Cloud LLM Providers in 2026: Bedrock vs Vertex vs Azure OpenAI

Cloud LLM access has consolidated around the three hyperscaler offerings — AWS Bedrock, Google Vertex AI, Microsoft Azure OpenAI Service. The 2024-2026 period has seen substantial competitive dynamics with each platform expanding capabilities.

I want to walk through where each sits.

AWS Bedrock#

Bedrock offers the broadest model selection:

• Multi-model access — Anthropic Claude (substantial), Meta Llama, Mistral, Amazon Nova, Cohere, plus various.
• Strong AWS integration — IAM, S3, CloudWatch, etc.
• Enterprise features — guardrails, monitoring, fine-tuning.
• Bedrock Agents for agentic deployments.
• Knowledge Bases for RAG.

Best for: AWS-anchored deployments wanting multi-model flexibility.

Google Vertex AI#

Vertex AI anchored around Gemini:

• Gemini models as the flagship plus other partner models.
• Strong Google Cloud integration — BigQuery, Cloud Storage.
• Strong multimodal capability.
• Vertex AI Agent Builder for agentic.
• AutoML integration for broader ML.

Best for: GCP-anchored deployments, Gemini-preferring teams.

Azure OpenAI Service#

Azure OpenAI is the Microsoft-OpenAI integration:

• OpenAI models with Azure deployment.
• Strong Microsoft enterprise integration.
• Azure AI Foundry as broader AI platform.
• Strong governance features.

Best for: Microsoft-anchored deployments, OpenAI-preferring teams.

The choice framework#

Pick Bedrock if:

• AWS-anchored.
• Multi-model flexibility matters.
• Claude is preferred.

Pick Vertex AI if:

• GCP-anchored.
• Gemini is preferred.
• Multimodal needs.

Pick Azure OpenAI if:

• Microsoft-anchored.
• OpenAI models preferred.
• Enterprise compliance heavy.

Direct provider APIs#

Many sophisticated deployments use direct provider APIs (Anthropic, OpenAI, Google) for:

• Latest model access — sometimes faster than cloud equivalents.
• Lower cost in some cases.
• Specific features not yet in cloud offerings.

The trade-off is cloud-vendor integration.

What’s coming in 2026 and 2027#

Three things to watch:

Model availability gaps between direct and cloud APIs continue to close.

Multi-region deployment continues to mature.

AI-specific cloud features continue to differentiate.

Where pdpspectra fits#

Our AI engineering practice deploys across all three cloud providers depending on context.

Related reading: the AI gateway pattern post, the bedrock vs OpenAI vs Anthropic post, and the LLM router pattern post.

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Cloud LLM choice depends on broader cloud strategy. Talk to our team about your AI cloud architecture.