Omni vs Mitzu: Agentic Analytics vs Agentic Product Analytics on the Warehouse

Use this comparison if you are evaluating warehouse-native AI analytics platforms and want a clear read on where Omni and Mitzu actually overlap, where they diverge, and which one fits the questions your team asks most often.

Omni recently raised a Series C at a $1.5B valuation and is positioning aggressively as "the AI analytics platform for the enterprise." That puts it in front of teams who already use — or are evaluating — Mitzu for product analytics on the warehouse. Both tools are warehouse-native, both ground their AI in a semantic model, both surface reviewable SQL. They are not, however, the same shape of tool. Omni is BI-shaped: dashboards, reporting, spreadsheets, embedded analytics. Mitzu is product-analytics-shaped: funnels, retention, journeys, cohorts, segmentation, deep-dive investigations. The comparison is fair, and the right answer for many teams is "both, at different layers."

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. Critically, 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, summarise, 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 modelled them into the semantic layer.

What is Mitzu?

Mitzu is an agentic product analytics platform that runs on your data warehouse and answers behavioural questions through natural-language conversation, without writing SQL. The category is narrower than agentic BI on purpose: Mitzu is specialised for product, growth, and marketing behavioural questions on event data.

Mitzu meets users in three places: the in-app Analytics Agent, the Slack Agent in any public or private channel, and a remote MCP server that exposes Mitzu's capabilities to any MCP-compatible agent (Claude, Cursor, ChatGPT, custom). Setup is handled by a Configuration Agent that scans the warehouse, recognises common event schemas (Segment, Snowplow, Firebase, GA4, custom), maps user and group identifiers, picks up dimension tables, and builds the semantic layer automatically. Analysts review and adjust; nobody hand-writes YAML.

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 is event-centric and product-analytics-specialised: events, event properties, entities (users, sessions, accounts, teams), dimension properties on those entities, and — critically — 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

Take a typical product analytics question Mitzu is built for: "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 — assuming an analyst has previously modelled 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. No matter how 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 product analytics questions, side by side

The shape difference shows up across the methodology surface area Mitzu is built for. Each row is a question Mitzu's engine handles as a first-class primitive; in Omni each is a chart 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 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 / segmentation specifications, runs them through the deterministic engine, and replies with chart + summary + reviewable SQL.
• Mitzu Slack Agent — @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 the capabilities that matter most when product analytics and AI BI are both on the table.

When to choose Omni, Mitzu, or both?

These are different layers, not substitutes. Omni is an AI BI platform with a governed semantic foundation. Mitzu is an agentic product analytics platform with an auto-built, event-centric semantic layer and a deterministic query engine. The right choice depends on which questions dominate your team's analytics workload.

• Choose Omni when the workload is BI-shaped — dashboards across finance, sales, marketing, and product; embedded analytics inside a customer-facing product; spreadsheet-style modelling on top of warehouse data; a single governed semantic model that powers everything.
• Choose Mitzu when product, growth, or marketing teams need to ask diagnostic behavioural questions — why did week-2 retention drop, did the new pricing page move trial-to-paid, which onboarding step has the highest drop-off — and you want methodology guard-rails the LLM cannot break, plus a Slack agent that brings answers to where the team already works.
• Run both when BI and product analytics are both first-class concerns. Let Omni own the BI surface and embedded analytics; let Mitzu own the product analytics methodology layer. Both stay warehouse-native, both surface reviewable SQL, and there is no data duplication — the warehouse is the single source of truth.

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 first-class product analytics methodology typically pair Omni with a product analytics layer or use a dedicated product analytics platform.

Does Mitzu replace Omni for BI and dashboards?

No. Mitzu is built for product analytics — behavioural questions on event data, diagnostic deep-dives, impact analysis. It is not a BI tool and is not pitched as one. Dashboards and saved insights exist in Mitzu, but cross-domain BI breadth, embedded analytics, and spreadsheet-style modelling are not the focus.

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. It expresses what a BI tool needs to know. Mitzu's semantic layer is product-analytics-shaped — events, event properties, entities, dimension properties on those entities, and sampled property values — and auto-built by a Configuration Agent that scans the warehouse.

It expresses what a product analytics agent needs to know, including the real values that show up in user-facing filters.

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; there is no duplication. A common pattern: Omni handles BI, reporting, and embedded analytics on top of dbt-modelled tables; Mitzu handles product analytics on the raw event tables and feeds back into the same warehouse for downstream modelling. Each tool owns the questions it is best at.

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

• ClickHouse AI vs Mitzu: Agentic SQL vs Agentic Product Analytics
• 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)