PostHog vs Google Analytics 4 vs Analytics 360: Which Should You Choose?

Choose PostHog if

Start with PostHog when:

• the authenticated application matters more than the public marketing site;
• product managers and engineers need funnels, retention, paths, cohorts, and user-level investigation;
• session replay, feature flags, experiments, and surveys should share one product-data model;
• a B2B team needs company, workspace, project, or another group as an analytical entity;
• engineering wants product analysis and feature-delivery workflows in the same platform.

The main limitation is category fit: PostHog can measure websites and acquisition properties, but it does not reproduce the depth of GA4’s ecommerce reporting, Google Ads workflows, and Google marketing integrations.

Choose Google Analytics 4 if

Start with GA4 when:

• acquisition source, campaign, landing page, content, or ecommerce performance is the main question;
• the organization uses Google Ads, Search Console, Firebase, BigQuery, or other Google services;
• a public website or storefront is more important than complex authenticated-product behavior;
• a no-license-fee analytics baseline matters;
• marketers need standard traffic and acquisition reporting before the team is ready to operate a product-analytics program.

The main limitation is that GA4 is not a replay, feature-flag, survey, or experiment-delivery platform. Authenticated-product analysis is possible, but it usually requires more deliberate event design and warehouse work than a product-first tool.

Choose Analytics 360 if

Choose Analytics 360 when:

• the organization has already chosen the GA4 model;
• Standard-property limits, retention, exploration sampling, or BigQuery export limits are material;
• separate business units need governed subproperties or leadership needs roll-up reporting;
• formal service levels, enterprise support, procurement, and organizational governance are required;
• a large Google Marketing Platform implementation needs enterprise scale.

Analytics 360 is the enterprise edition of Google Analytics. It extends GA4; it does not replace GA4’s event model with a different product-analytics methodology.

Use both when

Use PostHog with GA4 or Analytics 360 when both halves of the customer journey matter:

• GA4 or Analytics 360 measures the visit, campaign, source, ecommerce journey, and advertising audience;
• PostHog measures authenticated behavior, adoption, replay, experiments, and feature delivery;
• a governed identity or warehouse layer connects acquisition context to the eventual user or customer account.

At-a-glance verdict

The practical verdict: PostHog is the stronger starting point for an authenticated SaaS product. GA4 is the stronger starting point for a public website, ecommerce operation, or acquisition program. Analytics 360 is justified when a large organization wants GA4 with enterprise limits and governance—not merely because it wants better product analytics.

Neither rating nor feature count changes that category distinction. A tool can have a higher rating and still be the wrong fit for the decision a team needs to make. For a wider market view, see our product analytics tools comparison.

Why are PostHog and Google Analytics compared if they belong to different categories?

People compare PostHog and Google Analytics because both collect events, identify users to some degree, build funnels, segment behavior, and export data. Those overlapping mechanics make them look interchangeable.

The important difference is the decision each product is organized around.

GA4 begins with questions such as:

• Which channel brought this visit?
• Which campaign generated a conversion?
• Which landing pages and content perform?
• How does an ecommerce journey convert?
• Which audiences should be activated in Google’s marketing ecosystem?

PostHog begins with questions such as:

• Which users adopted a feature?
• Where does an onboarding or product workflow fail?
• Do users return and repeat valuable behavior?
• What happened in a failed session?
• Should a feature be released, rolled back, or tested with another cohort?

Analytics 360 retains GA4’s orientation. It gives the Google Analytics model more capacity, governance, support, and formal service levels. It does not turn GA4 into a feature-flag, session-replay, or account-centric product-intelligence platform.

That is why the useful comparison is not “Which analytics product has the most features?” It is “Which decision system should this team operate?”

Consultancy-authored practitioner comparison

Product and website data models

All three products use events, but “event-based” does not mean “the same data model.”

PostHog’s model

PostHog organizes analysis around events, persons, sessions, and optional groups. A person can begin anonymously and later be identified, allowing earlier anonymous behavior to be associated with the identified profile when implemented correctly.

Its paid group-analytics add-on can introduce entities such as:

• company;
• workspace;
• organization;
• project;
• household;
• another application-specific group.

PostHog currently documents support for up to five group types per project and an unlimited number of groups. Groups and group properties can be used in trends, funnels, retention, stickiness, feature flags, experiments, and warehouse-backed analysis. Enabling group analytics changes billing because identified events become billable under that add-on. See the official group analytics documentation.

This model fits products in which a known user performs meaningful actions after login and the team needs to connect analysis to delivery or debugging.

GA4’s model

GA4 also uses events. Page views, screen views, purchases, form actions, and custom interactions are represented as events with parameters. Reports and Explorations organize those events through users, sessions, traffic sources, audiences, dimensions, metrics, and properties.

