Kutta for Product
Product analytics from the data you already have
No SDK install. No six months of event-schema planning. Your usage data already exists in your database and exports, Kutta’s AI turns it into funnels, cohorts, and adoption views today.
Instrumented platforms are powerful, and they make you pay for that power in setup time, event-volume pricing, and engineering dependency. Kutta is the other path: product analytics computed from the data your product already produces.
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The problem
Event tracking is a tax on every question
Product teams without a dedicated data function get stuck between heavyweight analytics platforms and flying blind. The friction is structural.
The instrumentation project
Event schemas, SDK installs, taxonomy debates, months of planning before the first funnel renders, and every new question risks a new tracking ticket.
Event-volume pricing
Usage-based billing means your analytics bill grows with your product’s success, and finance asks why the dashboard tool costs more than the database.
Cohorts locked behind SQL
The retention question is simple; the query isn’t. Without a data team, week-over-week cohort curves stay unanswered.
How Kutta helps
Funnel drop-off analysis from exports, not SDKs
Upload usage exports or connect your product database, and Kutta’s AI traces activation, onboarding, and feature-adoption losses. Find where trial users churn, the screen, action, or day they disengage, without instrumenting a single event.
Funnel drop-off tracing
Activation, onboarding, and adoption losses stage by stage
No event schema required
Works from the usage data your product already records
Trial churn pinpointing
Identify where and when trial users disengage
Database or file import
Connect directly or upload CSV, Excel, and Parquet exports
Retention Cohorts — weekly
Cohort analysis without SQL
Ask "compare week-over-week retention across releases" in plain English. Kutta computes retention cohorts from your data and lets you slice by segment, plan tier, or signup period, the analysis that normally waits on a data team, available to every PM.
Retention cohorts on demand
Week-over-week curves across releases and segments
Plain-English slicing
Segment by plan, geography, or signup cohort conversationally
Drill-down to users
From a cohort curve to the accounts behind the dip
Grounded answers
Real queries against your data, verifiable, not approximated
Feature adoption, visible at last
See which features land and which never get touched. Kutta builds adoption views from usage data so roadmap debates run on evidence, and the whole board shares to stakeholders as a live link, not a screenshot.
Feature adoption heatmaps
Usage intensity across your feature surface
Release impact comparison
Adoption and retention before vs. after each ship
Shareable dashboards
Stakeholders see live numbers, not stale decks
Flat pricing
No event-volume billing: free tier, then $30/mo Pro
Feature Adoption — 30 days
How it works
From raw data to answers in three steps
Connect your data
From spreadsheets to enterprise databases, Kutta connects to your data wherever it lives: CSV, Excel, Parquet, and more.
Visualize it
Simply describe your data or what you want to track. Kutta builds a dashboard in seconds.
Analyze & uncover
Query in plain English, filter with precision, and turn raw data into real decisions.
Compare
How Kutta compares for product analytics
Instrumented platforms, notebook tools, and AI analysts each serve a different team shape. Honest answer: real-time event streams need an instrumented tool, most other product questions don’t.
| Kutta | Julius AI | Hex | |
|---|---|---|---|
| Setup required | Upload exports or connect your DB | Upload files per chat session | Warehouse connection + notebook authoring |
| Skills required | Plain English | Plain English (chat) | SQL/Python, built for technical analysts |
| Output | Persistent live dashboards | One-off chat answers | Notebooks and published data apps |
| Large datasets | Multi-million rows in-browser via Arrow | Documented struggles with large files | Depends on warehouse compute |
| Price to start | Free tier · Pro $30/mo flat | Free tier capped at ~15 messages/mo | Free community tier; $36/editor/mo Pro |
Competitor details as of June 2026; see full comparisons below for sources and where each tool wins.
Want the full picture?
If you need ad-hoc Python scripting and statistical modeling artifacts, Julius is genuinely good at that. If you need living dashboards your team checks weekly, unlimited questions, and big files that stay interactive, that’s what Kutta is built for.
