Hybrid AI: governing operations after model selection
Red Hat's 28 August guide extends model selection with a more structural question: who operates compute, model artifacts, inference, RAG, and agent tools once the system reaches production? For Belgian and French companies, that choice shapes sovereignty as much as cost and continuity.
1. Managed APIs, self-hosting, or hybrid
Red Hat distinguishes managed APIs, where the provider operates compute, model storage, and inference, from self-hosting, where those responsibilities remain within the company's environment. APIs reduce upfront investment and speed up delivery; self-hosting strengthens data control and may make high-volume costs more predictable, but requires GPU capacity, platform skills, updates, and incident response.
A hybrid architecture assigns every use case an explicit hosting mode. Exploratory or lower-risk flows may use an API, while regulated data or critical processing stays on controlled infrastructure. This allocation should document network paths, regions, private connectivity, logs, and the location of RAG indexes.
2. What changes for a Belgian or French company
For an SME, the concrete change is to separate the business need from technical prestige. An enterprise API may be sufficient for limited document assistance if retention, access, and budgets are controlled. Hosting a model becomes relevant when confidentiality, volume, latency, or continuity justify it and someone is accountable for operating the platform.
For mid-market companies, large groups, and public administrations, every workload needs an owner, a data boundary, a risk tier, an availability objective, and a fallback plan. IT teams must budget for concurrency and token peaks rather than averages, then monitor latency, errors, queues, cost, retrieval quality, and tool calls.
3. Underside analysis: sovereignty is decided on Day 2
Underside's analysis is that model location is not enough. Sovereign AI must also be observable, correctable, replaceable, and stoppable. The model identifier, prompts, RAG index, and tool configuration should be versioned as one release unit so promotion and rollback preserve a coherent state.
Continuity planning must cover ordinary failures: unavailable APIs, lost GPUs, regressed models, or broken business tools. Degraded modes may include a smaller model, cached responses, read-only agents, or temporary suspension of actions. This operational fallback contributes more to real sovereignty than a regional promise alone.
4. Agents, MCP, and Odoo: governing actions
RAG adds ingestion, embeddings, indexes, document permissions, and retrieval metrics. An agent adds orchestration, a service identity, an allowlist of tools, timeouts, input validation, and invocation logs. MCP makes tools easier to expose, but it does not replace authentication, role-based filtering, or audit.
In Odoo Enterprise, an assistant reading documents can remain a systems-to-AI flow. An agent creating a quotation, changing inventory, or triggering a payment reverses the control path by calling the information system. Companies should therefore separate read and write permissions, restrict every service account, require human approval for consequential acts, and connect each action to an audit-ready correlation ID.
5. An actionable decision framework
The right decision is not one platform for every workload. Use cases should be classified by sensitivity, criticality, volume, latency, vendor dependency, and internal skills. The matrix should also cover Apple Enterprise endpoints, European clouds, local environments, and business integrations: data can remain local while inference is remote, or the reverse.
Operational priority: for each use case, document the model, hosting mode, data paths, RAG index, agent tools, SLOs, costs, logs, degraded mode, owner, and rollback procedure.
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