GA4 supports a business-provided User-ID for signed-in users. That can connect behavior across sessions, devices, and platforms when the same ID is supplied. Google explicitly treats User-ID as a reserved identifier and advises against registering the raw ID as a custom dimension. See the official User-ID documentation.

GA4 can measure an authenticated application. The distinction is not that GA4 is incapable of product events. The distinction is that its standard reports, advertising relationships, and governance model begin from web/app and marketing analysis rather than from product operation.

Analytics 360’s model

Analytics 360 uses the same Google Analytics event and property model. An organization can gain higher limits, longer eligible retention, unsampled exploration capacity, subproperties, roll-up properties, service levels, support, and higher-scale integrations.

A migration from Standard to Analytics 360 should therefore be understood primarily as a commercial, capacity, governance, and service-level change. It is not a migration to a different concept of users, events, sessions, or product adoption.

Events, page views, users, and identities

A reliable comparison requires separating four concepts that analytics implementations often collapse.

An event is an observation

An event records that something happened. Examples include:

• page_view;
• sign_up;
• report_builder_opened;
• report_created;
• subscription_started;
• purchase.

Event names alone rarely create useful analysis. A team also needs stable properties, definitions, ownership, validation, and a decision that the event will support.

For an implementation framework, see the existing product analytics instrumentation plan.

A page view is not automatically feature adoption

A page view may indicate discovery, navigation, a refresh, or a route transition. It does not prove that the user completed the page’s primary job.

For example, visiting /reports/new is weaker evidence than recording report_created after the backend confirms a valid report. A robust implementation can retain both:

• page and screen events for navigation context;
• explicit business events for workflow completion.

PostHog’s autocapture can accelerate discovery and debugging, but a durable product model still benefits from deliberate events. GA4’s enhanced measurement and recommended events can reduce basic setup effort, but important product and ecommerce outcomes still require implementation discipline.

A user is an identity strategy, not merely an ID field

The team must decide:

• when anonymous activity becomes identified;
• which internal ID is canonical;
• how IDs behave across browser, mobile, and server events;
• whether users can belong to more than one account;
• how account changes are represented;
• which identifiers are prohibited from analytics destinations;
• how deletion and retention requests propagate.

PostHog’s person model makes individual user timelines and anonymous-to-identified merging central to the product workflow. GA4 supports User-ID, but organizations frequently rely on BigQuery or another governed layer for deeper identity reconciliation.

A company is not the same as a property

This distinction is critical in B2B SaaS.

A customer company or workspace is an entity inside the measured product. A GA4 property, Analytics 360 subproperty, or roll-up property is an analytics-administration construct.

Do not use Analytics 360 subproperties or roll-ups as evidence that GA4 has native B2B account analytics:

• a subproperty filters data from a source property for a team or use case;
• a roll-up property combines data from multiple source properties;
• neither automatically models the customer companies using one SaaS product.

B2B account and group analytics

PostHog is the strongest of these three choices when a team wants a reusable company, workspace, or project entity inside the analytics and experimentation workflow.

A B2B implementation might send:

• a stable person ID;
• a stable company ID;
• company properties such as plan, region, or lifecycle stage;
• product events such as report_created;
• exposure events for a feature flag or experiment.

That allows the team to ask:

• Which companies adopted Reporting?
• Is usage broad across each company or concentrated in one champion?
• Which account segments have the highest completion?
• Did a company-level rollout improve repeat use?
• Which accounts experienced a failed workflow?

GA4 can carry a company or workspace identifier as a custom event parameter or user property, subject to its data policies and cardinality considerations. BigQuery can then support account-level queries. That is a valid implementation, but the company remains a custom modeling decision rather than a first-class group entity across GA4’s product surface.

Analytics 360 raises limits and gives enterprises more ways to govern properties and reporting. It does not remove the need to build that account model.

This distinction is explored further in our guides to B2B product analytics, product usage by company, and account versus user adoption.

Funnels, retention, paths, and cohorts

PostHog

PostHog provides native product-analysis views for:

• trends;
• funnels;
• retention;
• paths;
• stickiness;
• lifecycle;
• cohorts.

Funnels can analyze ordered product actions, breakdowns, conversion windows, and relevant properties. Retention can measure whether a person or group returns to repeat a behavior. Paths expose navigation between events or pages. Cohorts can be used in analysis and adjacent product workflows.

The important benefit is not merely having a funnel chart. It is being able to move from a funnel or cohort to a person, group, replay, feature flag, or experiment without rebuilding the context in another system.

Official references:

• Product analytics overview
• Funnels
• Retention

Google Analytics 4

GA4 Explorations support:

• funnel exploration;
• path exploration;
• cohort exploration;
• segment overlap;
• free-form analysis;
• user-lifetime analysis where available.

