Omni vs Mitzu: Governed BI, AI Agents and Product Analytics on the Warehouse

Use this comparison if you are choosing a governed BI and AI analytics platform for your warehouse and want a clear read on where Omni and Mitzu compete head to head, where they differ, and which one fits the questions your team asks most often.

Omni recently raised a Series C at a $1.5B valuation and positions itself as "the AI analytics platform for the enterprise." That puts it in front of the same data teams evaluating Mitzu. Both tools are warehouse-native, both let the data team define metrics once and build dashboards on them, both ground their AI in a semantic model, and both surface reviewable SQL. On governed KPI reporting they are direct competitors. They differ in what sits next to the dashboards. Omni adds spreadsheet-style workbooks and embedded analytics. Mitzu adds product analytics (funnels, retention, journeys), experiment analysis and an agent that explains why a number moved, all on the same definitions.

What is Omni?

Omni is an AI analytics platform built by ex-Looker co-founders. The public product page positions it as a unified surface for self-service BI, embedded analytics, and AI-driven analysis, all anchored by a governed semantic model that Omni calls "the semantic foundation for native and external AI agents." Data stays in the customer's warehouse (Snowflake, BigQuery, Databricks, Redshift, and similar); Omni reads it in place.

Omni's semantic layer follows the LookML lineage of its founders: metrics, dimensions, joins, relationships, business logic, row-level filters, and access controls, declared in code and version-controlled in Git. The AI is positioned as another consumer of that model. Per Omni's own materials, the agent has a coordinator that plans actions, selects tools, executes queries, evaluates results, and decides what to do next. The AI generates semantic queries against the model rather than raw SQL, and Omni then compiles those semantic queries to SQL.

• Workbooks: a hybrid surface combining point-and-click exploration, SQL, and a spreadsheet-style interface. The flagship analyst experience.
• Dashboards and reports: the classic BI consumption layer, built from the semantic model.
• AI chat: natural-language interface with maintained conversation context; can build calculations, filter, summarize, explain charts, compare time periods.
• Embedded analytics: Omni dashboards and workbooks embedded inside customer-facing products with multi-tenant permissions.
• MCP server: exposes governed Omni queries to external AI tools (Claude, ChatGPT, Cursor, VS Code) so they inherit the same metrics and permissions.
• Git version control: the semantic model lives in Git; pull requests, branches, and review apply to metric definitions.

Omni's strength is BI breadth on a governed semantic foundation. It handles cross-domain analytical questions (revenue, marketing performance, operational reporting, product KPIs, finance) as long as someone has modeled them into the semantic layer.

What is Mitzu?

Mitzu is an analytics platform on your data warehouse for BI, product analytics and experiment analysis, where the data team governs the metrics and everyone uses them through dashboards, an AI agent, Slack and their own AI assistants. The data team defines metrics such as net revenue, orders, average order value and margin once on fact and dimension tables. KPI dashboards, funnels, retention charts and A/B test results all use those definitions, so a revenue drop can be followed to the funnel step and the users behind it in one analysis.

Mitzu meets users in the app, where dashboards and the Analytics Agent live; in Slack, through @mitzu in any channel; through a remote MCP server that any MCP-compatible assistant (Claude, ChatGPT, Cursor, GitHub Copilot) can call; and by email from scheduled agents that review a dashboard and report what moved. Setup starts with a Configuration Agent that scans the warehouse, recognizes common event schemas (Segment, Snowplow, Firebase, GA4, custom), maps user and group identifiers, and picks up dimension tables. Mitzu drafts metric definitions from the warehouse and dbt models, and the data team reviews and owns them. Each person sees only the data their role in Mitzu allows, on every surface.

The trust differentiator: Mitzu's agent does not write SQL, and does not write semantic queries either. It assembles structured analysis specifications: funnel steps with a conversion window, retention cohorts and return events, segmentation filters with sampled property values, journey definitions with starting events and depth. A deterministic query engine, the same code that has been generating SQL for Mitzu's UI for years, turns those specifications into SQL. The same specification produces the same SQL every time. Methodology errors that LLMs reliably make (a funnel without a conversion window, a retention chart that double-counts, a cohort defined off the wrong join) are guard-railed by the engine, not by prompt engineering.

Mitzu's semantic layer covers both sides of the platform: metrics on fact and dimension tables for KPI reporting, and the event side for product analytics, with events, event properties, entities (users, sessions, accounts, teams), dimension properties on those entities, and sampled property values. Filters are suggested from real values in the warehouse, not invented by the AI. Saved insights, dashboards, and named cohorts feed back into the semantic layer as additional context for future questions.

Omni vs Mitzu: side-by-side

SQL examples: the same question, two architectures

Dashboards are where the two tools meet. The architectures differ one question later, when a KPI moves and someone asks about the funnel behind it: "What is our 7-day signup-to-activation conversion rate, broken down by acquisition channel, for the last 30 days?"