Choose Kutta if…
- You ask a lot of questions, message caps change how you work, and Kutta has none
- Your files are big: multi-million-row CSVs and Parquet stay interactive in the browser
- You want dashboards that persist: a board the team checks weekly, not a chat to re-run
- You’re a team: flat pricing and shareable boards instead of a $375/mo workspace tier
Choose Julius AI if…
- You need arbitrary Python: custom statistical tests, model fitting, bespoke transforms
- You want generated artifacts like Excel files and slide decks from your analysis
- You work alone on one-off analyses where a chat transcript is enough
- You’re a student, Julius offers an aggressive 50% educational discount on paid tiers
Pricing — Kutta: Pro $30/mo. Julius AI: Free $0 · Plus/Lite ~$20–35/mo · Pro ~$45/mo · Business ~$375/mo.
Julius AI pricing as reported by third-party teardowns as of June 2026, tier names and prices have varied across sources; check julius.ai/pricing for current figures. Kutta pricing is current.
Kutta is built past those walls: multi-million-row CSV and Parquet files stay interactive in the browser via Apache Arrow, the AI builds persistent dashboards instead of disposable chat replies, and your team can share and re-query the same living boards. It’s not a ChatGPT replacement for general work, it’s the dedicated tool for the data part.
Choose Kutta if…
- Your CSV is too large for ChatGPT, the ~50MB wall is exactly where Kutta starts
- You re-run the same analysis weekly and want a living dashboard, not a re-prompt ritual
- Stakeholders need to see the numbers, share a live board, not a transcript
- You need database or ad-platform connections, not just file uploads
Stick with ChatGPT if…
- Your files are small and your questions are one-offs
- You need general-purpose help, writing, code, research, alongside light data work
- You want arbitrary Python execution on your data within sandbox limits
- You already pay for Plus and haven’t yet hit the file-size or persistence walls
Pricing — Kutta: Free $0 · Pro $30/mo. ChatGPT: Free $0 · Plus $20/mo · Pro $200/mo.
ChatGPT pricing and limits as published by OpenAI as of June 2026; file-handling behavior based on OpenAI documentation and independent testing reports.
FAQ
Frequently asked questions
Define the funnel stages (signup → activation → key action → conversion), count users reaching each stage in a period, and compute stage-to-stage conversion. In Kutta, upload your usage export and ask for the funnel, the AI computes drop-off per stage and lets you drill into the users behind the biggest leak.
Yes, if your product writes usage data to a database, you already have the raw material. Kutta computes funnels, retention cohorts, and adoption metrics directly from database exports, skipping the SDK install, event-schema project, and event-volume pricing those platforms require. Real-time event streaming is the one job that still needs an instrumented tool.
Use a tool that translates plain English into the underlying queries. In Kutta, ask "weekly retention cohorts for users who signed up in Q1, by plan tier", the AI runs the computation against your uploaded data and renders the cohort curves, no SQL written, with drill-down to verify the underlying records.
Product analytics studies how users behave inside your product, funnels, retention, feature adoption. Business intelligence aggregates business performance, revenue, pipeline, operations, across the company. Kutta spans both because the mechanics are the same: connect data, let AI build the dashboard, query it in plain English.
Not for most questions. Funnels, retention, adoption, and churn analysis can all be computed from data your product already stores, signups, logins, actions, subscription states. Event tracking earns its complexity for real-time behavioral streams and session-level replay; for everything else, exports plus AI analysis gets you answers this week.
Yes. Export usage data from your database or admin panel, upload the CSV to Kutta, and describe the analysis, "monthly active users by plan," "feature adoption since the March release," "retention by signup cohort." The AI builds the dashboard and you refine it conversationally. Parquet and Excel work the same way.
Explore more
Kutta works across your whole team
Your data story starts now
No setup. No SQL. No waiting.
No credit card required