These tools are capable of analyzing signup, checkout, content, and product journeys. The main limitations are not the absence of every analytical technique. They are the surrounding workflow, sharing and governance model, identity design, query limits, sampling conditions, and absence of native replay or feature delivery.

Official references:

• Explorations
• Funnel exploration
• Path exploration
• Cohort exploration

Analytics 360

Analytics 360 uses the same exploration techniques but raises material limits. Current official documentation lists a Standard exploration sampling threshold of 10 million events per query and an Analytics 360 threshold of 1 billion. Analytics 360 also provides unsampled exploration capacity through daily data tokens, subject to current quotas and conditions.

This matters for large properties. It does not mean an enterprise should buy Analytics 360 solely because it wants a better onboarding funnel. The organization should first determine whether its actual constraint is:

• data volume;
• sampling;
• retention;
• property governance;
• BigQuery export;
• service levels;
• support;
• or the underlying product-analysis workflow.

Session replay and qualitative evidence

PostHog is the only one of the three products with native session replay.

Its web replay can connect a recording to events, users, funnels, errors, and product analysis. PostHog also documents mobile replay support for Android, iOS, React Native, and Flutter, with platform-specific installation and masking behavior. See the official mobile replay documentation.

GA4 and Analytics 360 do not include native session replay. A team using either product must add another replay or digital-experience tool when it needs visual evidence of what occurred in a session.

Replay changes the type of question a team can answer:

• A funnel shows where completion dropped.
• A replay can show whether the user encountered an unclear field, unexpected navigation, loading delay, validation problem, or another visible obstacle.
• An event stream can confirm what was recorded.
• A server or error-tracking system can confirm what failed technically.

A replay still does not prove motivation or establish that one observed session represents the entire population. Use aggregate analysis to choose recordings, then use recordings as evidence for further investigation. Our session replay versus product analytics guide explains that combined workflow.

Official PostHog walkthrough

Feature flags and experiments

This is one of the clearest category differences.

PostHog

PostHog includes native feature flags with documented support for:

• boolean flags;
• multivariate flags;
• targeting rules;
• rollout percentages;
• remote configuration and payloads.

Experiments use feature-flag delivery and analytics together. A team can define a variant, record exposure, measure outcomes, inspect affected cohorts, and use the same platform to manage rollout.

Official references:

• Creating feature flags
• Experiments

This integration is especially useful when the decision is operational: not only “Did variant B perform better?” but also “Should engineering ship it, expand it, pause it, or inspect the affected sessions?”

Google Analytics 4 and Analytics 360

GA4 can analyze experiment results sent from another system. It does not provide a general native feature-flag delivery platform.

Google’s current documentation describes using third-party A/B testing tools with Google Analytics. Google Optimize and Optimize 360 were sunset on September 30, 2023. Firebase A/B Testing remains a separate Google workflow for supported app and Remote Config use cases, but that should not be confused with GA4 itself providing broad feature-flag infrastructure.

Official references:

• Integrate A/B testing tools with Google Analytics
• Google Optimize sunset

Analytics 360 does not change that fundamental division of responsibility.

Acquisition, campaigns, ecommerce, and advertising

GA4 is the strongest default of these products when the primary problem is acquisition measurement.

It is built to organize:

• source, medium, and campaign;
• landing pages;
• channel groups;
• content engagement;
• conversions or key events;
• ecommerce events and item data;
• audiences;
• attribution and advertising relationships;
• Google Ads and other Google product links.

Google publishes a recommended ecommerce event model covering actions such as viewing items, adding items to a cart, beginning checkout, and purchasing. See the official GA4 ecommerce documentation.

PostHog can capture:

• referrers;
• UTM properties;
• landing pages;
• web sessions;
• conversion events;
• the later authenticated user journey.

That may be enough for a SaaS team with modest acquisition needs. It is not equivalent to the complete Google marketing and ecommerce workflow. Teams that depend on paid media optimization, Google Ads audiences, item-level ecommerce reporting, or established agency processes will usually retain GA4 or Analytics 360.

Analytics 360 is justified when those Google-centered workflows need enterprise limits, governed organizational structures, contractual service levels, and support. It does not need to be purchased merely to make basic GA4 attribution available.

GA4 versus Analytics 360

Analytics 360 should be evaluated as an enterprise extension of Google Analytics, not as a separate product-analytics category.

The following differences were verified against Google’s current documentation on August 4, 2026.

Three caveats matter:

1. Higher limits do not create a new analytical model. A difficult event taxonomy remains difficult in Analytics 360.
2. Subproperties and roll-ups solve organizational governance problems. They are not customer-company entities inside a B2B product.
3. A service level is scoped. Do not describe every report or integration as real-time or SLA-backed when Google’s terms contain explicit timing, property-size, and integration exclusions.

