Mixpanel vs. Statsig
Mixpanel vs. Statsig
OpenAI has handed Statsig over to Amplitude. But the team that built Statsig, the team you bought from and worked with, is gone. If you're deciding what comes next, the question isn't whether to move. It's whether you do it on your timeline or Amplitude's.
OpenAI has handed Statsig over to Amplitude. But the team that built Statsig, the team you bought from and worked with, is gone. If you're deciding what comes next, the question isn't whether to move. It's whether you do it on your timeline or Amplitude's.
Whose roadmap are you on?
Whose roadmap are you on?
Amplitude has promised to maintain Statsig, but the integration and sunset is coming eventually. It’s worth asking where your money is going.
Amplitude has promised to maintain Statsig, but the integration and sunset is coming eventually. It’s worth asking where your money is going.
Everything you need, programmable
Everything you need, programmable
Use Mixpanel Headless to generate data apps and create deterministic automations for your experiments and flags, all using your coding agent of choice.
Use Mixpanel Headless to generate data apps and create deterministic automations for your experiments and flags, all using your coding agent of choice.
One platform, one data model
One platform, one data model
Analytics, session replay, experiments, and flags all share the same events, cohorts, and metrics, so there's nothing to reconcile across vendors.
Analytics, session replay, experiments, and flags all share the same events, cohorts, and metrics, so there's nothing to reconcile across vendors.
使い方を見る
Mixpanel の エクスペリメント（実験機能）で、どのようにテストを実施し、分析できるのかをセルフデモ形式で学べます
Mixpanel の エクスペリメント（実験機能）で、どのようにテストを実施し、分析できるのかをセルフデモ形式で学べます
*up to 12 months of remaining value
Work how you want to work
In code, via MCP, and through built-in agents, so your whole team works from the same data without sacrificing their workflow.
Headless programability: Every report, cohort, experiment, and alert is a callable Python object, giving agents and scripts full programmatic access to your product data.
Experiments and insights connected end-to-end: When you spot an issue, you can immediately test and measure improvements with built-in experiments, all without switching tools.
AI agents for your most pressing problems: Automate experiment setup, evaluation, root cause analysis, replay clustering, and more, plus an Onboarding Agent to get Mixpanel into your codebase fast.
Work how you want to work
In code, via MCP, and through built-in agents, so your whole team works from the same data without sacrificing their workflow.
Headless programability: Every report, cohort, experiment, and alert is a callable Python object, giving agents and scripts full programmatic access to your product data.
Experiments and insights connected end-to-end: When you spot an issue, you can immediately test and measure improvements with built-in experiments, all without switching tools.
AI agents for your most pressing problems: Automate experiment setup, evaluation, root cause analysis, replay clustering, and more, plus an Onboarding Agent to get Mixpanel into your codebase fast.
Work how you want to work
In code, via MCP, and through built-in agents, so your whole team works from the same data without sacrificing their workflow.
Headless programability: Every report, cohort, experiment, and alert is a callable Python object, giving agents and scripts full programmatic access to your product data.
Experiments and insights connected end-to-end: When you spot an issue, you can immediately test and measure improvements with built-in experiments, all without switching tools.
AI agents for your most pressing problems: Automate experiment setup, evaluation, root cause analysis, replay clustering, and more, plus an Onboarding Agent to get Mixpanel into your codebase fast.
Work how you want to work
In code, via MCP, and through built-in agents, so your whole team works from the same data without sacrificing their workflow.
Headless programability: Every report, cohort, experiment, and alert is a callable Python object, giving agents and scripts full programmatic access to your product data.
Experiments and insights connected end-to-end: When you spot an issue, you can immediately test and measure improvements with built-in experiments, all without switching tools.
AI agents for your most pressing problems: Automate experiment setup, evaluation, root cause analysis, replay clustering, and more, plus an Onboarding Agent to get Mixpanel into your codebase fast.
Experiment and rollout with confidence and ease
Flexible, powerful, and AI-driven testing workflows, now connected to the rest of your product development process.
Target precisely with behavioral cohorts: Go beyond attribute and environment based targeting with experiment groups from real user actions and event history, so your targeting reflects how people actually use your product — not just the attributes passed at request time.
