Amplitude AI Agents vs Mitzu: Agents in a Product Analytics Suite vs One Governed Analytics Platform on Your Warehouse

Use this comparison if you are evaluating Amplitude AI agents and want to see what changes when the agent works on governed metrics in your warehouse: your dbt models, billing tables and CRM data, not only the events you sent to a product analytics vendor.

In February 2026 Amplitude introduced Amplitude AI Agents: a Global Agent for natural-language analytics plus a set of specialized agents around dashboard monitoring, session replay, web experimentation and feedback. The incumbent product analytics platforms now have agents of their own, so the useful question for Snowflake, BigQuery and ClickHouse teams is no longer do they have AI, but what data does the AI see, and whose definitions does it use. In Amplitude's standard setup the agents work on Amplitude's own behavioral data store; Amplitude also offers a warehouse-native mode on Snowflake. Mitzu is one analytics platform on your warehouse for BI, product analytics and experiment analysis, governed by the data team, with an AI agent whose SQL comes from a deterministic engine.

What is Amplitude AI Agents?

Amplitude AI Agents is the umbrella name for the agentic surface on top of Amplitude's analytics platform, announced in February 2026. The flagship is the Global Agent, a chat surface that analyzes data, builds dashboards, investigates root causes and recommends actions across funnels, experiments, segments and customer journeys. Around it sit four specialized agents focused on particular workflows.

• Global Agent: natural-language Q&A across Amplitude data; explains what changed and why, builds dashboards, segments users, maintains the event taxonomy.
• Dashboard Monitoring Agent: watches metrics, detects shifts within hours, and delivers investigations to Slack or email.
• Session Replay Agent: reviews user sessions, identifies friction, quantifies revenue impact, and proposes fixes.
• Web Experimentation Agent: designs and launches experiments, analyzes results, and requests human approval before rollout.
• AI Feedback Agent: converts unstructured survey and support feedback into themes linked back to behavioral cohorts.

The agents draw on what Amplitude itself stores: behavioral events, session replays, experiments, guides, surveys, and the event taxonomy maintained inside the product. The architecture page describes a "rich semantic model" built from "event taxonomies, charts, dashboards, experiments, flags, and governance metadata," and notes the underlying model layer uses systems from OpenAI, Google and Anthropic plus homegrown backends for orchestration, memory and session storage. Amplitude also exposes a Model Context Protocol server so external agents (Claude, Cursor, ChatGPT and others) can read Amplitude data through an MCP-compatible client.

In Amplitude's standard setup, events are captured by Amplitude SDKs (or forwarded by a CDP), stored in Amplitude's columnar backend, and analyzed there; the agents are another layer of analysis over that data. Amplitude also sells a warehouse-native mode on Snowflake, where Amplitude analyzes data that stays in the customer's Snowflake account. Which agents and features are available in that mode is a question to check with Amplitude for your plan, so this comparison treats the standard setup as the default and calls out warehouse-native mode where it changes the answer.

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. Like Amplitude, it runs funnels, retention, journeys and dashboards with an agent on top. Unlike Amplitude, it reads business and event data in place on whichever warehouse you run, and the same governed definitions power KPI reporting, product analytics and A/B test analysis.

The data team defines metrics such as net revenue, orders, average order value and margin once, on its own fact and dimension tables, and Mitzu can draft those definitions from the warehouse and dbt models for the data team to review. A Configuration Agent scans the warehouse for event and dimension tables, identifiers and property values. Every KPI dashboard, agent answer and scheduled agent reuses the same definitions, and each person sees only the data their Mitzu role allows. People reach Mitzu in the app, by mentioning @mitzu in Slack, through a remote MCP server from Claude, ChatGPT, Cursor, GitHub Copilot and other MCP clients, and through emails from scheduled monitoring agents that review dashboards and KPIs. Supported warehouses are Snowflake, BigQuery, Databricks, Redshift, ClickHouse, PostgreSQL, Athena, Trino, Firebolt and Microsoft Fabric. See Warehouse Native vs First-Generation Product Analytics for the broader architectural picture.