Illustrative product-analysis workflow

The following scenario is illustrative. It does not describe a hands-on benchmark.

Assume a fictional B2B SaaS company has introduced a Reporting feature. A successful workflow includes:

1. opening the report builder;
2. selecting data;
3. configuring the report;
4. creating the report;
5. returning later to create or view another report.

The team also wants to compare adoption across customer workspaces.

The table demonstrates why PostHog can be a better product-analysis workflow without making GA4 technically incapable of recording product events. It also demonstrates why GA4 can remain valuable when the product team needs to know which campaign created the user before the user entered the Reporting workflow.

Warehouse export and raw-data access

A warehouse export is not merely a backup feature. It changes where complex identity, finance, account, and attribution analysis can happen.

PostHog

PostHog documents batch-export destinations including:

• BigQuery;
• Snowflake;
• Databricks;
• Amazon S3;
• Azure Blob Storage;
• PostgreSQL;
• Redshift.

See PostHog batch exports.

The broader platform also includes data-warehouse and data-pipeline products. That can keep product analysis and activation close to the same event model, but row processing, export volume, destinations, and operational ownership can introduce additional cost.

Google Analytics 4

GA4 can export raw event data to BigQuery. This is one of its most important capabilities because it allows teams to:

• write custom SQL;
• retain data under their own cloud configuration;
• join acquisition events to application, billing, CRM, or account data;
• create governed BI models;
• work around some interface constraints.

The Standard daily export has a current limit of one million events per day. Streaming behavior, cloud charges, schema design, and discrepancies between BigQuery and interface reporting still require technical ownership.

Official references:

• Set up BigQuery export
• BigQuery Export schema
• BigQuery export types and reporting differences

Analytics 360

Analytics 360 raises BigQuery daily export capacity to as many as 20 billion events and provides Fresh Daily for eligible Normal and Large properties with Google Cloud billing enabled. Google currently provides no Fresh Daily service level. That can be material for enterprises that need large, governed event feeds early in the following day.

The warehouse still creates its own total cost:

• storage;
• query processing;
• orchestration;
• semantic modeling;
• identity resolution;
• BI tools;
• quality monitoring;
• data engineering;
• access governance.

Buying Analytics 360 does not remove those responsibilities.

Data ownership, hosting, and self-hosting

“Can I export the data?” and “Can I operate the analytics system?” are different questions.

PostHog Cloud

PostHog Cloud is the supported managed deployment path. It removes most infrastructure operations and gives teams access to the platform’s current hosted products and support options.

PostHog self-hosting

PostHog’s source code is available, and a self-hosted path exists. However, PostHog’s current documentation describes self-hosted deployment as unsupported. It warns that:

• there are no versioned self-hosted releases in the conventional supported-product sense;
• deployment tracks current Docker images;
• security and upgrade workflows differ;
• scaling guidance and support are limited;
• feature parity with Cloud should not be assumed.

See PostHog’s self-hosting documentation.

Therefore, describe PostHog as having an open-source, unsupported self-hosting path, not as offering a fully supported enterprise on-premises edition.

A team considering this route should budget for:

• ClickHouse and application infrastructure;
• storage;
• backups;
• upgrades;
• observability;
• security response;
• data retention;
• capacity planning;
• incident ownership;
• feature differences.

For a broader deployment framework, see our self-hosted product analytics guide.

Google Analytics and Analytics 360

GA4 and Analytics 360 are Google-hosted services. They cannot be self-hosted.

BigQuery export can give the customer a copy of event-level data inside its Google Cloud environment, but that is not equivalent to operating the original analytics collection and reporting service.

Privacy and consent considerations

This comparison does not make a jurisdiction-specific legal conclusion. The correct implementation depends on:

• where the organization and its users are located;
• which identifiers and event properties are collected;
• whether replay is enabled;
• which advertising features are used;
• the organization’s role and contractual relationships;
• consent and communication requirements;
• retention and deletion policies;
• the configuration of each tool.

A responsible evaluation should answer five technical questions.

1. What is collected?

Document:

• event names;
• event properties;
• user and account identifiers;
• URLs and query strings;
• form and DOM content;
• replay fields;
• campaign identifiers;
• device and location data;
• imported CRM or advertising data.

2. What is intentionally excluded?

PostHog documents controls for opt-in or opt-out behavior, masking, redaction, and capture configuration. Replay implementations need particular care because the visible interface may contain information that a simple event taxonomy would never collect.

See PostHog privacy and data collection.

GA4 implementations should use Google’s available privacy controls, retention settings, and consent-state mechanisms where applicable. Consent Mode allows an implementation to set default consent states and update them after a user interaction; it is a technical mechanism, not a substitute for legal analysis.

