Open Source Analytics Platform
The consent banner that greets visitors before they read a word is the visible cost of hosted analytics; the invisible one is that a third party now holds a copy of everywhere those visitors went. The open source platforms here measure web traffic, product usage, and system health without shipping any of it off-site, so the record of who did what lives in a database you run rather than on a vendor's network. That ownership is what makes cookie-free, GDPR-friendly measurement realistic, and it is also what lets you keep raw events long enough to answer a question you had not thought to ask when you first set the tracking up.
Netdata
Sees every metric every second, then uses per-metric ML to flag what looks abnormal
Elasticsearch
Distributed RESTful search and analytics engine with vector search and near real-time indexing
Grafana
Open-source platform for monitoring and observability with dashboards, alerting, and mixed data sources
Apache Superset
Web-based business intelligence and data exploration platform with charts, dashboards, and SQL editing
ClickHouse
Open-source column-oriented OLAP database for real-time analytical reports with SQL
Metabase
Open source analytics for asking questions, building dashboards, and embedding BI
TiDB
Open-source distributed SQL database with ACID transactions, HTAP, MySQL compatibility, and vector search
DuckDB
In-process SQL OLAP database for analytical queries with CLI, language clients, and direct CSV and Parquet reads
Umami
Privacy-focused web analytics with self-hosting and a cookie-free alternative to Google Analytics
Our picks
The right pick depends on which kind of analytics you are after, so here is one strong option per job.
Privacy-first web analytics: Plausible Analytics Plausible tracks traffic, goals, conversions, and funnels without cookies or persistent identifiers, so there is no consent banner and the data stays compliant with GDPR, CCPA, and PECR. A tiny script keeps pages fast and supports single-page apps, and you can self-host the Community Edition or use the EU-hosted cloud.
All-in-one product analytics: PostHog PostHog combines event analytics, session replay, feature flags, experiments, and surveys in one stack, capturing events by autocapture or manual instrumentation and analyzing them with visualizations or SQL. Data pipelines send events on to other tools, webhooks, or a warehouse, and you can run it on PostHog Cloud or self-host with a one-line Docker deploy.
Self-serve BI and dashboards: Metabase Metabase lets non-technical people ask questions with a visual query builder while analysts drop into SQL, then turns the results into interactive dashboards with filters and alerts. It can schedule subscriptions to email, Slack, or a webhook and embed charts in your own app, running self-hosted or on Metabase Cloud.
Dashboards for system metrics: Grafana Grafana queries, visualizes, and alerts on metrics and logs wherever they live, mixing several data sources in one dashboard with template variables and ad-hoc exploration. Alert rules route to Slack, PagerDuty, and similar tools, which makes it the go-to when the thing you are analyzing is system health rather than customer behavior.
Sorting out which kind of analytics you need
The label analytics platform hides at least three different jobs, and this list contains all of them. Privacy-first web analytics like Plausible, Umami, or Matomo answer how many people visited and where they came from, with a light script and no cookie banner. Product analytics like PostHog, OpenPanel, or Countly go deeper into events, funnels, retention, and individual user journeys. BI tools like Metabase, Superset, and Redash sit on top of a warehouse and let analysts ask arbitrary SQL questions. Observability stacks like Grafana, SigNoz, and Netdata watch systems rather than customers. Deciding which of these you are shopping for narrows the field faster than any feature grid.
Once you know the category, look at where the data lands, because that decides what you can ask later. The privacy-focused web trackers store lightweight events and stay cheap, but they will not reconstruct a single user's path months on. Event-heavy product analytics keep raw events and lean on a columnar store, which is powerful but storage-hungry as volume and retention grow. If you already run a warehouse, a code-first layer such as Evidence or a dbt-native tool like Lightdash may fit better than standing up a second event store just to duplicate data you already have.
Finally, weigh the operational and governance work, since telemetry systems fail differently from ordinary apps. Test the boring paths before committing: restore a backup, replay a failed ingestion window without double-counting, and load-test the worst dashboard query rather than the demo. For anything touching personal data, check how consent, PII redaction, retention limits, and deletion requests are handled, because owning the data also means owning the responsibility for it, and that is precisely the part a hosted vendor used to absorb on your behalf.
Related categories
Frequently asked questions
What is the difference between web analytics, product analytics, and BI on this list?+
Web analytics tools like Plausible, Umami, and Matomo answer how many people visited and where they came from, with a light script and no cookie banner. Product analytics like PostHog, OpenPanel, and Countly track individual events, funnels, retention, and user journeys. BI tools like Metabase, Superset, and Redash sit on top of a database or warehouse and let analysts ask arbitrary SQL questions. Most teams eventually want more than one of the three.
Which of these can I run without a cookie consent banner?+
Plausible, Umami, Rybbit, GoatCounter, Ackee, and Swetrix all track without cookies or persistent identifiers, which is what lets them skip the consent banner and stay GDPR-friendly. They measure traffic rather than identifying individuals; GoatCounter, for instance, counts unique visits with a non-identifiable hash instead of a cookie. Confirm your own setup does not add custom identifiers that would pull consent requirements back in.
Why do so many of these run on ClickHouse?+
Event and telemetry data is append-heavy and queried with large aggregations, which is exactly what a columnar OLAP store is built for. Plausible, OpenPanel, SigNoz, Uptrace, and Swetrix all use ClickHouse for that reason, usually with Postgres alongside for configuration. ClickHouse appears on this list in its own right, so if you outgrow a packaged analytics tool you can query the same engine directly.
Can one tool replace both Google Analytics and Mixpanel?+
A few here aim to. PostHog and OpenPanel both combine a Google-Analytics-style traffic dashboard with Mixpanel-style event funnels, cohorts, and session replay in one platform. Rybbit similarly spans web and product analytics with funnels and user journeys. The trade is weight: these keep raw events and lean on a columnar store, so they cost more to run at high volume than a lightweight page-view tracker does.
Some of these are databases, not dashboards. Do I need one?+
Right, ClickHouse, DuckDB, Apache Druid, Apache Pinot, TimescaleDB, and QuestDB are the storage and query engines that analytics dashboards sit on, not turnkey tracking tools. You need one directly only if you are building your own analytics pipeline or already have raw events to query. If you just want traffic or product metrics, a packaged tool bundles the database for you.
Which of these fit a team that already runs dbt and a warehouse?+
Lightdash is built for exactly that: it reads metrics defined in your dbt project's YAML so business logic stays with the models. Evidence takes a code-first path, generating reports from SQL and markdown that you version like any other code. Metabase, Superset, and Redash also connect straight to a warehouse, so you can point them at existing tables without standing up a separate event store.
How do observability tools like Grafana or SigNoz differ from web analytics?+
Observability platforms watch systems, not customers. Grafana, SigNoz, Netdata, OpenObserve, and Uptrace collect logs, metrics, and traces to tell you why a service is slow or erroring, often through OpenTelemetry. Web and product analytics instead track human behavior such as visits, conversions, and retention. They share dashboards and sometimes a database, but you pick them to answer different questions, and many teams run one of each.