Product analytics lives or dies on one thing: who holds the raw stream of events that describes what your users do. A hosted platform makes that stream easy to slice into funnels, paths, and retention, but the events themselves sit in the vendor's system, governed by its pricing tiers and its definition of an active user, and pulling years of history back out to analyze your own way is never as clean as sending it in. Open source analytics keeps those events in a store you control. Funnels and cohorts are computed from the raw data rather than a vendor's rolled-up summary, you set your own retention, and the analysis answers to your questions instead of a plan limit.
Umami is a simple, fast, privacy-focused web analytics platform and an open source alternative to Google Analytics, Mixpanel, and Amplitude. It tracks site traffic without cookies, so there is no cookie banner to add, and the dashboard stays clean and easy to read.
PostHog is an open source, all-in-one platform for building products. It brings product analytics, web analytics, session replay, error tracking, feature flags, experiments, surveys, a data warehouse, data pipelines, AI observability, and workflows together in one stack.
Countly is a digital analytics and customer engagement platform for understanding user behavior across mobile, web, desktop, and connected devices. It keeps data on infrastructure you control, with on-premises or private-cloud deployment for teams that need full data ownership.
Pick by how deep your analysis needs to go.
For a full product-analytics stack: PostHog PostHog is the closest replacement for how teams use Amplitude, combining autocaptured or manually instrumented product analytics with session replay, feature flags, experiments, surveys, and data pipelines in one system. You can query events with visualizations or SQL, and it runs self-hosted on Linux with Docker or on PostHog Cloud in US and EU regions. Pipelines can filter, transform, and export events to other tools, webhooks, or a warehouse, so it fits teams that want ownership without giving up a real product-analytics UI.
For mobile-focused behavioral analytics: Countly Countly is a self-hosted product-analytics and customer-engagement platform that tracks sessions, events, and crash reports across mobile, web, and desktop, then adds push notifications, remote config, and dashboards on top. Its Compliance Hub handles consent and data-subject requests, and the self-hostable Countly Lite runs on MongoDB and Node.js. It suits teams whose analytics center on mobile apps and who want engagement tools alongside the numbers.
For privacy-friendly web analytics: Umami Umami is a simpler, faster fit when you mainly want site metrics rather than deep behavioral analysis. It tracks traffic without cookies, so there is no consent banner, and it presents everything on one clean dashboard. It self-hosts on Node.js with a PostgreSQL database, or through the official Docker image, keeping every visitor record in a store you own; a hosted Umami Cloud is available if you would rather not run it yourself. The code is MIT licensed.
Map the Amplitude behaviors your teams actually reach for, not the screens they recognize. The hard parts are event semantics, identity resolution, cohort definitions, funnel logic, and the governance around who can create or change a metric, and a replacement has to match your product questions before it matches every chart type. It also helps to be honest about depth. PostHog is the closest match to Amplitude, pairing event-based product analytics, session replay, feature flags, and experiments in one self-hostable stack. Countly sits in the middle, a self-hosted product-analytics and engagement platform with event, session, and crash tracking and mobile SDKs, closer to Amplitude's behavioral focus than to plain web stats. Umami is the lightest of the three, a cookie-free tool better suited to traffic and conversion than deep behavioral exploration.
Expect gaps in self-serve breadth. Amplitude is tuned for non-SQL product exploration - fast funnel slicing, path analysis, reusable cohorts, and collaboration around saved analyses - and an open source move may ask for more schema discipline or more engineering involvement. Some teams accept that because they want raw event ownership and predictable infrastructure. The piece people most often miss is not a chart type but the shared metric layer that keeps product, growth, and support teams arguing from the same definitions.
The move usually begins with a historical export from Amplitude, a backfill into the new store, and a period of dual-writing from your SDKs so you can compare the same production traffic in both systems. Raw events, timestamps, event and user properties, and identifiers can survive if you export them carefully; dashboards, notebooks, saved cohorts, and permissions are application objects you will mostly rebuild and validate. The real cleanup is identity mapping, timezone handling, deduplication, and reconciling new funnel counts against the Amplitude baseline before anyone depends on them.
PostHog, in most cases. It is built around event-based product analytics with funnels, cohorts, session replay, feature flags, and experiments, which lines up with how teams use Amplitude, and it runs self-hosted or on PostHog Cloud. Countly is the next closest, a self-hosted product-analytics and engagement platform with strong mobile support. Umami is the simplest, cookie-free web analytics for traffic, sessions, and conversion. Reach for Umami for privacy-friendly site metrics, Countly for mobile-heavy behavioral analytics, and PostHog for the fullest product analysis.
Not automatically. You may stop paying a bill tied to event volume, but you take on storage, compute, upgrades, monitoring, backups, and staff time. The math improves when you already run data infrastructure or have high, predictable event volume. PostHog's self-hosted hobby deploy is sized for roughly 100,000 events a month, so heavy workloads still need real capacity planning. If you need a polished product-analytics UI with little engineering support, total cost can move sideways.
Amplitude can export event data, including raw events with timestamps, event properties, user properties, and identifiers. What comes back is the data, not the product experience, so charts, notebooks, dashboard layouts, formulas, permissions, and some cohort logic have to be rebuilt. Before exporting, freeze a date range, write down your timezone assumptions, and capture baseline counts for the funnels and retention reports you care about most so you can check the new system against them.
Identity is the place to be most careful. Amplitude may combine user IDs, device IDs, and its own internal identifiers in ways that shape funnels, retention, and user counts, so export enough identifier fields to reconstruct the join rules and decide how the new system resolves anonymous-to-known transitions. Validate with a handful of real user journeys, not just aggregate totals, because an identity mistake can quietly make conversion and retention look wrong.
Cohorts rarely move as reusable objects. You can often export membership or recreate the rules, but the new tool may define time windows, property filters, and identity differently. For the cohorts that matter, write the behavioral rules down in plain language, export a sample membership list from Amplitude, and compare results after rebuilding. If a cohort feeds messaging, experiments, or sales workflows, run it in parallel before switching those downstream uses.
Yes, if the analytics are business-critical. Dual-writing sends the same production traffic to Amplitude and the new stack so you can compare before anyone depends on the replacement. Keep the overlap long enough to cover weekly usage patterns and major flows, and watch for missing events, timestamp drift, property type mismatches, and identity gaps. Once your key reports reconcile within an agreed tolerance, freeze new Amplitude dashboard work and move teams across.
The gaps tend to be polish, guided exploration, and collaboration built for people who do not write SQL. Funnel and retention analysis exist in tools like PostHog, but pathing, anomaly workflows, and stakeholder-friendly notebooks can be thinner or need more setup. The trade is control: you gain ownership of event data and infrastructure while accepting that product managers may lean more on analytics engineers. Weigh which analyses truly have to stay self-serve.
