Best Open Source Analytics Tools in 2026
Best Open Source Analytics Tools in 2026
Google Analytics is free but collects enormous amounts of user data and requires cookie consent banners. GA360 costs $150K+/year. Here are the best open source analytics tools that respect user privacy.
Quick Comparison
| Tool | Focus | License | Script Size | Cookie-Free | Self-Host |
|---|---|---|---|---|---|
| Plausible | Web analytics | AGPL-3.0 | <1 KB | ✅ | Docker |
| Umami | Web analytics | MIT | 2 KB | ✅ | Docker |
| Matomo | Full analytics suite | GPL-3.0 | 22 KB | ❌ (optional) | Docker |
| PostHog | Product analytics | MIT | 67 KB | ❌ | Docker |
| OpenPanel | Web + product | MIT | 5 KB | ✅ | Docker |
Detailed Breakdown
Plausible — Best Lightweight Analytics
Replaces: Google Analytics (for marketing teams)
- Cookie-free, GDPR compliant by default
- <1 KB script (45x smaller than GA)
- Real-time dashboard
- UTM campaign tracking
- Custom events
- Goals and conversions
- Google Search Console integration
- Email reports
What you get: Pageviews, visitors, bounce rate, visit duration, referrers, locations, devices — the essential metrics without the noise.
What you don't get: User-level tracking, funnels, session recordings, heatmaps.
Best for: Marketing teams, content sites, SaaS landing pages. Anyone who wants clean analytics without complexity.
Resource: 1 GB RAM, 1 core.
Umami — Best Free Alternative
Replaces: Google Analytics (for developers)
- Cookie-free, GDPR compliant
- Custom events via data attributes
- Shareable dashboard links
- Multiple website tracking
- Real-time data
- Team accounts
- MIT license (most permissive)
Advantage over Plausible: Completely free (no paid cloud tier). MIT license vs AGPL.
Best for: Developers who want analytics on side projects with zero cost.
Resource: 512 MB RAM, 1 core.
Matomo — Most Complete GA Replacement
Replaces: Google Analytics (full feature parity)
- Page analytics, events, goals
- Ecommerce tracking
- Heatmaps and session recordings (premium)
- A/B testing
- Form analytics
- Funnels and cohorts
- Tag manager
- Roll-up reporting (multi-site)
- GDPR compliance tools
- Import Google Analytics data
Best for: Organizations that need full Google Analytics feature parity, especially with ecommerce tracking.
Resource: 2 GB RAM, 2 cores.
PostHog — Best Product Analytics
Replaces: Mixpanel, Amplitude, Hotjar
- Event tracking and funnels
- Session recordings
- Feature flags
- A/B testing
- User paths
- Retention analysis
- SQL access to raw data
- Notebooks (data exploration)
Different from web analytics: PostHog is for understanding how users interact with your product, not just pageviews.
Best for: Product teams who need Mixpanel-level analytics with session recordings.
Resource: 4 GB RAM, 4 cores.
Choosing the Right Tool
| Need | Choose |
|---|---|
| Simple, clean web analytics | Plausible |
| Free, developer-friendly | Umami |
| Full GA replacement | Matomo |
| Product analytics + feature flags | PostHog |
| Ecommerce tracking | Matomo |
| Session recordings | PostHog |
| Smallest script size | Plausible (<1 KB) |
Privacy Comparison
| Feature | GA4 | Plausible | Umami | Matomo |
|---|---|---|---|---|
| Cookie-free | ❌ | ✅ | ✅ | Optional |
| GDPR consent needed | ✅ | ❌ | ❌ | Optional |
| Data ownership | You | You | You | |
| IP anonymization | Optional | Default | Default | Default |
| Script size | 45 KB | <1 KB | 2 KB | 22 KB |
Cost Savings
| Scenario | GA360 | Self-Hosted Plausible | Savings |
|---|---|---|---|
| Up to 1M pageviews | $150K/year | $54/year (VPS) | $149,946 |
| Marketing team | $9/month (Plausible Cloud) | $5/month (self-hosted) | $48/year |
For most sites, Plausible Cloud at $9/month or Umami self-hosted for free replaces GA entirely.
Self-Hosting Deployment Guide
Self-hosting an analytics stack is more approachable than many developers expect. All five tools publish official Docker images and maintain detailed setup documentation. The practical difference comes down to database requirements and server resources.
Plausible and Umami are the easiest to deploy. Plausible relies on ClickHouse for its fast aggregation queries — ClickHouse handles billions of rows efficiently and is purpose-built for time-series analytics. You will want at least 1 GB RAM dedicated to ClickHouse alone, with another 512 MB for the Plausible application container. On a Hetzner CX21 (2 cores, 4 GB RAM, €5.83/month) you will have comfortable headroom. Umami is even lighter: it uses PostgreSQL or MySQL, runs comfortably on 512 MB RAM, and a single 2 GB VPS handles millions of monthly pageviews without breaking a sweat.
Matomo requires more thought at setup. The default installation uses MySQL or MariaDB and PHP, which you can run through their official Docker Compose stack. For high-traffic sites, Matomo recommends enabling their MariaDB archiving cron job — this pre-aggregates reports so the dashboard loads quickly rather than querying raw events on every page view. Without archiving configured, dashboards slow down noticeably above a few million monthly pageviews.
