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Mitzu

Mitzu

Mitzu is an agentic product analytics platform that runs on a company's own data warehouse.

Operating headquarters
San Francisco, CA, US🇺🇸
Founded
2022
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Mitzu - Library

mitzu.io·Crawled Oct 8, 2026·

Topics

Publication type

Content origin
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Product

6 pages
  1. Agentic Product Analytics Platform/product-analytics

    An agentic product analytics platform that runs on your data warehouse. Ask for funnels, retention, and cohorts in plain language — no SQL, no data copy.

  2. AI Analytics Agent for Product & Marketing Teams/ai-analytics-agent

    Use an AI analytics agent to get trusted answers with verified SQL on your warehouse. Cut reporting backlog and speed up product and growth decisions.

  3. AI Analytics for BigQuery with Transparent SQL/ai-analytics-for-bigquery

    Run AI analytics for BigQuery with natural-language questions, verified SQL, and zero-copy execution. Mitzu helps teams answer product and growth questions faster on first-party data.

  4. AI Analytics for Snowflake, Warehouse-Native/ai-analytics-for-snowflake

    Run AI analytics for Snowflake directly on first-party data. Mitzu translates natural-language questions into verified SQL so product and GTM teams can move faster without losing governance.

Blog

6 pages
  1. 4 Data Infrastructure Models for Scalable Analytics/post/data-infrastructure-operations-ownership-who-actually-runs-your-data

    Learn how Cloud, Private, or BYOC deployments impact data operations and scale modern analytics with Mitzu.

  2. Blog: Agentic Product Analytics Insights/post

    Insights on agentic analytics, AI analyst assistants, and the future of data-driven teams — from the Mitzu team.

  3. Cruise Booking Platform Uses Mitzu for Revenue Attribution/post/case-study-how-clickncruise-uses-mitzu-for-revenue-attribution-and-customer-retention

    Learn how CLICKnCRUISE uses Mitzu for affordable product analytics to improve customer retention, optimize revenue attribution, and drive business growth.

  4. Data Mesh vs Data Fabric: Scalable Enterprise Data/post/data-mesh-vs-data-fabric

    Data Mesh vs Data Fabric comparison with features, pricing, data architecture, and which analytics stack each option fits best.

Pricing

3 pages
  1. Amplitude Pricing in 2026: What Each Plan Actually Costs/post/amplitude-pricing

    Amplitude pricing in 2026: Free covers 2M events a month, Plus starts at $0 and scales to 70M, and Growth and Enterprise are custom event-based deals.

  2. Mixpanel Pricing in 2026: What You Pay as Events Scale/post/mixpanel-pricing

    Mixpanel pricing in 2026: Free covers 1M events a month, Growth starts at $0 and scales to 20M, and Enterprise is a custom annual contract quoted by sales.

  3. Pricing & Plans – Free to Start, No Per-Event Fees/pricing

    Mitzu has three plans: Analyst, Team and Enterprise. Start free with a 14-day trial, no credit card. Every plan has unlimited events and no per-event pricing.

Comparison

6 pages
  1. Mitzu Comparison Pages: Analytics Alternatives/compare

    Compare Mitzu with leading analytics tools and choose the right warehouse-native, agentic analytics platform for your team.

  2. Mitzu vs Amplitude: Agentic Analytics Comparison/compare/amplitude

    Compare Mitzu vs Amplitude for trusted agentic analytics. See how semantic-layer grounding, reviewable SQL, and trusted data reduce hallucinated answers.

  3. Mitzu vs Google Analytics 4: Unsampled Product Analytics/compare/google-analytics

    The unsampled alternative to GA4. Why product teams prefer warehouse-native analytics for accuracy, unlimited retention, and B2B account tracking.

  4. Mitzu vs Looker Studio: Warehouse-Native Analytics Comparison/compare/looker-studio

    Compare Mitzu and Looker Studio to evaluate product analytics capabilities. Discover which analytics tool offers the best value for warehouse-native product and marketing analytics.

Team

6 pages
  1. Ambrus Pethes/writer/ambrus-pethes

    Growth at Mitzu. Expert in data engineering and product analytics.

  2. István Mészáros/writer/istvan-meszaros

    Co-founder and CEO of Mitzu. Passionate about product analytics and helping companies make data-driven decisions.

  3. Trusted Agentic Analytics for Engineering Teams/teams/engineering

    Trusted agentic analytics for modern engineering teams. Semantic-layer grounding, warehouse-native metric alignment, and reviewable SQL on trusted data.

  4. Trusted Agentic Product Analytics & Feature Tracking/teams/product

    Empower product managers with trusted agentic analytics. Use semantic-layer grounded metrics to measure feature adoption, retention, and journeys without SQL.

About

1 page
  1. Warehouse-native analytics/about

    Mitzu helps teams answer business questions on their own data warehouse—with an analytics agent, transparent SQL, and no copying data to third parties.

Contact

1 page
  1. Book a Demo/contact

    See what an AI analyst assistant looks like in your Slack. Book a 20-min demo and watch Mitzu answer your actual data questions live.

Homepage

1 page
  1. Homepage/

    Ask funnel, retention, and cohort questions in plain language. Mitzu auto-builds a governed semantic layer and answers with deterministic, reviewable SQL.

Privacy

2 pages
  1. Privacy Policy/privacy-policy

    Read Mitzu's privacy policy to understand how we collect, use, and protect data across our analytics products and website.

  2. Secure Warehouse-Native Agentic Analytics/privacy-security

    Mitzu provides 100% secure, warehouse-native analytics. Data never leaves your infrastructure. Explore our zero raw data exposure architecture.

Terms & Conditions

1 page
  1. Terms and Conditions/terms-and-conditions

    Review Mitzu's legal terms for using our warehouse-native analytics platform, website, and related services.

Coverage

2 items
  1. mitzu-iogithub.com/mitzu-io

    GitHubRepository owner

  2. 180: István Mészáros: Merging web and product analytics on top of the warehouse with a zero-copy architecturehumansofmartech.com/2025/07/29/180-istvan-meszaros-warehouse-native-analytics/

    István Mészáros builds a warehouse-native analytics layer that merges web and product analytics using a zero-copy architecture. It lets teams define metrics once and query them directly, avoiding syncs across five tools that may use inconsistent definitions of terms such as “active user.” Teams can review SQL together, clean up the logic, and move faster.

    Jul 2025 · Humans of MartechNews article