Cal.com
Open-source scheduling you can self-host, embed in your own product and connect to your own calendar stack. The self-hostable codebase now lives under the Cal.diy name, described by its maintainers as scheduling infrastructure for absolutely everyone, while Cal.com continues as the hosted service. You get booking pages, round-robin and team scheduling, and an API for driving it all programmatically, with booking data kept inside your own systems.
View the repo · calcom/cal.diy ↗Teams that want booking embedded in their own product or site under their own domain, and firms whose client-data policies rule out a third-party scheduling cloud. It suits businesses with engineering capacity, because the self-hosted deployment is a full web application with a database behind it. A small team that just needs a booking link will be better served by a hosted scheduler until control of the data becomes the deciding factor.
Install itgit clone https://github.com/calcom/cal.diy.git
cd cal.diy && yarn && yarn devThis is a substantial Node.js application with a Postgres database, calendar OAuth integrations and email delivery to configure, so treat self-hosting as running a product rather than installing a utility. Calendar credentials for Google and Microsoft need their own app registrations and verification steps. Keep upgrades regular, since a public booking page is internet-facing by definition, and test the timezone behaviour with real invitees before pointing clients at it.
Where Blash AI comes inScheduling is rarely the whole job. We embed the booking flow, wire confirmed bookings into your CRM through n8n, and add the qualification before the call and the follow-up after it, so every booking triggers the same pipeline. For the client paperwork that follows the meeting, it pairs naturally with Documenso from this library.
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.