Official references:

• Google consent mode
• Google Analytics data retention
• Google Analytics privacy controls

3. Where does the data go?

Map browser, mobile, server, vendor-cloud, warehouse, advertising, replay, and export destinations.

4. Who can access it?

Review roles, projects, properties, workspaces, environments, API keys, support access, exported datasets, and downstream tools.

5. How is it deleted or retained?

Align application deletion, analytics deletion, replay retention, warehouse retention, backups, and derived tables.

No product name makes an implementation automatically compliant. Self-hosting does not remove governance obligations, and a consent configuration does not establish a legal conclusion by itself.

Implementation and governance effort

The easiest script to install is not necessarily the easiest analytics system to maintain.

PostHog implementation effort

PostHog can create early value through SDK installation, autocapture, web analytics, and replay. Deeper value requires:

• deliberate identity calls;
• trusted custom events;
• group design for B2B use cases;
• replay masking and sampling;
• event-volume controls;
• flag and experiment governance;
• environment separation;
• cost monitoring;
• ownership of dashboards and definitions.

The broad platform can reduce the number of vendors a team coordinates. It also creates more configuration surface inside one vendor.

GA4 implementation effort

A basic website tag can produce traffic reports quickly. A trusted implementation for campaigns, ecommerce, authenticated products, consent states, server events, and BigQuery is a larger program.

Typical work includes:

• GTM or direct-tag governance;
• event and parameter definitions;
• ecommerce validation;
• cross-domain configuration;
• User-ID design;
• channel and campaign conventions;
• consent-state behavior;
• advertising links;
• BigQuery export;
• report and exploration governance;
• discrepancy investigation.

Analytics 360 implementation effort

Analytics 360 adds:

• commercial procurement;
• billable-event forecasting;
• organization and property architecture;
• subproperty filters;
• roll-up design;
• access governance;
• service-level review;
• support processes;
• data-quality ownership across regions or brands.

Its value is highest when the organization genuinely needs those controls. Buying it without an operating model can create a more expensive version of the same measurement confusion.

Pricing and total operating cost

Pricing was verified on August 4, 2026.

What PostHog’s sticker price can hide

PostHog’s free allowances are substantial for a small product, but the platform meters several products independently.

Cost can grow through:

• indiscriminate autocapture;
• high-volume anonymous traffic;
• identified product events;
• recording too many sessions;
• mobile replay;
• enabling group analytics;
• feature-flag request volume;
• survey response volume;
• long retention;
• warehouse or pipeline rows;
• platform packages.

Use product-level billing caps, sampling, event governance, and regular volume reviews. Do not estimate PostHog from one per-event number without distinguishing event types and products.

What “free GA4” can hide

GA4 Standard has no license fee, but a mature implementation can require:

• analytics engineering;
• GTM governance;
• server-side tagging infrastructure;
• consent management;
• BigQuery storage and queries;
• BI or semantic modeling;
• replay software;
• experiment software;
• implementation partners;
• recurring QA.

For a small marketing site, those costs may remain minimal. For a global ecommerce or authenticated-product implementation, they can exceed the cost of a paid analytics license elsewhere.

What Analytics 360’s contract can hide

The contract is only one component. Total operating cost may include:

• higher event volume;
• subproperty processing;
• regional or brand implementation;
• partner services;
• BigQuery processing;
• data engineering;
• support and escalation processes;
• governance councils;
• migration and training.

Do not judge whether Analytics 360 is worth it by comparing a rumored annual price with PostHog’s lowest free allowance. Compare the operating model the organization actually needs.

Customer ratings and recurring review themes

Ratings were taken from G2 as one common source. They were not combined with Capterra, TrustRadius, or another review site.

The differences in averages are too small and the reviewer populations too different to declare a comparison winner.

PostHog review themes

Commonly praised

Recent positive reviews repeatedly mention:

• the breadth of analytics, replay, flags, experiments, and related tools in one platform;
• useful product-behavior visibility;
• developer-oriented setup and documentation;
• the ability to move from metrics to individual sessions;
• a generous entry point for smaller teams.

Commonly criticized

Critical reviews and lower-scoring comments commonly mention:

• a learning curve once the team moves beyond basic dashboards;
• technical configuration for advanced use cases;
• complexity created by the breadth of the platform;
• dashboard or report discoverability;
• concern about predicting cost at higher event or replay volume.

Google Analytics review themes

Commonly praised

Reviews frequently praise:

• broad website and marketing visibility;
• established traffic and acquisition reports;
• Google ecosystem integrations;
• depth for analysts who understand the product;
• the availability of a no-license-fee Standard product.

Commonly criticized

Recurring criticism includes:

• a steep learning curve;
• reports or settings that are difficult to find;
• interface complexity;
• difficulty translating business questions into the correct report or Exploration;
• implementation and interpretation challenges after the GA4 transition.

