FOR AI SAAS TEAMS & AGENCIES
Build an AI product you can run on your customer’s infrastructure. [1]
Build and host an app in one managed workflow. [4]
Agents · Memory · Knowledge · Tools [1]
Self-hosted Runtime · Local models · Air-gap [2]
App integration and Runtime configuration required. Air-gap needs local models and dependencies. Commercial Runtime licensing applies. [10]
WHAT YOUR PRODUCT NEEDS
Read the scope and plan alongside each capability. These descriptions are not scores or a claim that every integration is automatic.
Your app provides UI, business logic and authentication integration. The Runtime supplies agents, knowledge, memory and tools. Evaluate the complete assembled application. [1]
Documents a managed app stack with hosting, authentication, database, storage and runtime AI/connector access. [4]
Configure model endpoints and connect Runtime agents, retrieval, memory and tools to your product. Native action controls apply on the integrated execution path. [1] [5] [3]
Managed AI and connectors can support app behavior. If moving away from the managed stack, inventory and replace the AI/connector dependencies your app uses. [4]
Make app journeys, grounding and action-denial checks part of acceptance. Runtime grounding metadata can inform checks; it does not prove whole-app correctness. [9] [3]
Documents browser testing, automated tests and direct backend verification. Confirm the coverage and evidence needed for your acceptance criteria. [8]
Self-host the Runtime, including air-gap with local generation, embeddings and compatible tools. External-only services and backend differences require planning. [2] [9]
Documents self-hosted frontend and backend migration paths. A complete move requires replacing managed AI/connectors and operating the required services. This is not proof of a ready-made offline AI Runtime. [4]
TWO DIFFERENT APPROVAL DECISIONS
Both questions matter. Review the enforcement boundary for each.
Enterprise workspace policies can require an owner/admin review or block publishing. Critical-security publishing settings are separate controls. Policy approval covers the app rather than a single immutable version. [11] [12]
The governed-action ledger records the approver and result, and the kernel refuses self-approval. Desk queues require integration; calling chat alone does not create an approval workflow. These action controls do not substitute for your release policy. [13] [14]
TRY THE SAME CUSTOMER TASK
Use this evaluation brief with either platform. Agree the required behavior and the test environment before you build.
Sign in, retrieve permitted product knowledge, retain the right user context and draft a reply. Require human approval for the agreed sensitive action.
Add an escalation rule. Repeat the original journeys and check that access boundaries and saved data still behave correctly.
Build from the delivered source, run in the agreed environment and verify backup and restore. Record model services, licenses and operating effort.
A working preview is one checkpoint. Acceptance also requires the critical journeys, permission checks and required data persistence to pass. Compare cost per accepted delivery, with human effort and ongoing usage recorded separately.
Confirm builder subscription, paid governance features, app hosting, model usage, commercial Runtime rights and engineering effort. This comparison does not establish a lower total cost for either product.
[10] [11] [4]Published by LibraOS. Official documentation reviewed . Selection guidance, not a performance or cost benchmark. Plans and versions can change.
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