Plausible Analytics
Plausible Analytics is lightweight, privacy-friendly web analytics you can self-host. It measures traffic without cookies or personal-data collection, which keeps most visitors outside consent-banner territory, and presents everything on a single fast dashboard. The script it adds to your pages is a fraction of the size of the mainstream alternative, and the Community Edition runs on your own server.
View the repo · plausible/analytics ↗Teams that want clean traffic insight without heavy tracking, and firms whose privacy stance or client base makes cookie banners and third-party analytics an awkward fit. It suits marketing sites, content sites and products that need trends, sources and goal conversions rather than user-level behavioural profiles. Teams built around deep ad-platform integration and remarketing audiences will find it deliberately does not do that.
Install it# follow the official self-host guide:
# https://github.com/plausible/community-editionSelf-hosting the Community Edition means running its ClickHouse and Postgres dependencies, which is more infrastructure than the tiny tracking script suggests, so follow the official guide and keep the stack updated. Define your goals and custom events early, because the numbers only become decisions once conversions are tracked. Confirm your own regulatory position too: cookie-free analytics reduces the consent burden in most cases without abolishing your privacy obligations.
Where Blash AI comes inWe set it up, define the events that map to enquiries and bookings, and feed the numbers into the same reporting layer as the rest of the business, alongside Metabase dashboards where deeper analysis is needed. For a site whose bookings run through Cal.com and whose follow-up runs through n8n, Plausible closes the loop by showing which sources produce the enquiries that convert.
When you want this running on your real stack, that is the engagement
Book an AI audit →n8n →
n8n is workflow automation you run on your own infrastructure. It connects the applications a business already uses through a visual editor, with hundreds of prebuilt nodes, a code step for anything bespoke, and native support for calling language models mid-flow. Think of it as a self-hosted alternative to Zapier or Make that you own outright, including every piece of data passing through it.
Ollama →
Ollama runs open-weight language models on your own machine or server. One command downloads a model and serves it behind a local API that most existing AI tooling can talk to, so an application built against a hosted provider can point at your own hardware instead. Sensitive data stays inside your network, there are no per-token fees, and it keeps working with no internet connection at all.
LiteLLM →
LiteLLM is a gateway that lets you call models from Anthropic, OpenAI, Google and many other providers through one consistent API. It runs as a Python library inside your code or as a standalone proxy server in front of your whole stack, with cost tracking, per-key budgets, rate limits and automatic fallbacks for when a provider has a bad day.