Analytics 360 review themes

Commonly praised

Reviews commonly mention:

• scale;
• advanced reporting;
• enterprise integrations;
• richer organizational use than the Standard edition;
• support for large analytics programs.

Commonly criticized

Recurring criticism includes:

• cost;
• implementation complexity;
• the need for specialists;
• a steep learning curve;
• difficulty justifying the product for organizations that do not need enterprise scale.

Best evidence from reviews

Review data is most useful for identifying recurring perceptions about:

• usability;
• setup effort;
• documentation;
• workflow breadth;
• support;
• cost predictability;
• the kinds of teams that regularly use the product.

What review data cannot establish

G2 reviews cannot prove:

• measurement accuracy in a specific implementation;
• legal suitability;
• security or compliance;
• return on investment;
• current feature packaging;
• current pricing;
• enterprise service quality for every customer;
• that one product is universally better.

Reviews are a supplement to official documentation and an organization’s own requirements—not a substitute for either.

Decision by team

Migration and using both products together

A dual stack is often more rational than forcing one tool to answer every question.

A complementary architecture

A practical split is:

1. Public marketing visit

   • GA4 or Analytics 360 records campaign, source, landing page, content, ecommerce activity, and advertising audiences.

2. Signup and identity transition

   • The application creates a stable internal user ID.
   • Approved first-touch or last-touch acquisition context is persisted in an application or warehouse layer.
   • PostHog identifies the user.
   • GA4 receives User-ID where appropriate.

3. Authenticated product behavior

   • PostHog records deliberate product events, replay, groups, experiments, and feature-flag exposures.
   • GA4 may receive a smaller shared set of important product outcomes needed for acquisition reporting.

4. Customer-account layer

   • A canonical company or workspace ID connects users to the customer account.
   • PostHog group analytics, a warehouse model, or an account-centric product-intelligence system analyzes account adoption.

5. Warehouse or governed identity layer

   • Acquisition, product, billing, CRM, and account data are joined using approved identifiers and documented transformations.

Rules for running both

Align business events, not every automatic event

Use shared names for a small set of decisions, such as:

• sign_up_completed;
• workspace_created;
• report_created;
• subscription_started.

Do not force every PostHog autocaptured event into GA4.

Keep a canonical identity dictionary

Document:

• internal user ID;
• internal account ID;
• GA4 User-ID;
• PostHog distinct ID;
• anonymous IDs;
• warehouse keys;
• allowed and prohibited properties.

Preserve acquisition context deliberately

Browser cookies and analytics identities can disappear or diverge. Persist the approved attribution fields that the business genuinely needs, then connect them to the eventual user or account through a controlled application or warehouse process.

Expect numerical differences

GA4 and PostHog can disagree because of:

• session definitions;
• identity behavior;
• consent states;
• blockers;
• bot handling;
• time zones;
• event timing;
• filters;
• retries;
• server events;
• retention;
• implementation defects.

The goal is not to make every dashboard number identical. The goal is to understand why material differences exist and which system is authoritative for each decision.

Use a parallel validation period

During a migration:

• run the old and new systems together;
• compare a small set of trusted business events;
• validate identities and account relationships;
• inspect missing and duplicate events;
• confirm downstream reports;
• train the teams that will own the new workflows;
• remove old tags only after the new implementation is trusted.

Migrating from GA4 to PostHog

Do not assume that historical GA4 reports can be reproduced exactly inside PostHog. Retain historical exports where needed, map decision-critical events, and start a clean product-oriented taxonomy.

Keep GA4 when paid acquisition, ecommerce, or Google Ads optimization remains important.

Migrating from PostHog to GA4

Map PostHog events to GA4 recommended events where they fit and use custom events only when necessary. Review parameter limits, identity behavior, cardinality, ecommerce item structures, and the absence of native replay or flags.

Moving from Standard to Analytics 360

Treat this as an enterprise upgrade and governance program. Confirm the actual property-upgrade, contract, partner, billing, and organization process with Google or an authorized provider. The analytical model remains GA4, so fix taxonomy and quality problems before assuming that higher limits will solve them.

Final recommendations by buyer type

Choose PostHog for a product and engineering operating system

PostHog is the best starting choice when the team needs to:

• understand feature adoption and retention;
• investigate individual or group behavior;
• watch relevant sessions;
• run experiments;
• manage feature flags;
• collect surveys;
• connect analysis to engineering action.

It is especially compelling for an authenticated SaaS application. It is less compelling as the only analytics product for a sophisticated ecommerce or Google Ads program.

Choose GA4 for an acquisition and public-site operating system

GA4 is the best starting choice when the team needs to:

• measure sources and campaigns;
• analyze landing pages and content;
• implement ecommerce reporting;
• build advertising audiences;
• connect to Google Ads, Search Console, Firebase, and BigQuery;
• establish a no-license-fee website baseline.