Get results you trust with statistical rigor: CUPED, Winsorization, Bonferroni, Benjamini-Hochberg, Retrospective AA, and SRM monitoring, all quickly visible via an Diagnostics Tab so you can catch problems before they affect results.
Stay in control with developer-grade flag management: OpenFeature support for every SDK, per-flag permissions, SDK persistence, dynamic config, and OpenAPI specs give engineering teams the control they expect, without vendor lock-in.
Experiment and rollout with confidence and ease
Flexible, powerful, and AI-driven testing workflows, now connected to the rest of your product development process.
Target precisely with behavioral cohorts: Go beyond attribute and environment based targeting with experiment groups from real user actions and event history, so your targeting reflects how people actually use your product — not just the attributes passed at request time.
Get results you trust with statistical rigor: CUPED, Winsorization, Bonferroni, Benjamini-Hochberg, Retrospective AA, and SRM monitoring, all quickly visible via an Diagnostics Tab so you can catch problems before they affect results.
Stay in control with developer-grade flag management: OpenFeature support for every SDK, per-flag permissions, SDK persistence, dynamic config, and OpenAPI specs give engineering teams the control they expect, without vendor lock-in.
Experiment and rollout with confidence and ease
Flexible, powerful, and AI-driven testing workflows, now connected to the rest of your product development process.
Target precisely with behavioral cohorts: Go beyond attribute and environment based targeting with experiment groups from real user actions and event history, so your targeting reflects how people actually use your product — not just the attributes passed at request time.
Get results you trust with statistical rigor: CUPED, Winsorization, Bonferroni, Benjamini-Hochberg, Retrospective AA, and SRM monitoring, all quickly visible via an Diagnostics Tab so you can catch problems before they affect results.
Stay in control with developer-grade flag management: OpenFeature support for every SDK, per-flag permissions, SDK persistence, dynamic config, and OpenAPI specs give engineering teams the control they expect, without vendor lock-in.
Experiment and rollout with confidence and ease
Flexible, powerful, and AI-driven testing workflows, now connected to the rest of your product development process.
Target precisely with behavioral cohorts: Go beyond attribute and environment based targeting with experiment groups from real user actions and event history, so your targeting reflects how people actually use your product — not just the attributes passed at request time.
Get results you trust with statistical rigor: CUPED, Winsorization, Bonferroni, Benjamini-Hochberg, Retrospective AA, and SRM monitoring, all quickly visible via an Diagnostics Tab so you can catch problems before they affect results.
Stay in control with developer-grade flag management: OpenFeature support for every SDK, per-flag permissions, SDK persistence, dynamic config, and OpenAPI specs give engineering teams the control they expect, without vendor lock-in.
Make bets that move the business
Every part of Mixpanel is designed to close the gap between data and action, with the infrastructure and community to make it stick.
See the big picture with Metric Trees and Context Engine: Connect metrics and experiment results to business goals, and teach Mixpanel your business goals so AI queries get more accurate the more you use them.
Move at lightning speed with Arb and mirror mode: Get answers in seconds on a flexible data model that updates retroactively, and mirror your warehouse data with connectors for BigQuery, Databricks, Redshift, Postgres, and Snowflake.
Community and support: Don’t go it alone with on-demand learning via Mixpanel University, the largest community of product builders, and support that actually cares.
Make bets that move the business
Every part of Mixpanel is designed to close the gap between data and action, with the infrastructure and community to make it stick.
See the big picture with Metric Trees and Context Engine: Connect metrics and experiment results to business goals, and teach Mixpanel your business goals so AI queries get more accurate the more you use them.
Move at lightning speed with Arb and mirror mode: Get answers in seconds on a flexible data model that updates retroactively, and mirror your warehouse data with connectors for BigQuery, Databricks, Redshift, Postgres, and Snowflake.
Community and support: Don’t go it alone with on-demand learning via Mixpanel University, the largest community of product builders, and support that actually cares.
Make bets that move the business
Every part of Mixpanel is designed to close the gap between data and action, with the infrastructure and community to make it stick.
See the big picture with Metric Trees and Context Engine: Connect metrics and experiment results to business goals, and teach Mixpanel your business goals so AI queries get more accurate the more you use them.
