Mitzu

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.

Mitzu is business intelligence software teams evaluate for business intelligence. Use this page to review pricing, integration signals, and the best alternatives before you commit.

Freemium Enterprise 70/100
#131 in Business Intelligence (131 tools)
Added 4 months ago
Data reviewed Jul 16, 2026

Profile facts come from the vendor source. AiMatch labels unknown pricing or API details instead of estimating them.

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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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Key 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

Free Tier Available

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

No SQL required for business users — natural language querying with analyst-verifiable SQL provided.
Fast onboarding and time-to-insight (claims of connection and insight in minutes).
Warehouse-native architecture avoids data copying and per-event pricing.
Deterministic queries and full SQL visibility reduce hallucinations and increase trust.
Built-in product-analytics methodology ensures consistent, repeatable diagnosis.

Limitations

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Frequently Asked Questions

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Getting Started

  1. 1 Step 1: Connect your data warehouse (supported: Snowflake, BigQuery, Databricks, Redshift, ClickHouse).
  2. 2 Step 2: Let the agent scan your warehouse and automatically create a product-analytics semantic layer (no YAML required).
  3. 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

Available: No

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