The trust differentiator: Mitzu's agent does not write SQL. It assembles structured analysis specifications (a metric and its breakdown, funnel steps with a conversion window, retention cohorts and return events, segmentation filters with sampled property values, journey definitions, an experiment and its metrics) and a deterministic query engine turns those specifications into SQL. The same specification produces the same SQL every time, and the SQL behind every answer is one click away.

Amplitude AI Agents vs Mitzu: side-by-side

Strengths at a glance

SQL examples: where the warehouse shows up

Both tools handle a classic funnel question cleanly. The difference becomes visible the moment a question needs data that lives outside the events table. Take a question marketing leaders ask constantly: "7-day signup-to-activation conversion by acquisition channel, weighted by 30-day revenue from billing." Amplitude in its standard setup can answer the first half, the funnel by channel. The revenue join is where data ingested into Amplitude ends and the warehouse begins.

Amplitude: the funnel part

Amplitude's Global Agent does not expose user-facing SQL; methodology is encoded in Amplitude's internal funnel engine. A user asks the question in chat; the agent returns a funnel chart with the 7-day window enforced and channel as the breakdown. Conceptually, the underlying operation is roughly equivalent to:

-- Conceptual shape of what Amplitude's engine computes for the funnel half
-- of the question, scoped to the events that have been ingested into Amplitude.
-- Channel attribution comes from Amplitude's own ingestion / taxonomy.
WITH signups AS (
  SELECT amp_user_id,
         min(event_time)               AS signup_at,
         any(initial_utm_source)       AS channel
  FROM amplitude_events
  WHERE event_type = 'signup'
    AND event_time >= now() - INTERVAL 30 DAY
  GROUP BY amp_user_id
),
activations AS (
  SELECT s.amp_user_id,
         s.channel,
         min(e.event_time) AS activated_at
  FROM signups s
  JOIN amplitude_events e
    ON e.amp_user_id = s.amp_user_id
   AND e.event_type  = 'activated'
   AND e.event_time  >  s.signup_at
   AND e.event_time  <= s.signup_at + INTERVAL 7 DAY
  GROUP BY s.amp_user_id, s.channel
)
SELECT s.channel,
       count(DISTINCT s.amp_user_id)                                       AS signups,
       count(DISTINCT a.amp_user_id)                                       AS activated_in_7d,
       round(count(DISTINCT a.amp_user_id)
             / nullIf(count(DISTINCT s.amp_user_id), 0) * 100, 1)           AS conv_pct
FROM signups s
LEFT JOIN activations a USING (amp_user_id)
GROUP BY s.channel
ORDER BY signups DESC;

The methodology is correct because Amplitude's engine enforces the window and the breakdown. The boundary is the data: amplitude_events contains only what has been forwarded into Amplitude. The 30-day revenue join, which lives in the billing system or in a Stripe-modeled dbt table, is out of scope unless that revenue stream has been pushed into Amplitude as events, or the team runs Amplitude's warehouse-native mode on Snowflake with billing modeled there.

Mitzu: funnel + warehouse revenue join

Mitzu assembles a funnel specification, roughly { first_event: "signup", subsequent_events: ["activated"], conversion_window: "7d", breakdown: "channel", date_range: "last_30_days" }, and the deterministic engine emits SQL that joins the funnel output to the billing table modeled in the warehouse:

-- Engine output: 7-day signup-to-activation funnel by channel,
-- joined to a warehouse-modelled billing table for 30-day revenue.
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  = 'activated'
   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
),
revenue_30d AS (
  SELECT b.user_id,
         sum(b.amount_usd) AS revenue_usd
  FROM dim_billing b
  WHERE b.charged_at >= now() - INTERVAL 30 DAY
  GROUP BY b.user_id
)
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,
       coalesce(sum(r.revenue_usd), 0)                     AS revenue_30d_usd
FROM step_1  s1
LEFT JOIN step_2      s2 USING (user_id)
LEFT JOIN revenue_30d r  USING (user_id)
GROUP BY channel
ORDER BY revenue_30d_usd DESC;

