Building a service (Preview)

This page describes features which are only relevant when running Timefold Solver as a Service (Preview). The information on these pages may describe functionality which may be changed or even removed in a future release.

Run dataset optimization as a fully isolated service. This opinionated approach builds on Quarkus and eliminates the boilerplate typically needed to expose a solver over a REST API.

If you haven’t done so yet, start with the Getting started: building a service guide.

1. Foundations still apply

Building a service does not replace the core Timefold Solver concepts, it builds on top of them. Everything covered in the following sections is still relevant and applies directly to your service model:

Using Timefold Solver: How to model your planning problem, configure the solver, and understand the solving lifecycle. This knowledge is required regardless of whether you embed the solver as a library or run it as a service.
Constraints and Score: How to define hard and soft constraints, choose a score type, and analyze solution quality. Constraint streams and score calculation work identically in a service model.

The pages in this section cover only what is specific to running the solver as a service: the REST API contract, model enrichment, constraint weight overrides, demo data, and metrics.