Standalone proof
Use the app's embedded engine and default model to prove one bounded workload on real endpoint hardware.
Every AI App can begin independently with its embedded AI Engine. Add AI Server when models, compute or APIs need to be shared; add AI Gateway only when several servers need one resilient front door.
AI Apps retain their embedded engine and local route even when they connect to the organisation platform shown below.
Use the app's embedded engine and default model to prove one bounded workload on real endpoint hardware.
Add one central model, compute or API service only when the business case needs it.
Add members, entitlements, server enrolment, policy and audit evidence when central governance matters.
Put AI Gateway in front of several server workers when resilience, specialisation or measured capacity requires them.
Deployment options include Windows, macOS, Linux, Docker and Kubernetes. The right topology depends on workload, hardware, identity, availability and compliance needs.
Connect compatible clients to chat, embedding, image, speech and vision services.
Place models and inference on infrastructure controlled by your organisation.
Manage members, licences, enrolment, policy, audit and usage evidence.
Move from one node to a gateway-managed farm as demand and evidence grow.
The deployment planner evaluates embedded apps, optional servers, Gateway, administration, hardware and paid licence quantities—then sends only orderable lines into the Store.