Omni: SQL compiled from an AI-generated semantic query

In Omni, the AI generates a semantic query against the semantic model. That assumes an analyst has previously modeled signup and activation as events, defined acquisition_channel as a dimension on the user entity, and authored a conversion-window measure or the join logic to support one. Omni then compiles the semantic query to SQL. Plausible output:

-- Plausible compiled SQL from an Omni semantic query.
-- The exact shape depends on how the semantic model was authored
-- (was a 7-day window measure defined? is channel on the user
-- or on the signup event? is "activation" a single event or a model?).
WITH signups AS (
  SELECT u.user_id,
         u.acquisition_channel,
         min(e.event_at) AS signup_at
  FROM events e
  JOIN users  u USING (user_id)
  WHERE e.event_name = 'signup'
    AND e.event_at >= current_date - INTERVAL '30 days'
  GROUP BY 1, 2
),
activations AS (
  SELECT user_id, min(event_at) AS activated_at
  FROM events
  WHERE event_name = 'activation'
  GROUP BY user_id
)
SELECT s.acquisition_channel,
       count(DISTINCT s.user_id)                                       AS signups,
       count(DISTINCT CASE
               WHEN a.activated_at BETWEEN s.signup_at
                                       AND s.signup_at + INTERVAL '7 days'
               THEN s.user_id END)                                     AS activated_in_7d,
       round(100.0 * count(DISTINCT CASE
               WHEN a.activated_at BETWEEN s.signup_at
                                       AND s.signup_at + INTERVAL '7 days'
               THEN s.user_id END)
             / nullif(count(DISTINCT s.user_id), 0), 1)                AS conv_pct
FROM signups s
LEFT JOIN activations a USING (user_id)
GROUP BY 1
ORDER BY signups DESC;

The semantic model carries real constraints, so it is harder for the AI to invent a channel value or join the wrong table. The methodology question (what counts as a 7-day window, whether activation events before the signup count, whether a user with multiple signups is double-counted) is still resolved by the model definitions and by what the AI chose to ask for. If a strict funnel-window measure was not authored into the model, the AI has to compose one inside the semantic query; how reliably it does that across phrasings is an empirical question.

Mitzu: SQL from a deterministic engine

The Mitzu agent does not write the SQL and does not author a semantic query. It assembles a funnel specification, roughly { first_event: "signup", subsequent_events: ["activation"], conversion_window: "7d", breakdown: "acquisition_channel", date_range: "last_30_days" }, and the deterministic engine emits the same SQL every time:

-- Engine output for a 2-step funnel with a 7-day conversion window,
-- broken down by acquisition channel, for the last 30 days.
-- Same spec → same SQL.
WITH step_1 AS (
  SELECT user_id,
         min(event_time)             AS step_1_at,
         any(properties['channel'])  AS channel
  FROM events
  WHERE event_name = 'signup'
    AND event_time >= now() - INTERVAL 30 DAY
    AND event_time <  now()
  GROUP BY user_id
),
step_2 AS (
  SELECT s1.user_id,
         s1.channel,
         min(e.event_time) AS step_2_at
  FROM step_1 s1
  INNER JOIN events e
    ON e.user_id = s1.user_id
   AND e.event_name = 'activation'
   AND e.event_time >  s1.step_1_at
   AND e.event_time <= s1.step_1_at + INTERVAL 7 DAY
  GROUP BY s1.user_id, s1.channel
)
SELECT s1.channel                                AS channel,
       count(DISTINCT s1.user_id)                AS step_1_users,
       count(DISTINCT s2.user_id)                AS step_2_users,
       round(count(DISTINCT s2.user_id)
             / nullIf(count(DISTINCT s1.user_id), 0) * 100, 1) AS conv_pct
FROM step_1 s1
LEFT JOIN step_2 s2 USING (user_id)
GROUP BY channel
ORDER BY step_1_users DESC;

The conversion window is enforced strictly (activation must come after the signup and within 7 days). Distinct users prevent double-counting. The channel value comes from the signup row, so attribution is consistent. The engine has been generating this shape of SQL in production for years; the agent's job is to assemble the specification, not to author the query.

The SQL is shown to the analyst as a verification artifact, not the agent's authored work. However the question is phrased ("signup to activated within a week, by channel," "7-day activation funnel split by source"), the same specification compiles to the same SQL.

Other questions, side by side

Once a metric is defined, both tools put it on a dashboard. The differences show up in the questions below: journey and diagnostic questions that Mitzu's engine handles as first-class primitives on the same governed metrics, and embedded analytics, where Omni leads. In Omni, the product analytics questions are charts an analyst assembles from metrics, dimensions, and the relationships the semantic model exposes.

