Umami

Umami — analytics and business intelligence platform.

AnalyticsSimple

Stack Components

AnalyticsUmami

Trackingscript tag

PrivacyCookieless

Why

Data-driven product decisions through event tracking, funnels, and dashboards. PostHog and Plausible offer privacy-first analytics without GDPR consent banners, growing 200% in 2024.

BigQuery and ClickHouse process petabyte-scale data at $5/TB scanned.

When

Use for product analytics, user behavior, and BI reporting. Avoid for simple page-view counting (use Plausible).

How

1. Create Umami account/project 2. Add tracking SDK 3. Define events/metrics 4. Build dashboards 5. Add environment configuration — cp .env.example .env && edit values 6. Run tests — npm test or pnpm test to verify setup 7. Deploy — git push to trigger CI/CD or manual deploy

Example

// Umami tracking analytics.track('button_clicked', { id: 'signup' }); // Umami — usage example import { Umami } from "analytics-umami"; const result = await umami({ input: "data" }); console.log(result);

Common Problems

⚠Umami event volume explosion — sample high-cardinality events
⚠GDPR consent not enforced — gate tracking behind consent banner
⚠Configuration drift between environments — use infrastructure-as-code (Terraform/Pulumi) to keep dev/staging/prod identical

Best Practices

✓Define a consistent event taxonomy before implementing Umami
✓Use server-side tracking for accuracy and ad-blocking resilience
✓Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit

FAQ

What is Umami and what projects is it for?

Umami — analytics and business intelligence platform. It pairs layered technologies for end-to-end type safety and out-of-the-box deploys, fitting everything from MVPs to production SaaS.

How do I spin up a working demo with Umami in 10 minutes?

Two steps: (1) Create Umami account/project; (2) Add tracking SDK. Get the local dev server running, then wire one real data source to verify the end-to-end loop.

What is the most common pitfall with Umami and how do I avoid it?

The most common pitfall is: Umami event volume explosion — sample high-cardinality events. Avoid it by adding the matching checklist to your PR template and running type-check + lint + unit tests in CI — three gates before merge.

What are the best practices for Umami?

Best practices for Umami include: Define a consistent event taxonomy before implementing Umami; Use server-side tracking for accuracy and ad-blocking resilience; Set up health checks and graceful shutdown — handle SIGTERM to drain in-flight requests before exit. The first one — "Define a consistent event taxonomy before implementing Umami" — should be set up at project init to avoid costly rework later.

When should I NOT choose Umami?

Pick something else in three cases: (1) the team has zero experience with the core framework and the deadline is tight (learning-curve risk); (2) it's a pure content/marketing site (Astro is lighter); (3) you need very high throughput (>10k RPS) or strict-consistency microservices (Go/Rust). Umami shines for mid-to-high complexity full-stack and API work — don't over-engineer simple static pages with it.