to OpenAI's frontier models under the two companies' commercial partnership. Google Vertex AI, the ML research group's platform, retains the strongest data-pipeline integration of the three—BigQuery is native, and the platform originated the Agent-to-Agent (A2A) protocol, an open standard for cross-vendor AI agent communication. Each platform's compliance and pricing structures follow that origin story: Bedrock publishes granular security certifications (SOC 1/2/3, HIPAA, FedRAMP High, ISO 27001, GDPR) as selling points to regulated finance teams; Azure frames itself as a production lifecycle tool with evaluation and red-teaming, appealing to large enterprises that need governance as much as model access; Vertex bets on teams whose primary job is training and evaluating models, not just running hosted inference. The catalog gap—1,700 models on Azure versus 200-plus on Vertex and just over 100 on Bedrock—matters less than it appears on paper: the models most production teams actually need (Claude, Llama, Mistral, DeepSeek) appear on all three. The real lock-in is OpenAI's GPT-5 exclusivity to Azure and the each platform's native integrations to its parent cloud.