Mitzu
Mitzu is an agentic product-analytics platform that connects to your data warehouse to run diagnostic analyses, monitor KPIs, and answer deep product questions in natural language with deterministic SQL and full SQL visibility.
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Quick Overview
Best for: Business Intelligence
What it does
Business Intelligence software for decision-makers comparing workflow fit and alternatives.
Best fit
Business Intelligence
Pricing snapshot
Freemium from Free (no credit card required)
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Mitzu
Mitzu is an agentic analytics platform that connects directly to your data warehouse to diagnose why metrics change, run impact analyses, validate hypotheses, and monitor anomalies. It combines a configuration agent that builds a semantic layer with a deterministic SQL engine so answers include the exact SQL used and avoid LLM-generated query hallucinations. The product is aimed at product, growth, marketing, and data teams who need fast, trusted product analytics across funnels, cohorts, retention, and journeys without copying event data into a third-party system.
Mitzu is an agentic product-analytics platform that connects to your data warehouse to run diagnostic analyses, monitor KPIs, and answer deep product questions in natural language with deterministic SQL and full SQL visibility.
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Claim this listing for $29Key Features
Impact Analysis
Compares cohorts and correlations to determine whether a release, campaign, or change caused observable metric changes.
Deep Dives
Runs thorough investigations across funnels, cohort breakdowns, and segments, synthesising findings from multiple queries.
Hypothesis Validation
Tests product hypotheses against real cohort behavior (for example onboarding speed vs retention) and reports whether they hold.
Monitoring & Anomaly Detection
Continuously monitors the warehouse to surface metric shifts, retention anomalies, and activation drops via alerts (email or Slack).
Automatic Semantic Layer
A configuration agent scans the warehouse, maps event tables, and recognises Segment, Snowplow, Firebase and custom schemas without YAML.
Deterministic SQL Engine
Generates repeatable, deterministic SQL from a specification so the same query logic and answers are reproducible and verifiable.
Full SQL Visibility
Every answer includes the SQL that produced it so analysts can verify logic before sharing — avoiding black-box AI queries.
Funnel, Retention, Segmentation, Journey Tools
Built-in product-analytics toolkit: funnels, retention cohorts, segmentation, journey mapping (Sankey), dynamic user cohorts, and B2B account analytics.
Warehouse-native Integrations
Native connections to BigQuery, Snowflake, Databricks, Redshift, ClickHouse, Firebase, GA4 and custom schemas; no event copying required.
Collaboration & Integrations
Accessible in the Mitzu app, via Slack (@mitzu) and as a trusted analytics backend to external AI tools using the MCP server.
Pricing
Get Started for Free — no credit card required (page advertises free start but no further pricing tiers are listed on the page).
Get Started for Free
Free (no credit card required)- Onboard quickly and connect your warehouse
- Try agent-driven analyses and monitoring
Use Cases
Diagnose metric movements
Ask why a metric moved and Mitzu runs a diagnostic across funnels and cohorts to return a grounded finding from your warehouse data.
Assess releases and campaigns
Run impact analyses to determine whether product releases or marketing campaigns materially changed conversion or retention.
Continuous monitoring
Automatically detect and alert teams to DAU spikes, retention drops, and activation issues before they escalate.
Hypothesis testing
Validate product or growth hypotheses against real cohorts and report whether the hypothesis holds.
B2B account-level analytics
Track expansion, churn risk, and engagement at the company/account level for enterprise use cases.
Integrations
BigQuery / Snowflake / Databricks / Redshift / ClickHouse
Direct warehouse integrations for running analyses against native event, billing, CRM and support data.
Firebase / GA4 / Segment / Snowplow / Custom schemas
Recognises common event schemas and integrates without requiring a clean dbt project or YAML.
Slack
@mitzu can surface answers, insights and alerts directly in Slack channels.
AI tool backends (via MCP server)
Plugs Mitzu into Claude, Cursor, ChatGPT or custom agents as a trusted analytics backend.
Benefits
Limitations
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Frequently Asked Questions
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Getting Started
- 1 Step 1: Connect your data warehouse (supported: Snowflake, BigQuery, Databricks, Redshift, ClickHouse).
- 2 Step 2: Let the agent scan your warehouse and automatically create a product-analytics semantic layer (no YAML required).
- 3 Step 3: Ask questions in the Mitzu app, Slack (@mitzu), or via connected AI tools and review results with full SQL visibility.
Support
docs
Documentation and product resources available via the site's Resources/Docs section.
book a demo
Request a demo or speak with the Mitzu team via the 'Book a Demo' flow on the website.
community
Community resources are listed under Resources for additional help and discussion.
API
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