PostHog is the heaviest lift. The self-hosted version runs ClickHouse, Kafka, Redis, PostgreSQL, and several application services. Minimum comfortable deployment is a 4-core, 8 GB RAM server. PostHog recommends their cloud for teams under 1 million monthly events if server costs are a concern, since a server capable of running the full stack costs $40–80/month — comparable to their Growth tier for small teams. That said, self-hosted PostHog gives you SQL access to raw event data, which is worth the investment for data-mature product teams.
Integrating Analytics Into Your Observability Stack
Analytics tools do not operate in isolation. A mature self-hosted stack typically layers web analytics on top of infrastructure and application monitoring. Plausible or Umami handles business metrics — pageviews, conversions, traffic sources. A tool like Grafana and Prometheus handles server-level metrics — CPU, memory, request latency. PostHog handles product analytics — user paths, feature flag results, session recordings.
These layers complement each other. When a marketing campaign drives a traffic spike, Plausible captures the source and conversion rate. Grafana alerts you if that same spike degrades API response time. PostHog shows whether users who arrived from that campaign actually completed the key product action. Running all three gives you the full picture without paying Mixpanel, Datadog, and GA360 licensing fees.
For teams already running a self-hosted Grafana stack, both Plausible and Matomo expose Prometheus-compatible metrics endpoints. You can pull analytics data — pageview counts, active user counts, goal completion rates — directly into Grafana dashboards alongside your application metrics, creating a unified operational view.
Security and Access Control
All five tools ship with basic authentication. For teams deploying in a business context, you will want to layer additional access controls on top of the defaults.
The most important configuration step is placing analytics behind a reverse proxy — Caddy or Nginx — and terminating TLS there. Do not expose analytics containers directly on port 80 or 443 without a proper reverse proxy handling SSL certificates, ideally through Let's Encrypt auto-renewal.
Plausible and Umami both support multiple sites and multiple users, but they are not designed as multi-tenant platforms with fine-grained per-site permissions. If you need strict separation — for example, a digital agency managing analytics for multiple clients where each client should only see their own data — Matomo's fine-grained permissions model is more suitable. Matomo lets you assign users to specific sites with read-only, write, or admin access.
PostHog has the most sophisticated access control. Its organization and project structure allows you to separate product analytics by team or product line, restrict access to raw event data to specific roles, and manage personal data deletion requests through a built-in privacy compliance workflow. This matters for regulated industries where GDPR data subject access requests are a real operational concern.
For all tools, consider running them on a private network with your main application server rather than on a publicly accessible subdomain if the analytics data is sensitive. A private Wireguard or Tailscale network connecting your analytics server to your application servers keeps the data pipeline off the public internet entirely.
Community Health and Project Sustainability
Choosing an analytics tool you will self-host for years means evaluating whether the project will still be actively maintained when you need a security patch or a new feature. All five tools covered here have strong fundamentals, but they differ in governance model and funding.
Plausible is a bootstrapped, profitable business. The team of roughly a dozen people funds development through their cloud product. This means the self-hosted version may occasionally lag behind the cloud tier on features — Plausible has explicitly said their cloud comes first — but the project is financially sustainable without relying on venture capital. The AGPL license means any commercial fork must publish its changes.
Umami is maintained by a small team with a cloud offering as well. The MIT license is more permissive than Plausible's AGPL, which matters for embedding analytics into commercial products. The project has a growing contributor base and moves quickly. The risk is that a small team with a permissive license has less financial moat, but Umami has maintained consistent releases for several years.
Matomo is the oldest project in this list — it began as Piwik in 2007 and has a long track record of stability. Matomo SAS, the company behind it, sells premium plugins and a hosted version. The core GPL codebase has over 100 contributors. This is the safest choice from a longevity standpoint for enterprises that need a conservative, auditable track record.
PostHog raised significant venture funding and is one of the best-funded open source companies in the developer tools space. Their team is large, the release cadence is high, and they maintain excellent documentation. The MIT license on the self-hosted version is genuinely permissive. The risk is that VC-backed companies occasionally change business strategy — but PostHog has been consistent in their open core model and have a strong community following.
For teams that want to go deeper on the analytics landscape, our guide to open source alternatives to Google Analytics covers privacy-first options in more detail, including tools focused on server-side event collection for cookie-free tracking architectures.
Migrating From Google Analytics to a Self-Hosted Alternative
The migration from Google Analytics to a self-hosted tool is simpler than most teams expect. The primary concern is historical data — you will not carry your GA4 data over to Plausible or Umami. Matomo is the only tool in this list that supports importing Google Analytics history, which makes it the right choice if preserving years of historical pageview data is a hard requirement.
For teams that can accept a clean break, the migration process is: deploy your chosen tool, replace the GA script tag with the new analytics script, verify events are being captured correctly, and remove GA. The new tool collects data from day one going forward. Within 30 days, your dashboard has a full month of data and most reporting needs are met. Within 90 days, you have enough data for meaningful trend analysis.
The hardest part of the migration is typically internal — getting stakeholders who have used GA for years to trust a different dashboard. Plausible's clean, simplified metrics are often a feature for leadership but can feel like a downgrade to analysts who relied on GA's audience segmentation. Set expectations before migrating: what you gain in privacy and control, you trade for some of GA's advanced segmentation. For most organizations, that is a worthwhile trade.
For teams running privacy-first analytics alongside other open source tools, it is worth reviewing the full best open source CRM software options — CRM and analytics data complement each other, and self-hosting both means complete customer data ownership without piecing together cross-tool data sharing through third-party APIs.
Compare all analytics tools on OSSAlt — privacy, features, and script sizes side by side.
See open source alternatives to Google Analytics on OSSAlt.