hyperloglog VS PostHog

/api/report - Used for observability and diagnostics (e.g. per-feed status, cluster counts, errors). Logs and metrics go to Axiom so I can trace ingest runs and spot failures without digging through Vercel logs.

This was our biggest operational change. Last year I praised Axiom for logs. It was great, on the base plan. Until we scaled.

Axiom | Senior Design Engineer | REMOTE (UTC-5 to UTC+1) | Full-time

Axiom (https://axiom.co) is used by thousands of great companies like Asana and Framer for logging and observability. Our underlying tech is best-in-class, but we have a much higher ambition for the craft and quality of our user experience.

We’re looking for a brilliant design engineer who cares deeply about UI craft. You’ll be a critical bridge between design and engineering, creating the systems and interfaces that reimagine entire product categories.

- Stack including React, TypeScript, TanStack, Tailwind

To keep tabs on what’s happening, we use Axiom for logs and Grafana for dashboards and alerts. We pipe all of our logs from most services straight into Axiom and have some pre-defined filters there that help us understand whats going on. With Grafana we have a bunch of custom dashboards that help us triage issues, and more importantly, alert us if some metrics are looking off. Without Grafana, Sliplane could not exist!

I've heard good things about Axiom[0], especially for high scale needs.

0: https://axiom.co/

If your app is instrumented with OpenTelemetry, I'd really recommend something like SigNoz (https://signoz.io/) or Axum (https://axiom.co/) (or even Baselime.io but their application is a mess to work with)

1. Axiom has a generous free plan (not sure if it's self-hostable)

Great choice of monitoring and analytics tools (Sentry, Axiom, Posthog and Uptime Kuma) coupled with amazing Slack integrations that allowed us to iron out any issues way before the traffic spike while the troubling features were still fresh from the oven.

For the last 1.5 years, I have been using Axiom for all of my logs ingestion, querying, and monitoring needs. It is a great product and I never had one issue with it in my time using it. Spoiler alert, even today, when it failed it was actually my fault, but let's see what happened.

Axiom

This is the same model that PostHog, Supabase, and dozens of other developer tools use. Open core, with a managed offering on top.

Offchain: Website traffic, in-app behaviour, marketing channels, growth campaigns (Google Analytics or PostHog).

Topic PostHog (Web Vitals) Apogee Watcher Primary job Product analytics OS; Web Vitals are real-user metrics from the browser Synthetic PageSpeed monitoring + CrUX in results Instrumentation Requires posthog-js on the site No script on monitored sites Metrics FCP, LCP, INP, CLS from real sessions ($web\_vitals) when capture runs Lighthouse lab + CrUX (where available) via PSI Cookieless analytics With always (and cookieless paths without session IDs), $web\_vitals does not populate—see Cookieless mode above No snippet; tests do not use PostHog session state Best for “How do my users experience my app?” “How are these URLs doing on a schedule—and across many clients?” Multi-site agency Analytics projects and teams—not Watcher’s org/site/page model Multi-tenant orgs, roles, discovery, budgets Budgets & email alerts Insight-based—build trends from $web\_vitals, attach [alerts](https://posthog.com/docs/alerts) with thresholds, frequency, destinations; maintain as analytics evolves Monitoring-native—performance budgets and email alerts tied to scheduled tests; no separate insight to curate first Extras Flags, replay, experiments, cohorts, warehouse pipelines PDF-style reporting direction, Leads prospecting workflows Cost shape Event-based (vitals count toward event quotas) Plan-based subscription; PSI quota bundled—verify [pricing](https://apogeewatcher.com/pricing)

PostHog provides an open-source analytics approach, giving teams flexibility to customize dashboards, session recordings, and heatmaps.

Originally built for Web2, PostHog can be extended with Web3 event tracking through custom data pipelines. Key features of Posthog are funnels, session recordings, feature flags, open-source & self-hosted

A big thanks to PostHog who pioneered this approach with HogQL, a SQL-like interface on top of ClickHouse. TRQL started as a TypeScript conversion of their Python implementation but evolved significantly during development to handle our specific use cases.

Plausible brought open source to web analytics. Cal.com did it for scheduling. Formbricks did it for surveys. PostHog did it for product analytics. Quackback does it for feedback collection.