Report card

Do AI agents pick Statsig?

How often agents choose Statsig when a developer needs feature flags — measured across Claude, GPT, Gemini.

Statsig

#3 of 5 feature flags
3%Pick Rate
95% CI 1%6%

When developers ask an AI agent for feature flags, Statsig is picked 3% of the time — ranking #3 of 5 measured across Claude, GPT, Gemini.

Picked 3% Named, not picked 11% Never surfaced 86%

Beaten by LaunchDarkly (25%), Unleash (6%).

Awareness

0 docs in FineWeb (10BT sample) · 1.7K URLs in Common Crawl

In the raw web crawl, but thin in the filtered corpus models actually train on — an awareness gap at the source.

AdoptionDid the pick convert to installs? Public Adoption = npm downloads of your packages. The proof half of the funnel.

10.4M installs / month ▲ 13% MoM

Growing despite a low Pick Rate — winning the agent pick is upside, not a dependency.

Get the full report

The per-model and per-surface breakdown for Statsig — where it wins, where it loses, and to whom — plus an alert when the Pick Rate moves.

About Statsig

Statsig is a feature flagging and experimentation platform for shipping and validating product changes.

Statsig provides feature gates (feature flags), dynamic configuration, and A/B testing tools for development teams. It lets you roll out features to specific users or segments, run controlled experiments, and measure impact on business metrics. It targets teams who want to decouple feature releases from code deploys and make data-informed product decisions.

Where to start

Create an account at statsig.com, then initialize the Node.js server SDK with your server secret key to begin checking feature gates and dynamic configs for your users.

Install

Links and summary verified from public sources.

The rest of the Feature Flags ranking

← Full Feature Flags leaderboard · How this is measured