Conversion methodology is identical to Amplitude (strict window, distinct users, channel from the signup row), but the join to dim_billing happens in the same SQL pass because Mitzu reads the warehouse directly. The billing table never leaves the warehouse and the agent never had to author this join: the deterministic engine produced it from the analysis specification and the semantic layer's knowledge of which entities are joinable. Because revenue is a governed metric, it is the same revenue the KPI dashboard shows.

Retention: cohort definition, same shape

Retention is another methodology primitive both tools handle natively. Mitzu's engine produces SQL like this for the question "week-2 retention for users acquired via paid channels, by signup week, for the last 12 weeks":

-- Engine output for a weekly retention chart, cohorted by signup week,
-- with a 'paid' channel filter resolved against sampled property values.
WITH cohorts AS (
  SELECT user_id,
         min(event_time)                              AS signup_at,
         date_trunc('week', min(event_time))           AS cohort_week,
         any(properties['channel'])                   AS channel
  FROM events
  WHERE event_name = 'signup'
    AND event_time >= now() - INTERVAL 12 WEEK
  GROUP BY user_id
),
filtered AS (
  SELECT *
  FROM cohorts
  WHERE channel IN ('paid_search', 'paid_social', 'paid_display')
),
returns AS (
  SELECT f.cohort_week,
         count(DISTINCT f.user_id)              AS cohort_size,
         count(DISTINCT CASE
           WHEN e.event_time >= f.signup_at + INTERVAL 14 DAY
            AND e.event_time <  f.signup_at + INTERVAL 21 DAY
           THEN f.user_id END)                  AS week_2_returners
  FROM filtered f
  LEFT JOIN events e
    ON e.user_id = f.user_id
   AND e.event_time > f.signup_at
  GROUP BY f.cohort_week
)
SELECT cohort_week,
       cohort_size,
       week_2_returners,
       round(week_2_returners / nullIf(cohort_size, 0) * 100, 1) AS retention_pct
FROM returns
ORDER BY cohort_week;

The filter values paid_search, paid_social and paid_display come from the semantic layer's sampled property values, not from the agent guessing what your channel labels look like. The week-2 window is defined as the 7-day band starting 14 days after signup, not the loose "sometime in week 2" interpretation an LLM might pick. Same specification, same SQL, same answer.

Advantages and trade-offs

Amplitude AI Agents

Mitzu

Where the agent meets the user?

The two tools also meet users in different places. Amplitude's Global Agent lives inside Amplitude's web app: questions are asked next to the charts, dashboards and experiments the team has built up over time, with specialized agents posting investigations and digests to Slack and email. Amplitude's MCP extends that surface outward, letting Claude, Cursor, ChatGPT or any MCP-compatible client read Amplitude data on demand.

Mitzu keeps the work inside one workspace and brings the agent to the tools people already use. Inside the app, KPI dashboards, point-and-click insights, experiment results and the analytics agent share the same semantic layer, and any chart the agent builds can go straight onto a shared dashboard. In Slack, @mitzu answers in the thread with the thread as context, which is how product managers, marketers and leadership often use it without opening the app. The remote MCP server gives Claude, ChatGPT, Cursor and GitHub Copilot the same governed answers, and monitoring agents review dashboards on a schedule and email the team when something moves. See Agentic Analytics Platforms Compared for a broader survey of agent surfaces across vendors.

When to choose Amplitude, Mitzu, or both?

The right answer often depends on where the rest of the stack sits and who owns the definitions. Amplitude is a product analytics and digital experience suite with agents on top. Mitzu is one governed analytics platform on the warehouse, covering KPI reporting, product analytics and experiment analysis.

