What are Anthropic and Claude?

Anthropic is the artificial intelligence lab founded in 2021 by a group of former OpenAI researchers, with model safety and alignment as an explicit part of its mission from the start. Claude is the family of models and assistants that resulted: a general-purpose product for conversation, writing and reasoning, alongside Claude Code, a tool built for agent-assisted software development directly from the terminal. Anthropic's most visible contribution to the ecosystem, beyond the models themselves, is arguably the Model Context Protocol, an open standard for connecting language models to external tools and data that other labs later adopted, a concrete example of a single vendor pushing a piece of infrastructure toward the industry commons instead of keeping it as proprietary leverage, which is not something every lab chooses to do. On the commercial side it competes head to head with OpenAI and Google, leaning on reliability and predictable behavior for regulated enterprise use as its main pitch.

What it does

Beyond the models, Anthropic's most visible contribution to the ecosystem is arguably the Model Context Protocol (MCP): an open standard for connecting language models to tools, data and external services in a uniform way, later adopted by other labs and platforms. It is a concrete example of a single vendor pushing a piece of infrastructure that becomes an industry commons rather than staying a proprietary lock-in. Anthropic sells access to Claude models via API and through the major hyperscalers' cloud platforms, alongside its direct product.

How to evaluate it as a buyer

Anthropic positions itself as a credible alternative to OpenAI and Google, with a commercial emphasis on reliability, predictable behavior and adoption in regulated enterprise contexts. It should be evaluated on equal footing with the other frontier labs: no GenAI vendor is objectively "safer" regardless of use case, and every choice needs verifying against your own domain (answer quality, cost, latency, data agreements) rather than marketing narrative. The practical advantage for teams already working with MCP is interoperability: the tool infrastructure built around the protocol also works with other models that support it.