Azure OpenAI vs AWS Bedrock vs Vertex: where I would actually put a production AI workload in 2026

By early 2026, every major cloud has a managed LLM platform. Azure OpenAI Service, AWS Bedrock, Google Vertex AI. The decision is not "which is best" but "which has the fewest sharp edges for your specific situation".

Azure OpenAI Same OpenAI models, slightly higher latency than direct OpenAI API, lower at scale. Region availability is patchy. Quota allocation is opaque. The plus side: enterprise compliance documentation that procurement teams already know.

AWS Bedrock The most model-diverse: Claude, Llama, Titan, Mistral, Cohere, Stability. Pricing is broadly in line with direct provider APIs. Region availability is the best of the three. The minus: the SDK is more verbose than direct calls, and streaming has tail-latency quirks.

Google Vertex AI Gemini exclusivity is the obvious draw. Embeddings are excellent. The Gemini 2.5 Pro grounding feature for live web search is unmatched. The minus: GCP IAM is more painful than AWS IAM, and that is a high bar.

Where I would actually put a production workload Depends on the work: - **Chat for end-users in EU**: Azure OpenAI in westeurope, paired with Cosmos DB and Service Bus. Compliance story is cleanest. - **Document analysis at scale**: Bedrock with Claude. Best output quality, lowest engineering risk. - **Live data lookups**: Vertex with Gemini grounding. No competition. - **Multi-provider failover**: none of them. Use [SarmaLink-AI](https://github.com/sarmakska/Sarmalink-ai) and route across all three.

What changes the calculus If your team is already deep in one cloud, stay there. The 5-10% latency difference is dwarfed by the cost of context-switching three platforms.