---
id: ch4-sovereignty
type: spine
title: "Not tied to one AI provider: JackDaw's mixed model strategy"
readingTime: 3
standalone: true
core: false
teaser: "Commercial APIs today, self-hosted and European research infrastructure prepared in parallel: why D4.2 hedges, and where data spaces still fall short."
voice: thinker
parent: null
diagram: null
recallQ: "Why does the PoliRural+ Beta platform prepare self-hosted and European LLM options alongside commercial cloud APIs?"
recallA: "Because the future cost of commercial AI services is uncertain and some cases need local deployment, lower cost, stronger data control or a choice of model. The Beta therefore uses OpenAI GPT services for stability, opens self-hosted models through Ollama and Llama CPP on CCSS infrastructure, and experiments with PSNC and CESNET LLM infrastructure."
highlights:
  - "Today: OpenAI GPT services where they give a stable path to users"
  - "Prepared: self-hosted Ollama and Llama CPP on CCSS infrastructure; experiments with PSNC and CESNET"
  - "Reason given: uncertain future cost of commercial AI, plus cases needing local control"
  - "Data spaces: D4.2 aligns with them but notes a gap between policy ambition and usable data"
status: accepted
concept: jackdaw
lang: en
lens: generic
depth: technical
formalism: none
visuality: text-first
lengthBand: standard
genre: explainer
state: edited
carriers: prose
---

Whose model answers a farmer's question, and on whose servers, is an architectural choice. D4.2 makes it deliberately, and without slogans.

## Three model routes, in parallel

**Commercial cloud, for now.** The Beta relies in part on mainstream cloud APIs, most notably OpenAI's GPT services, because they offer a practical and stable path to user-facing functions. For a Beta tested by pilots, D4.2 counts that stability as decisive.

**Self-hosted, prepared.** The platform also opens for self-hosted models through **Ollama** and **Llama CPP**, running on infrastructure of the partner CCSS. D4.2 lists the cases this serves: local deployment, lower operating cost, **stronger data control**, and experimental choice of models. The map's natural-language navigation bar already runs on a local Ollama model.

**European research infrastructure, experimental.** Integrations with the LLM infrastructures of **PSNC** and **CESNET** are being pursued, and the agent can already be pointed at OpenAI-compatible endpoints, including models hosted at PSNC.

## Why hedge

D4.2 gives two reasons. First, **the future cost of commercial AI services is uncertain**: many operate under pricing conditions that may shift. Second, smaller local models are improving quickly and already perform well on consumer hardware for many tasks, so edge and distributed processing are treated as credible future options rather than distant ideas. Preparing cloud, self-hosted and distributed routes in parallel keeps the AI layer adaptable and avoids complete reliance on one provider.

## Data spaces: aligned, with eyes open

On data, D4.2 aligns the platform with European Data Spaces and EOSC principles: resources should be discoverable, interoperable, reusable and reachable through machine-readable interfaces, so that API workflows and AI agents can use them, not only map viewers. Interfaces are defined towards the **Green Deal and Agricultural Data Spaces**, and the project uses **AIM** as a data model for agricultural data exchange.

D4.2 also says what many reports leave out: there is a **gap between policy ambition and operational availability**. Much effort has gone into frameworks and governance while broad, usable data access remains hard, which can resemble "an effort made before the data itself has reached sufficient critical mass". The platform positions itself to benefit as that ecosystem matures, without claiming maturity it does not have.

## The human layer

The pilots' policy brief (D7.3, Annex 10) adds that independence is not only about servers. It asks the Commission to **fund the human layer of AI** (a "data curation line" in AI project budgets) and to fund **multilingual AI sandboxes** for under-resourced languages such as Maltese and Latvian. A model you host yourself still answers badly in a language it was never tuned for.

The engineering consequence for [JackDaw](#c/jackdaw): keep the model swappable and the data well described, because both markets are still moving.

**Sources:**
- D4.2 PoliRuralPlus platform Beta, §2.3 AI integration layer and associated tech; §2.4 Data Spaces, EOSC and external infrastructure alignment; §3.3.1 Agent Architecture; §3.3.2 Map Component — [PDF](https://www.poliruralplus.eu/docs/421/D4.2_PoliRuralPlus_Platform_Beta.pdf)
- D7.3 Policy Brief No. 1, Annex 10: Strategic recommendations 1 and 4 — [PDF](https://www.poliruralplus.eu/docs/349/D7.3_Policy_Brief_No.1.pdf)