AI Reality Check: A Handful of Models Run Countless Apps

Everywhere you look, there’s a new AI assistant, copilot, or automation tool. The market feels crowded—almost overwhelming. But here’s the reality: there aren’t dozens of companies building large language models (LLMs). There are only a handful.

The rest? They’re using those same LLMs under the hood. And once you understand that, you start to see the patterns in what these tools can (and can’t) do.

The Big 5 Creators of LLMs

Right now, the vast majority of AI-powered products are built on models from just a few providers:

• Anthropic → Claude models: Known for safety, reliability, and long-context reasoning; strong focus on alignment and guardrails.

• Google DeepMind (Alphabet) → Gemini models: Multi-modal (text, images, code), tightly integrated with Google Workspace and Vertex AI for enterprise scale.

• Meta (Facebook) → LLaMA open-source models: Community-driven, open-source, optimized for research and developer flexibility.

• Mistral → Lightweight, open-source LLMs optimized for performance: Efficient, modular, and strong for specialized use cases; excels in speed and resource efficiency.

• OpenAI → GPT models: Market leader in versatility and adoption; excels at general-purpose reasoning, creativity, and broad ecosystem integration.

China has its own set of big players (Baidu, Alibaba, Tencent, Huawei), but for most Western businesses, the models above are the foundation.

Others

• AI21 Labs (Israel) → Jurassic LLM family: Strong focus on text generation and comprehension; excels at structured tasks like summarization and document analysis.

• Aleph Alpha (Germany) → Luminous LLMs: Emphasis on explainability, transparency, and multilingual capability; popular in EU for regulatory alignment.

• Cohere → Enterprise-ready LLMs: Optimized for business applications with retrieval-augmented generation (RAG); strong in search, classification, and custom embeddings.

• Stability AI → Stable Diffusion & text models: Leader in open-source generative AI for images; branching into LLMs with emphasis on creative freedom and open access.

• xAI (Elon Musk) → Grok model: Integrated directly into X/Twitter; positioned as conversational and real-time, leveraging social media context.

Who Actually Uses Them?

Anthropic	OpenAI	Anthropic + OpenAI	Google (Gemini)	Mistral + Anthropic + Others	Anthropic + Google + Open AI + Others
Amazon (AWS)	Asana	Box	Google Workplace	Databricks	Atlassian (with Teamwork Graph)
Zoom	ClickUp	Salesforce	Vertex AI
	Duolingo	ServiceNow
	Khan Academy
	HubSpot
	Microsoft
	Notion
	Shopify
	Slack
	Snapchat
	Spotify

Different logos. Different UIs. Same engines underneath.

Meta’s LLaMA models power experiments on Hugging Face and smaller companies seeking flexible, low-cost alternatives to commercial LLMs.

Glean doesn’t build its own LLMs—it’s model-agnostic. Through its Model Hub, it lets companies choose from top providers like OpenAI, Anthropic, Google, Meta, and Mistral. It layers these with RAG and its enterprise knowledge graph, so responses are accurate, secure, and context-aware.

Why It Feels Overwhelming (and Why It Doesn’t Have to)

When you look at the app marketplace, it feels like there are hundreds of AI options. But here’s the secret: most of them are simply packaging the same model in a way that fits their audience or workflow.

• Want help drafting an email? Dozens of tools can do it—because they’re all using OpenAI models.

• Want meeting notes summarized? Same story—Anthropic and OpenAI models are powering most of those apps.

• Want document translation or tone adjustment? Likely Meta or OpenAI models under the hood.

Once you recognize this, the choice gets simpler. You’re not choosing between 50 different AIs—you’re choosing the interface and workflow that fits you best.

How to Cut Through the Noise

Instead of asking:❌ “Which AI tool should I use?”

Try asking:✅ “Which LLM is powering this tool, and does the interface fit my needs?”

That one shift helps you:

• Avoid duplicate tools that do the same thing

• Set realistic expectations about what’s possible

• Focus on adoption, not chasing hype

The Bottom Line

There are only a few companies creating LLMs—but countless companies repackaging them. When you understand this, the AI landscape looks a lot less overwhelming. The trick isn’t testing every tool on the market. It’s learning the strengths of the core models and then picking the applications that align with your work.

That’s when AI goes from confusing buzz to practical everyday value.