Move at lightning speed with Arb and mirror mode: Get answers in seconds on a flexible data model that updates retroactively, and mirror your warehouse data with connectors for BigQuery, Databricks, Redshift, Postgres, and Snowflake.
Community and support: Don’t go it alone with on-demand learning via Mixpanel University, the largest community of product builders, and support that actually cares.
Make bets that move the business
Every part of Mixpanel is designed to close the gap between data and action, with the infrastructure and community to make it stick.
See the big picture with Metric Trees and Context Engine: Connect metrics and experiment results to business goals, and teach Mixpanel your business goals so AI queries get more accurate the more you use them.
Move at lightning speed with Arb and mirror mode: Get answers in seconds on a flexible data model that updates retroactively, and mirror your warehouse data with connectors for BigQuery, Databricks, Redshift, Postgres, and Snowflake.
Community and support: Don’t go it alone with on-demand learning via Mixpanel University, the largest community of product builders, and support that actually cares.
Mixpanel vs Statsig
Experiments (A/B/n)
Feature flags
Product analytics
Web analytics
Session replay
Web autocapture
Group/account analytics
Mobile session replay
Heatmaps
Full platform MCP Server support (analytics, experiments, flags, replay)
Open source SDK (Headless)
Custom roles & granular permissions
Verified metrics and cohorts
Metric Trees
Borrowed properties
Flexible data model (retroactive updates)
Warehouse Connectors (mirror mode)
Amazon Redshift Connector
Warehouse-native deployment
No-code / visual web editor
Install with AI
Feature
Mixpanel vs Statsig
Experiments (A/B/n)
Feature flags
Product analytics
Web analytics
Session replay
Web autocapture
Group/account analytics
Mobile session replay
Heatmaps
Full platform MCP Server support (analytics, experiments, flags, replay)
Open source SDK (Headless)
Custom roles & granular permissions
Verified metrics and cohorts
Metric Trees
Borrowed properties
Flexible data model (retroactive updates)
Warehouse Connectors (mirror mode)
Amazon Redshift Connector
Warehouse-native deployment
No-code / visual web editor
Install with AI
Feature
Mixpanel vs Statsig
Experiments (A/B/n)
Feature flags
Product analytics
Web analytics
Session replay
Web autocapture
Group/account analytics
Mobile session replay
Heatmaps
Full platform MCP Server support (analytics, experiments, flags, replay)
Open source SDK (Headless)
Custom roles & granular permissions
Verified metrics and cohorts
Metric Trees
Borrowed properties
Flexible data model (retroactive updates)
Warehouse Connectors (mirror mode)
Amazon Redshift Connector
Warehouse-native deployment
No-code / visual web editor
Install with AI
Feature
Product analytics
Web analytics
Session replay
Web autocapture
Group/account analytics
Mobile session replay
Heatmaps
Full platform MCP Server support (analytics, experiments, flags, replay)
Open source SDK (Headless)
Custom roles & granular permissions
Verified metrics and cohorts
Metric Trees
Borrowed properties
Flexible data model (retroactive updates)
Warehouse Connectors (mirror mode)
Amazon Redshift Connector
Warehouse-native deployment
No-code / visual web editor
Install with AI
Feature flags
Experiments (A/B/n)
成長への一歩を今すぐに
成長への一歩を今すぐに
ウェビナー
データを“成果”に変える
Zola のようなトップ企業が、データ収集と
ビジネス変革のギャップをどのように埋めているのか。
その戦略的フレームワークを学べます
ビデオ
More ships, more problems? How Mixpanel is building experiments for the AI Era
Mixpanelのメトリックツリーは、データに明確な文脈を
与え、あらゆる施策・目標・指標をひとつの成長ストーリー
としてつなげて可視化します
Mixpanelのメトリックツリーは、データに明確な文脈を
与え、あらゆる施策・目標・指標をひとつの成長ストーリー
としてつなげて可視化します
eBook
ノーススターの先へ
メトリックツリーで成長戦略を実行可能にし、
持続的かつ測定可能な成長を実現するフレームワークの
構築方法を学びましょう