posthog

Pros of Plausible

• Lightweight and privacy-focused, with no cookies and GDPR compliance
• Simple, clean interface with easy-to-understand metrics
• Open-source and self-hostable with minimal resource requirements

Cons of Plausible

• Limited feature set compared to PostHog's comprehensive toolkit
• Less customizable and extensible for complex analytics needs
• Fewer integrations with third-party tools and services

Code Comparison

PostHog (Python):

from posthog import Posthog

posthog = Posthog(project_api_key='your_api_key', host='https://app.posthog.com')
posthog.capture('user_id', 'event_name', {'property': 'value'})

Plausible (JavaScript):

plausible('event_name', {props: {property: 'value'}})

PostHog offers a more robust API with various client libraries, while Plausible focuses on simplicity with a straightforward JavaScript snippet. PostHog provides more granular control over event tracking and user properties, whereas Plausible emphasizes minimal code for basic event tracking.

Pros of Matomo

• More established project with a longer history and larger user base
• Offers both self-hosted and cloud-hosted options
• Stronger focus on data privacy and GDPR compliance

Cons of Matomo

• Less modern tech stack compared to PostHog
• More limited event-based analytics capabilities
• Slower development pace and fewer frequent updates

Code Comparison

Matomo (PHP):

$tracker = new MatomoTracker($idSite = 1, 'http://example.org/matomo/');
$tracker->doTrackPageView('Page Title');

PostHog (JavaScript):

posthog.capture('page_view', { page_title: 'Page Title' });

Both Matomo and PostHog are open-source analytics platforms, but they differ in their approach and target audience. Matomo (formerly Piwik) is a more traditional web analytics tool, similar to Google Analytics, while PostHog focuses on product analytics and offers features like session recording and feature flags.

PostHog uses a more modern tech stack (React, Django, ClickHouse) and has a stronger emphasis on developer-friendly features. It also provides a more comprehensive suite of product analytics tools out of the box.

Matomo, on the other hand, has a stronger focus on data privacy and compliance, making it a popular choice for organizations with strict data protection requirements. It also offers a wider range of plugins and integrations due to its longer presence in the market.

Pros of umami

• Lightweight and simple to set up, ideal for basic website analytics
• Privacy-focused with no cookies and GDPR compliance
• Free and open-source with a clean, minimalist interface

Cons of umami

• Limited feature set compared to PostHog's comprehensive analytics suite
• Lacks advanced user behavior tracking and product analytics capabilities
• Smaller community and ecosystem for extensions and integrations

Code Comparison

umami (JavaScript):

export async function getWebsiteStats(...) {
  return runQuery(
    prisma.website.findUnique({
      where: { id },
      select: {
        pageviews: true,
        uniques: true,
        bounces: true,
        totaltime: true,
      },
    })
  );
}

PostHog (Python):

def get_event_count_for_team(team_id: int, event_name: str, ...) -> int:
    return EventDefinition.objects.filter(
        team_id=team_id,
        name=event_name,
        deleted=False,
    ).aggregate(Count("id"))["id__count"]

Both repositories offer web analytics solutions, but they cater to different needs. umami is more focused on simplicity and privacy, while PostHog provides a more comprehensive suite of product analytics tools. The code snippets demonstrate the different approaches: umami uses JavaScript and focuses on basic website stats, while PostHog uses Python and offers more complex event tracking capabilities.