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# Exploring LLMs across clouds

Are you overpaying for enterprise AI infrastructure? Compare the distinct LLM architectures, RAG capabilities, and hidden pricing structures across Amazon, Google, and Microsoft to build autonomous agentic ecosystems.

- **Speakers:** [Tomislav Tipurić](https://www.wearedevelopers.com/@tomislav-tipuric)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 28:38
- **URL:** https://www.wearedevelopers.com/videos/1457-exploring-llms-across-clouds

## Summary

The shift from traditional text-in/text-out language models to autonomous, multimodal 'agentic AI' is rapidly reshaping cloud ecosystems. Rooted in early transformer architectures, generative AI has evolved beyond simple chatbots into complex reasoning models capable of natively processing images, video, and audio. As this technology matures, navigating the AI landscape requires a deep understanding of how the top three cloud providers—Amazon, Google, and Microsoft—position their foundational models, pricing structures, and developer platforms to capture enterprise adoption.

Each major cloud vendor offers a distinct approach to model hosting and infrastructure. Google leverages its DeepMind research with the Gemini family natively on Vertex AI, while Microsoft maintains a tight integration with OpenAI’s GPT and reasoning models via Azure AI Foundry. Amazon, previously relying on its basic Titan models, has recently introduced the Nova family via Amazon Bedrock to capture the agentic market. While token pricing across these providers is beginning to equalize, subtle economic differences remain; Amazon currently offers the most cost-effective ingestion for massive document inputs, whereas Microsoft and Google lead in cost efficiency for high-volume content generation.

At the enterprise application level, leveraging these foundational models effectively demands Retrieval-Augmented Generation (RAG). By combining vector search—translating text into mathematical embeddings stored in managed services like Cosmos DB, AlloyDB, or open-source solutions like Pinecone—organizations can ground AI in proprietary data to eliminate model hallucinations. From automating complex document processing and contact center analytics to building semantic retail recommendation engines, the application layer is vast. However, successfully adopting these AI developer tools requires rigorous organizational change management rather than simply provisioning access, ensuring teams seamlessly integrate AI into existing engineering workflows. Ultimately, entering the 'brave new world of agentic AI' requires balancing strategic cloud architecture with autonomous system design.

**Keywords:** agentic AI, cloud vendor AI ecosystems, amazon bedrock nova models, google vertex AI gemini, azure AI foundry, retrieval-augmented generation (RAG), vector search embeddings, foundational reasoning models, multimodal LLM interfaces, AI token pricing economics, model hallucination mitigation, developer AI workflows, organizational AI change management, enterprise vector databases, semantic recommendation engines

## Chapters

1. **The fundamental mechanics of large language models** (00:05) — How generative pre-trained transformers use probabilistic predictions and temperature settings to generate text.
1. **Evolution from text interfaces to agentic reasoning models** (02:52) — Tracking the transition from basic text chatbots to multimodal capabilities and autonomous agents.
1. **Comparing foundational AI models across major cloud providers** (06:26) — A breakdown of embedding, multimodal, and reasoning models offered by Amazon, Google, and Microsoft.
1. **Model performance leaderboards and API token pricing structures** (12:29) — Evaluating top model rankings on public arenas alongside an analysis of token-based input and output costs.
1. **Extending AI capabilities with retrieval augmented generation** (15:49) — Implementing knowledge engineering patterns and leveraging developer assistants to ground generative output in custom unstructured data.
1. **Semantic similarity and vector database search mechanics** (20:11) — How vector representations and orchestrators map semantic meaning to improve database retrieval accuracy.
1. **Cloud infrastructure supporting retrieval augmented generation ecosystems** (22:12) — Mapping the managed container environments, vector databases, and foundational AI solutions across major cloud ecosystems.
1. **Enterprise implementation scenarios for generative AI applications** (25:25) — Practical examples of deploying natural language understanding for contact center analytics and retail recommendation engines.
1. **Managing organizational change for AI developer productivity tools** (27:10) — Strategies for testing, documenting, and implementing generative coding assistants to improve engineering team velocity.

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