The three clouds no longer compete on having "an LLM": they compete on who solves everything around the LLM better. Here's how they compare in practice in mid-2026.
In 2026, evaluating Vertex AI, Amazon Bedrock, and Azure AI Foundry solely on language model quality is an analysis mistake. All three give access to similarly-classed models (Gemini, Claude via Bedrock, GPT and OpenAI models via Azure), and there's actually overlap: Anthropic's Claude is available both on Bedrock and on Vertex AI Model Garden. The real difference is at the platform layer: agents, managed RAG, data integration, and governance.
Vertex AI Model Garden offers the broadest, most heterogeneous catalog: proprietary models (Gemini, Imagen, Veo), partners (Claude, Mistral), and self-deployed open-weight models (Llama, Gemma) under one surface. Amazon Bedrock specializes in being the multi-provider hub by design since its launch, with Anthropic's Claude as its flagship model alongside Llama, Mistral, and Amazon's own Nova models. Azure AI Foundry has the deepest integration with OpenAI's models (GPT-4.x, o-series) thanks to the Microsoft-OpenAI strategic relationship, plus a growing catalog of open-weight models via its model catalog.
If the priority is day-one access to OpenAI's models with enterprise SLAs, Azure wins by default. If the priority is Claude with the most native integration into AWS tools (Lambda, Step Functions), Bedrock. If the priority is Gemini with the best native multimodal handling or the option to mix Gemini with Claude under one bill, Vertex AI.
All three have converged on offering managed agent frameworks: Vertex AI Agent Builder with the Agent Development Kit and Agent Engine, Amazon Bedrock Agents with native integration to Knowledge Bases and Action Groups, and Azure AI Foundry Agent Service with direct integration to Semantic Kernel and Copilot Studio. They're technically on par in basic capabilities (function calling, multi-agent orchestration, session traceability); the practical difference lies in how well the agent integrates with the rest of the stack the company already uses. A company with Microsoft 365 and Teams as its backbone gets immediate integration advantages on Azure it can't easily replicate on the other two.
Vertex AI Search and Amazon Bedrock Knowledge Bases solve the same problem -- managed indexing, retrieval, and grounding -- with similar approaches. Vertex AI Search has an edge in out-of-the-box hybrid search and broad enterprise-source connectors. Bedrock Knowledge Bases integrates more naturally if the data already lives in S3 and OpenSearch. Azure AI Search (the RAG component within Azure AI Foundry) has been mature for longer as a standalone search product and offers finer control over indexes and enrichment skillsets (inherited from Azure Cognitive Search).
All three charge per input/output token at the serverless tier, with Provisioned Throughput (Vertex AI), Provisioned Throughput (Bedrock), and PTUs (Azure) as reserved-capacity options for predictable traffic. In terms of commitment, AWS and Azure usually have more mature annual-commitment discounts (Savings Plans / reservations) than GCP's equivalent, though Vertex AI compensates with Committed Use Discounts also applicable to AutoML and custom training compute.
The decision is rarely won on a feature table: it's won on integration cost. If the data lives in BigQuery and the team already operates on GCP, any RAG or fine-tuning effort on Vertex AI saves weeks of plumbing versus moving data to another cloud. If the data lives in S3 with Glue/Athena pipelines, Bedrock reduces equivalent friction. If the organization is Microsoft-centric (Active Directory, SharePoint, Dynamics), Azure AI Foundry generally wins on identity and data integration, not model superiority.
For companies in the region, an additional factor is regional availability: GCP's `southamerica-east1` and equivalent AWS/Azure regions in Brazil offer the lowest latency without leaving the continent, but not every service (agents, certain Gemini or Bedrock models) is yet available in every South American region. Checking the specific regional availability matrix for the services you'll actually use -- not just the base model -- should be part of the proof of concept before signing any annual commitment.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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