It can record authenticated-product events, but a product team should test whether its daily analysis can remain self-service without additional replay, experimentation, identity, and warehouse tooling.

Choose Analytics 360 for enterprise Google requirements

Analytics 360 is appropriate when the organization can name the specific enterprise constraint:

• Standard limits;
• exploration sampling;
• BigQuery export scale;
• longer eligible retention;
• subproperties;
• roll-ups;
• formal service levels;
• enterprise support;
• large Google Marketing Platform governance.

“Enterprise company” by itself is not a sufficient reason. The organization should be able to explain which 360 capability changes a business process or risk.

Choose a dual stack when acquisition and adoption are both material

For many SaaS businesses, the most durable answer is:

• GA4 on the public site and acquisition journey;
• PostHog in the authenticated product;
• a stable identity and warehouse model connecting the two.

That division follows the customer journey rather than forcing one vendor to own every analytical job.

Where Hymetry fits

Hymetry is relevant when the central question is not only what one user did, but how a customer company and the people inside it adopt a B2B SaaS product.

Hymetry is account-centric product intelligence for B2B SaaS. Its product model connects:

• Pages, organized into meaningful grouped pages and product areas;
• Companies, showing account adoption and change;
• Users, showing the people and user distribution behind account behavior;
• Visits, providing session-level evidence;
• the account, user, page, and session context behind an analytical signal.

That model can be useful when a team asks:

• Which customer companies adopted Reporting?
• Is adoption broad across the account or dependent on one champion?
• Which product areas remain unused?
• Which users are active, light, passive, dropped, at risk, or gaining momentum?
• Which Visits should a product or customer-success team inspect?

Explore the relevant Hymetry product views:

• Companies
• Pages
• Users
• Visits

Hymetry may be considered when a B2B SaaS team wants an account-centric product model by default rather than constructing company analysis from individual-user events after the fact.

It is not a replacement for:

• GA4 or Analytics 360 when acquisition, advertising, ecommerce, and the Google marketing ecosystem are primary;
• PostHog when the team wants a broad developer platform, native feature flags, or advanced experimentation workflows;
• a mature warehouse, CRM, customer-success system, or enterprise marketing stack.

The useful question is not whether Hymetry has more general-purpose features. It is whether the company-account view should be the starting point for product investigation.

Frequently asked questions

Can PostHog replace Google Analytics?

PostHog can replace GA4 when the organization’s primary need is product behavior, web analytics, funnels, retention, replay, experiments, flags, and surveys—and when its acquisition requirements are modest.

It is less likely to replace GA4 completely when the team depends on sophisticated ecommerce reporting, Google Ads audiences, campaign attribution, or established Google Marketing Platform workflows. Many SaaS companies should use PostHog for the authenticated product and retain GA4 for acquisition.

Should a SaaS company use PostHog and GA4 together?

Often, yes.

A common division is:

• GA4 for the public website, acquisition, campaigns, and advertising;
• PostHog for the authenticated application, product adoption, replay, flags, and experiments;
• a governed identity or warehouse layer to connect source data to the eventual user and account.

The implementation should align a small set of important business events rather than duplicating every automatic event.

What is the difference between GA4 and Analytics 360?

GA4 is the current event-based Google Analytics model. Standard Google Analytics has no analytics license fee and is subject to Standard limits.

Analytics 360 is the paid enterprise service level of Google Analytics. It uses the same basic event, user, session, and property model while adding higher limits, longer eligible retention, higher-scale BigQuery export, unsampled exploration capacity, subproperties, roll-up properties, service levels, support, and enterprise contracting.

Is Analytics 360 better for product analytics?

Not inherently.

Analytics 360 gives GA4 more scale and governance. It does not add native session replay, feature flags, general experiment delivery, surveys, or a first-class customer-company model.

It is better than Standard when the product-analysis program is constrained by GA4 limits, retention, exports, sampling, governance, or service requirements. It does not automatically make GA4 more product-oriented.

Does PostHog support account-level B2B analytics?

Yes. PostHog’s paid group-analytics add-on can model entities such as companies, workspaces, organizations, or projects. Groups can be used in trends, funnels, retention, stickiness, flags, experiments, and warehouse-backed analysis.

The team must still implement stable group IDs, group membership, approved properties, and an account-adoption definition. Current documentation supports up to five group types per project.

Which product has session replay?

PostHog has native web session replay and documented replay support for Android, iOS, React Native, and Flutter.

GA4 and Analytics 360 do not include native session replay. They require a companion replay product.

Which choice is best for ecommerce?

GA4 is generally the best starting choice for ecommerce because of its recommended ecommerce event model, item data, acquisition reports, audiences, attribution workflows, and Google Ads relationships.

