Is GEO worth it for a developer tool company?

The number that decides it

Not a survey. One thing you can find out.

Which coding agents do your users run? Because the agents differ enormously in how often they read the web when deciding:

If your users are mostly on Codex, published content is in the room for almost every decision and generative engine optimization pays back in weeks.

If they are mostly on Claude Code, the content reaches about a third of the decisions in the moment they are made. For the rest, your leverage is what the model already learned, which moves over years, and what is in the repository, which you reach through templates and integrations.

Most companies have some of both, which is why the answer is "partly".

What is worth doing regardless

This part is cheap and I would do it for any developer tool, whatever the answer above.

• An answer-first paragraph of 40 to 60 words under every question-shaped heading. Ten minutes a page.
• Content in the HTML source rather than injected by JavaScript. Check with curl.
• The current version stated on documentation pages, in the text.
• A quickstart that runs when pasted into an empty project.
• Tables instead of paragraphs for anything comparable.
• AI crawlers allowed by name in robots.txt.
• llms.txt at the root and a Markdown twin of key pages.

That work helps chat answers, AI Overviews, classic search, and the agents that fetch your documentation while writing an integration. It is the highest-return hour in this whole area and most of it is a single pass over pages you already have.

What is worth doing if your buyers research first

Categories where people compare before adopting: authentication, databases, observability, anything with a compliance dimension. If that is you, a proper programme pays.

• A tracker, starting cheap. Around $29 a month at entry level, around EUR 89 for something with real analytics.
• A fixed prompt set that includes symptom wording, not only category names.
• Third party citations, which models weight above self-description.
• A monthly read of which of your pages get cited, and more of whatever that is.

What is not worth doing

A large monitoring subscription before you have acted on a small one. A dashboard nobody changes anything because of is a subscription, not a strategy.

Vendor comparison pages where you win every row. Discounted by models and by readers.

Treating a visibility score as a proxy for adoption. They come apart, measurably. Requests to make an app's data persist went to one product in 280 of 359 sessions on apps with their own server, and to a different one in 111 of 142 on apps without one. No mention count sees that, because a chat prompt has no repository in it.

The test that settles it in a weekend

Before committing a budget either way:

1. Take three repositories that look like your users' projects, with real lock files.
2. Write ten requests the way your users describe the problem, not the way you name the category.
3. Run each five times with Claude Code and Codex, in a sandbox.
4. Record what got installed, and read the sessions you lost.

300 runs, a few hundred dollars in tokens.

If the losses happen in the chat window, invest in generative engine optimization. If they happen inside the repository, no amount of it will reach them, and you need a different measurement.

The short version

Do the cheap content work. It is genuinely good practice and it helps everywhere.

Buy the tracker if your category is researched before it is adopted.

Do not assume a visibility score tells you anything about installs, because in our data those two numbers move for different reasons.