UI differences in one line each

• Omni workbook: a hybrid grid that combines a spreadsheet, a SQL editor, and a point-and-click query builder; the analyst's primary workspace.
• Omni dashboard: composed tiles backed by the semantic model; consumers filter and drill; analysts edit upstream metrics in Git.
• Omni AI chat: a side panel or full chat surface; conversation context maintained; calculations, filters, and charts created from prompts.
• Mitzu dashboards: KPI dashboards on governed metrics, built by clicking or by asking the agent; a scheduled agent can review one and email the team when something moves.
• Mitzu Explore: direct point-and-click funnel, retention, segmentation, and journey builders; outputs are charts plus the SQL behind them.
• Mitzu Analytics Agent: a chat surface where the agent assembles funnel, retention or segmentation specifications, runs them through the deterministic engine, and replies with chart, summary and reviewable SQL.
• Mitzu in Slack: @mitzu in any channel; the agent reads thread context, runs the same engine, and posts answers as cards.

Strengths and trade-offs

Omni

Mitzu

Capability scorecard

Where each tool stands on governed BI, the AI, and the product analytics Mitzu adds on the same definitions.

When to choose Omni, Mitzu, or both?

On governed KPI reporting these are substitutes: you pick one place for the data team to define metrics and build dashboards. The choice comes down to what your team needs next to the dashboards.

• Choose Omni when embedded analytics inside a customer-facing product, spreadsheet-style workbooks or a Git-versioned semantic model are central, or when most of the workload is cross-domain reporting that an analytics engineering team already models in code.
• Choose Mitzu when you want governed KPI dashboards and, on the same definitions, product analytics, A/B test analysis and an agent that explains why a number moved: why did week-2 retention drop, did the new pricing page move trial-to-paid, which onboarding step has the highest drop-off. The methodology is guard-railed by the engine, and the same answers reach Slack, MCP clients and scheduled agents that email the team.
• Run both when embedded analytics or pixel-perfect financial reporting matter alongside product and experiment analysis. Omni owns those; Mitzu owns KPI dashboards, product analytics and experiment analysis. Both stay warehouse-native and surface reviewable SQL, but metrics used in both tools are then defined twice, so decide which tool owns which definitions.

FAQ

Is Omni a product analytics tool?

Omni is an AI analytics platform with strong BI, embedded, and reporting capabilities on a governed semantic layer. Funnels, retention, journeys, and cohorts are not native primitives in the way they are in a dedicated product analytics tool; they are charts the analyst assembles from the semantic model. Teams who need that methodology pair Omni with a product analytics tool, or use a platform that covers BI and product analytics on one set of definitions.

Does Mitzu replace Omni for BI and dashboards?

For KPI dashboards and self-service reporting on governed metrics, many teams use Mitzu instead of a BI tool, and get product analytics, experiment analysis and the agent on the same definitions. Omni is the better fit for embedded analytics in customer-facing products and for spreadsheet-style workbooks, and teams with heavy pixel-perfect or financial reporting often keep a BI tool for that.

Both tools have a semantic layer. What is the difference?

Shape and authoring. Omni's semantic layer is BI-shaped (metrics, dimensions, joins, relationships, business logic) and hand-authored in code, Git-versioned in LookML lineage. Mitzu's semantic layer holds metrics on fact and dimension tables for KPI reporting, plus the event side that product analytics needs: events, event properties, entities, dimension properties on those entities, and sampled property values. Mitzu drafts it from the warehouse and dbt models, and the data team reviews and owns it.

The sampled values are what keep user-facing filters on real values.

Does Omni's AI write SQL? Does Mitzu's agent write SQL?

Omni's AI generates a semantic query against the semantic model; Omni compiles it to SQL. The AI is still composing the query, just at a higher level than raw schema. Mitzu's agent does not compose a query at any level. It assembles a typed analysis specification (funnel, retention, segmentation, journey, cohort) and a deterministic engine compiles the specification into SQL.

The same specification always produces the same SQL.

Can I use both together?

Yes. Both keep data in the warehouse, so there is no duplication of data. The usual split puts embedded analytics or pixel-perfect reporting in Omni, and KPI dashboards, product analytics and experiment analysis in Mitzu. Metrics used in both then exist in both semantic layers.

If your definitions already live in dbt, Mitzu drafts its metrics from those models, which keeps the two closer.

Where does the data live in either tool?

In your warehouse. Both architectures are warehouse-native. Neither moves data into a vendor silo. Compliance, data residency, and cost control all stay on your side of the line.

Related reading

• Mitzu BI and warehouse metrics
• ClickHouse AI vs Mitzu
• Agentic Analytics Platforms Compared
• BI vs Product Analytics
• Warehouse-Native vs First-Generation Product Analytics
• The Semantic Layer for Agentic Analytics
• AI Analytics, Hallucinations, and SQL Transparency
• Mitzu Product Analytics

References

• Omni Analytics: product home
• Omni Analytics: AI you can trust
• Omni: AI-Powered BI Tools (2026)
• Omni: Series C announcement
• Mitzu documentation (docs.mitzu.io)