Analytics 360 becomes relevant when an enterprise ecommerce organization needs higher limits, governed properties, support, and service levels.

PostHog can complement GA4 for a logged-in customer portal or another product-like experience, but it should not be selected merely to reproduce GA4’s established ecommerce and advertising workflows.

Which choice is best for an authenticated SaaS application?

PostHog is usually the best starting choice because it is organized around identified product behavior, funnels, retention, user or group analysis, session replay, flags, and experiments.

Retain GA4 when acquisition and campaign attribution matter. Evaluate Analytics 360 only when the organization has enterprise Google requirements.

Is PostHog self-hosted?

PostHog’s source code and a self-hosted deployment path are available. However, PostHog currently describes self-hosting as unsupported.

Do not treat it as a supported enterprise on-premises edition. A self-hosting team assumes infrastructure, upgrades, observability, backups, security response, retention, scaling, and feature-parity risk.

Which is cheaper?

There is no universal answer.

• GA4 Standard has no analytics license fee and is usually cheapest for basic public-site analytics.
• PostHog can be inexpensive within its free allowances and can consolidate several product tools, but event, replay, group, flag, warehouse, and support volume can increase the bill.
• Analytics 360 is a custom enterprise contract and should be justified by limits, governance, support, and service requirements.

Compare total operating cost, not only the lowest advertised price.

Where does Hymetry fit?

Hymetry fits B2B SaaS teams that want to investigate product behavior through customer companies, their users, grouped product pages, and session evidence.

It can provide an account-centric starting point for adoption and customer investigation. It does not replace GA4 or Analytics 360 for acquisition and advertising, PostHog for a broad developer platform and feature delivery, or a mature warehouse and enterprise stack.

Disclosure

Hymetry publishes this comparison. Hymetry is mentioned only where its account-centric B2B product model is relevant. Product capabilities, prices, ratings, and videos are verified against linked sources and may change after publication.

Methodology

This comparison was researched and verified on August 4, 2026.

Evidence levels

Hands-on

No direct hands-on benchmark or controlled test account was used for this article. The article does not claim that Hymetry tested the three products against a shared dataset.

Official demonstration and documentation

The analysis uses:

• official product pages;
• official feature documentation;
• official pricing and billing documentation;
• official identity, export, privacy, retention, governance, and service-level documentation;
• an official PostHog demonstration.

Documentation and independent reviews

The analysis also uses:

• G2 ratings and recurring review themes;
• a recent practitioner comparison from Vision Labs;
• selected independent comparison material as contextual evidence.

Independent and vendor-authored sources were not used to override current primary documentation.

Comparison method

The products were compared across:

• category and buyer intent;
• event and identity model;
• public-site and authenticated-product analysis;
• B2B group analysis;
• funnels, retention, paths, and cohorts;
• replay;
• flags and experiments;
• acquisition and ecommerce;
• raw-data export;
• hosting and deployment;
• implementation and governance;
• price and operating cost;
• customer review themes.

No weighted numerical winner was calculated because the products begin from different categories and priorities.

Pricing method

Pricing uses current official public pages. Analytics 360 is shown as Custom pricing because a reliable universal public contract price is not available. PostHog’s usage rates are shown with their billing units and free allowances rather than reduced to one misleading entry price.

Review method

All ratings use G2 as one common source. Scores from multiple review sites were not averaged. Review counts are approximate because they can change continuously.

Privacy method

The article compares technical collection, deployment, consent, masking, retention, and data-flow considerations. It does not make a jurisdiction-specific legal conclusion.

Sources

PostHog official sources

• PostHog pricing
• Product analytics documentation
• Group analytics
• Identifying users
• Funnels
• Retention
• Mobile session replay
• Creating feature flags
• Experiments
• Surveys
• Batch exports
• Privacy and data collection
• Self-hosting
• SDKs and libraries

Google official sources

• Google Analytics overview
• Analytics 360 overview
• Analytics 360 property limits
• Analytics 360 billing
• Analytics 360 service-level agreement
• Subproperties
• Roll-up properties
• Fresh Daily BigQuery export
• User-ID
• Firebase Analytics
• Explorations
• Funnel exploration
• Path exploration
• Cohort exploration
• BigQuery export
• BigQuery export types and reporting differences
• GA4 ecommerce
• A/B testing tools and Google Analytics
• Google Optimize sunset
• Consent mode
• Data retention
• Google Analytics privacy controls

Customer-review sources

• PostHog reviews on G2
• Google Analytics reviews on G2
• Google Analytics 360 reviews on G2

Video and independent-context sources

• Google Analytics vs PostHog — Vision Labs
• Vision Labs companion comparison
• What is PostHog? Official Demo & Tutorial