HowFoundation Models vs LLM ( VLM) differ and which IT Giant to select for...

Understanding Foundation Models (FM) and Large Language Models (LLM) Across Cloud Providers

With the rapid advancements in AI, cloud providers like AWS, Azure, Google, NVIDIA, and Meta offer powerful Foundation Models (FM) and Large Language Models (LLM) to cater to various business needs. This article aims to provide a clear understanding of these offerings, their pricing models, and specific LLMs for video tagging generation—essential knowledge for a pre-sales team.  

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AI Offerings by Major Cloud Providers

A. Google Cloud (Vertex AI & Gemini)

Foundation Models (FM):

Gemini → A multimodal AI model capable of handling text, images, audio, and video.  

Vertex AI → A platform offering access to foundation models, including Gemini.  

Large Language Models (LLM):

 Gemini Pro → Optimized for text-based applications like chatbots, summarization, and code generation.  

Pricing Model:

 Vertex AI Pricing: Based on API usage and model type (on-demand or provisioned throughput).  

  Gemini Pricing: Pay-per-token usage.  

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B. Amazon Web Services (AWS)

Foundation Models (FM):

 Amazon Bedrock → Provides access to multiple FMs from partners like Anthropic, AI21, and Meta.  

  Titan Models → Amazon’s proprietary foundation models.  

Large Language Models (LLM):

Titan Text → Amazon’s text generation model.  

 Claude (Anthropic), Llama (Meta), Jurassic (AI21) → LLMs available via Bedrock.  

Pricing Model:

Bedrock Pricing: 

    - On-Demand & Batch: Pay-per-use.  

    - Provisioned Throughput: Subscription-based pricing for enterprise needs.  

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C. Microsoft Azure  

Foundation Models (FM):

 Azure OpenAI Service → Hosts OpenAI's foundation models.  

 Phi-2 → Microsoft’s smaller-scale foundation model.  

Large Language Models (LLM):

  - GPT-4, GPT-3.5→ OpenAI’s advanced LLMs hosted by Azure.  

  -Turing-NLG → Microsoft’s in-house NLP model.  

Pricing Model:

  - Azure OpenAI Pricing: Pay-per-token for API usage.  

  - Custom Model Training: Additional costs apply.  

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D. NVIDIA AI

Foundation Models (FM):  

  - NVIDIA AI Foundation Models→ Includes models for text, vision, and scientific applications.  

  - Nemotron-3 → A general-purpose foundation model.  

Large Language Models (LLM):

  -Nemotron-3 8B → NVIDIA’s LLM for text generation.  

  -BioNeMo → LLM focused on biomedical applications.  

Pricing Model:

  - Pricing depends on GPU usage and API call volume (available upon request).  

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E. Meta (Facebook AI)

Foundation Models (FM): 

  - Llama Models (1, 2, 3) → Meta’s open-source foundation models.  

Large Language Models (LLM):

  - Llama 2, Llama 3→ Designed for text-based AI applications.  

Pricing Model:

  - Available via Azure, AWS, and Google Cloud with platform-dependent pricing.  

Conclusion

For pre-sales teams, understanding which AI model best fits a business need is crucial:  

- If you need a multimodal AI solution (text, images, video, code), opt for a Foundation Model like Gemini, Titan, or Nemotron.  

- For text-only applications (chatbots, summarization, translation), consider LLMs like GPT-4, Claude, or Llama 2.  

- For video tagging applications,Gemini Pro Visio , TagGPT and Azure AI Video Indexer are leading solutions.  

Each cloud provider offers different pricing models, so choosing the right cost-effective solution depends on usage, scalability, and enterprise needs.  

Steps for Pre-Sales Teams:

✅ Understand client requirements (multimodal vs. text-based AI).

✅ Compare pricing models for cost-efficiency.

✅ Recommend the right cloud provider based on model availability and integration needs.

By leveraging these insights,  teams can effectively identify AI solutions tailored to their needs.