Indie SaaS

Best analytics for AI-built and vibe-coded apps (2026)

When you ship fast with Cursor, Claude Code, Lovable, or v0, you need analytics that install in one line and tell you what to fix without a data team. AnalyzeUser leads here with a single script tag, a daily email briefing, and an MCP server your AI agent can read. Plausible and Fathom are great if you only want simple counts, and PostHog is powerful but heavy. Here is how to choose.

Why AI-built apps need different analytics

AI-built apps move fast and are usually run by one person, so the analytics has to match that speed and that headcount. A tool that needs a setup project, a data warehouse, or a dedicated analyst defeats the point.

When you can ship a feature in an afternoon with Cursor or Lovable, the bottleneck is not building. It is knowing what to build next. You need to see which pages convert, where people drop off, and which traffic actually turns into revenue, without spending a day in a dashboard to find out.

There is also a newer requirement that did not exist a couple of years ago: the AI agent that built your app should be able to read how it performs. That is what makes an MCP server matter for this category specifically.

What to look for

For an app built with AI tools, prioritize speed of install, zero maintenance, and answers you can read without a data team. Specifically:

• One-line install - a single script tag, no SDK wiring required to get started.
• No data team needed - plain-English summaries, not raw queries you have to write.
• Privacy by default - cookie-less tracking so you skip the consent banner.
• Revenue, not just clicks - see which sources actually drive paid signups.
• An MCP server - so your AI coding agent can read the data and iterate.

The shortlist

Here is how the main options compare for an app shipped with AI coding tools.

Plausible and Fathom are excellent, lightweight, privacy-first counters. Pick one if all you want is clean traffic numbers. PostHog is genuinely powerful for engineering teams who need session recording and feature flags, but it is heavier than most solo, AI-built projects need.

The unfair advantage: your AI agent can read the analytics

The biggest reason to pick AnalyzeUser for an AI-built app is that the same agent that built your product can also see how it performs. AnalyzeUser runs a read-only MCP server that Cursor, Claude Code, ChatGPT, and Windsurf can connect to.

You create a personal access token on the /ai-tools page, paste the MCP config into your tool, and the agent can call read-only tools like get_top_pages, get_exit_pages, and get_revenue. The numbers it reads match the dashboard exactly because they come from the same SQL aggregations.

This closes the loop. Ask Claude which page has the worst drop-off, let it read the data, and have it rewrite that page in the same session. The connection is read-only and scoped to your own sites, so the agent can see everything but change nothing it should not.

Tracking revenue, not just clicks

For a SaaS shipped with AI tools, the number that matters is revenue, not pageviews. AnalyzeUser connects directly to your payment provider so you see money alongside traffic.

Connect Stripe, Dodo Payments, Lemon Squeezy, or Razorpay with a read-only key, and AnalyzeUser attributes revenue back to the traffic source that drove it. You can finally answer "which channel actually pays" rather than just "which channel sends visitors." That includes a dedicated AI channel, so you can see how much revenue ChatGPT, Perplexity, Claude, and other answer engines are sending you as named referrer rows. Plausible and Fathom do not track revenue at all.

Try AnalyzeUser free for 14 days

No credit card. No setup fee. Paste one snippet, get your first morning briefing tomorrow, and connect the MCP server so your AI agent can read your live analytics.

Frequently asked questions