OpenAI, Anthropic, Google and the rest: what each is for when you are building

Business owners ask us which AI company is the right one the way they might ask which truck brand to buy. The better question is which one fits the job, and the answer changes often enough that we keep it loose.

Two kinds of company

The first thing to understand is that these companies do not all sell the same thing. Some run their models on their own computers and let you send text in and get text back, the way you use any web service. We call those hosted. Others publish the model itself so anyone can download it and run it on their own machines. We call those open. A few do both.

Hosted means someone else keeps it running, updates it, and bills you for use. Open means you can keep your data in house and control the cost, but you are now running infrastructure. For most small businesses, hosted is where to start.

What each is generally known for

As of this writing, here is the map we carry in our heads. It is a map of reputations, not a ranking, and it will be out of date in a few months.

• OpenAI and the GPT family. The one most people have used directly. Known for a wide range of models, broad tooling, voice and image features, and the largest set of ready made connections to other software. When a client's existing tools already talk to an AI, it is often this one.
• Anthropic and the Claude family. Known for careful, instruction following writing and for handling long documents well. Builders tend to reach for it when the output will be read closely or when the model needs to follow a detailed set of rules without wandering.
• Google and the Gemini family. Known for working with very long inputs and mixed inputs (text, images, video) and for fitting naturally into Google's own products. A business that lives in Google Workspace often finds this the path of least friction.
• Meta and the Llama family. The best known open family. Businesses and builders download it and run it themselves. Known for making self hosting practical for ordinary companies rather than only for labs.
• Mistral. A European company known for compact, efficient open models that run well on modest hardware, plus hosted options. Often chosen when the model has to be small and quick.
• DeepSeek. Known for open models that handle reasoning heavy tasks well and for publishing how they were built. Some clients will not use it for privacy or policy reasons, and we respect that.
• xAI and the Grok family. A hosted option tied closely to the X platform, known for awareness of current events and a more casual tone. We use it less in client products, but it is on the list.

How the map turns into a decision

We do not pick a company and then force every job onto its models. We pick per job. A website assistant that answers from a painting company's pages, a screener that calls applicants, and a nightly agent that reads a builder's lead pipeline are three jobs, and it is common for them to run on three different families inside one client's setup.

The questions that steer it:

1. Does the data need to stay in the client's control? If yes, an open family we host ourselves moves to the front.
2. Is the output going to be read word for word by a customer? If yes, writing quality matters more than speed.
3. Does it need to be quick enough for a live conversation? If yes, the heavier models drop out.
4. Does the client already have a relationship with one provider through other software? If yes, that provider gets a head start.
5. How much will it run? High volume pushes toward cheaper or self hosted options.

Why we refuse to be loyal

Every one of these companies ships new models on its own schedule, and the reputations above shift with each release. A family that was the obvious choice for document reading one quarter can be overtaken the next. We build every product so the model behind it is a setting we can change, and we keep a folder of real test inputs per client so a swap can be checked in an afternoon rather than argued about.

The companies compete hard, which is good for the businesses we build for. Abilities improve and costs come down, and the thing we built last year gets better without a rebuild. Loyalty to one company would mean giving that up.

What this means for an owner

You do not need to follow the AI news. You need a builder who does, and who has set things up so a change of model is a small job rather than a project. Ask whoever builds your software two questions: which family is behind each piece, and how hard it is to swap. If the second answer is "hard," that is the thing to fix.

This is how we see the field from the chair of a studio in Delray Beach that builds assistants, agents, and software for builders, landscapers, advisors, and a transit company. The names change; the way we choose does not.

Questions people ask