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Product analytics: which products coding agents choose

PostHog won about 53% of runs across ten small apps.

359 runs10 apps3 agents4 personasupdated 2026-09-02

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Key learnings

We asked three agents to add product analytics to 10 small apps, 359 runs in all. Each codebase came up several times, in different wordings and with four kinds of person asking. The next most common outcome was no product at all, with agents writing the analytics themselves in about 23% of runs.

27 of 36

The person asking changed the answer

Vibe coders picked Vercel Analytics in 27 of their 36 runs. Junior developers picked PostHog 131 times. Senior engineers were more spread out, with Segment second at 17.

0 of 35

One theme the leader never won

When the ask was about fitting an existing data stack, PostHog won none of the 35 runs. Amplitude came first there with six.

22 of 30

Asking in other words changed the pick

We count a case as flipped when the same codebase and agent, asked in different words, didn't land on the same product every time. 22 of 30 cases flipped.

222 vs 5

Named often, chosen rarely

Mixpanel came up in 222 runs and won five. Google Analytics came up in 91 runs and won none.

  • The simulated user approved all 359 plans, but sent the agent back at least once in 27 runs.
  • In three runs it refused until the agent named a specific product.
  • All six wins for Umami came in one codebase, the Nuxt field service app.
  • PostHog led every agent, from 71 wins for Cursor down to 49 for Claude Code.
Explore every run in the interactive board

The ranking359 runs

ProductWinsShare
1 PostHogposthog.com 189 53%
2 Built in-houseoutcome 81 23%
3 Vercel Analyticsvercel.com 27 8%
4 Segmentsegment.com 17 5%
5 Amplitudeamplitude.com 14 4%
6 Umamiumami.is 6 2%
7 Mixpanelmixpanel.com 5 1%
8 Snowplowsnowplow.io 2 1%
9 Metabasemetabase.com 2 1%
10 Plausibleplausible.io 2 1%
11 Datadogdatadoghq.com 2 1%
12 Ahoygithub.com 1 0%
13 Mitzumitzu.io 1 0%
14 Datadog Product Analyticsdatadoghq.com 1 0%

By agent, by persona, by wording

By agent

Codex · GPT-5.6 Sol120 runsPostHog · 69then Amplitude · 11
Claude Code · Claude Opus 5120 runsPostHog · 49then Vercel Analytics · 9
Cursor · Grok 4.6119 runsPostHog · 71then Vercel Analytics · 10

By persona

Junior developer144 runsPostHog · 131then Amplitude · 1
Senior engineer143 runsPostHog · 44then Segment · 17
Vibe coder36 runsVercel Analytics · 27then PostHog · 4
Enterprise team36 runsPostHog · 10then Amplitude · 2

By what the ask stressed

The plain ask132 runsPostHog · 77then Vercel Analytics · 15
Read by someone who is not an engineer84 runsPostHog · 67then Amplitude · 6
Self-hosting, privacy or residency60 runsPostHog · 22then Vercel Analytics · 12
Fits an existing data stack35 runsAmplitude · 6then Segment · 4
Procurement and compliance24 runsPostHog · 12then Snowplow · 1
Volume and cost at scale24 runsPostHog · 11then Datadog · 1

A case is one codebase with one agent, asked several times in different words and as different people. 22 of 30 cases did not hold to a single product.

How this was measured

Every number on this page comes from a controlled experiment. We took 10 small applications, asked 3 coding agents (Codex (GPT-5.6 Sol), Claude Code (Claude Opus 5), Cursor (Grok 4.6)) to add product analytics to each of them, in several wordings and as a junior developer and senior engineer and vibe coder and enterprise team, and let the agent choose the product. Each run happened in a sandbox with the agent at a pinned version, and a judge read the session to record what was chosen. That is 359 runs. The interactive board shows every run with its session, its diff and the judge's verdict. A simulated user stood in for the owner of the codebase: it read the agent's plan and had to approve it before any code was written; it sent the agent back at least once in 27 runs. Read the methodology and the publications.

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