• Choose Amplitude AI Agents when the team is already deep in Amplitude (session replay, web experimentation, in-app guides and the existing event taxonomy are the daily workflow) and the questions stay within data the platform already holds.
• Choose Mitzu when the warehouse is the system of record and the data team wants one place where revenue, orders and margin, funnels and retention, and A/B test results all use the definitions it governs; when questions need billing, CRM, support or NPS data; when data egress isn't acceptable; or when per-event pricing has become the wrong shape for the team's growth.
• Run both when Amplitude is the activation layer (session replay, experimentation, in-app guides) and the warehouse is the system of record for everything else. Amplitude handles the activation surface; Mitzu handles KPI reporting, the diagnostic questions that need warehouse joins, and experiment readouts on the business's own metrics.

FAQ

Does Mitzu work alongside Amplitude?

Yes. Amplitude owns the activation surface (session replay, experimentation, in-app guides). Mitzu runs on the warehouse and handles KPI reporting, product analytics and experiment analysis that need warehouse-modeled context. Many teams already export Amplitude events to their warehouse via the Amplitude → S3 / BigQuery / Snowflake connectors; Mitzu can read those exported tables directly, alongside any other event sources. See Amplitude alternatives (2026) for adjacent options if a full migration is the goal.

Does Amplitude's agent query the warehouse?

It depends on how Amplitude is deployed. In Amplitude's standard setup, the Global Agent and specialized agents operate on Amplitude's own behavioral data store (events, session replays, experiments, guides, surveys and governance metadata), so data has to be ingested into Amplitude via Amplitude SDKs or a CDP before it is in scope. Amplitude also offers a warehouse-native mode on Snowflake, where Amplitude analyzes data in the customer's Snowflake account. Check with Amplitude which agents and features are available in that mode for your plan; it is Snowflake-only, so it does not cover BigQuery, Databricks, Redshift or ClickHouse.

Does Mitzu's agent write SQL?

No. Mitzu's analytics agent assembles typed analysis specifications (a metric and its breakdown, funnel steps with a conversion window, retention cohorts and return events, segmentation filters with sampled property values, journey trees, an experiment and its metrics) and a deterministic query engine produces the SQL. The same specification produces the same SQL every time. The SQL is shown to the analyst as a verification artifact, not as the agent's authored work.

How does pricing compare?

Amplitude's pricing scales with MTUs and event volume, with agent capabilities tied to the platform tier. Mitzu plans include a number of Editor seats and unlimited events, so there is no per-event pricing; the customer's warehouse compute cost is independent and controlled by the customer. See the Mitzu pricing page for current details.

Can Mitzu replace Amplitude?

For product, growth and marketing analytics (funnels, retention, segmentation, journeys, cohorts, deep dives and root-cause investigations) yes, with the prerequisite that event data is in the warehouse, and Mitzu adds KPI dashboards on the data team's governed business metrics. For A/B tests, Mitzu analyzes results from exposure events in the warehouse; the tool that assigns users keeps doing that. For session replay, in-app guides and running web experiments, Mitzu is not the equivalent product; those surfaces are typically replaced by separate tools or kept in Amplitude during a hybrid period.

Related reading

• Warehouse Native vs First-Generation Product Analytics
• Amplitude alternatives (2026)
• ClickHouse AI vs Mitzu
• Agentic Analytics Platforms Compared
• ChatGPT vs AI Analytics Agent
• Mitzu BI and Warehouse Metrics
• Mitzu Experiment Analysis
• Mitzu Monitoring Agents
• Mitzu Semantic Layer
• Mitzu Product Analytics

References

• Amplitude: AI Agents product page
• Amplitude press: Introduces Agentic AI Analytics (Feb 2026)
• Amplitude blog: Introducing Amplitude AI Agents
• Mitzu documentation (docs.mitzu.io)