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Where do your documents and prompts go? The answer depends on three separate choices: the editor you use, where files are stored, and which AI backend processes each request. Open-source licensing can make code inspectable, but it does not by itself prove that an AI operation is local or private. A desktop editor may open a file entirely from your computer while sending an AI request to a hosted provider; a self-hosted office server may keep files on your infrastructure while still using a remote model.
The three layers that determine a document’s data path
Think of an AI office workflow as three connected layers. Auditing only one of them produces an incomplete privacy answer.
1. The editor
The editor is the application that opens, changes or generates a document. ONLYOFFICE Desktop Editors can open files stored on the computer and perform basic document work without a constant internet connection. That describes the document editor’s operating mode, not the destination of every AI request. Its desktop guide includes an AI Agent tab where you select an assistant provider, so an offline-capable editor can still be configured to call a remote service.
2. Storage and collaboration
A file can remain on a local computer, be stored on a self-hosted service such as Nextcloud, or sit with a third-party host. In a Nextcloud deployment using the ONLYOFFICE connector, the document server connects to Nextcloud for editing and collaboration. The file’s storage location, the editor-server location and the route between them are separate administrative decisions.
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3. The AI backend
Summarization, rewriting, translation and other model features can run on a local machine, an on-premises server or an external provider. Nextcloud’s AI documentation lists local large-language-model options, LocalAI, Ollama, the OpenAI API and other hosted integrations. The manual distinguishes options that keep data on premises from those that do not. Therefore, the selected backend—not the word open source alone—determines where model processing occurs.
Documentation establishes that these choices are configurable, but it does not establish one universal payload for every feature. Do not assume that a complete file is always uploaded, or that only a highlighted passage is sent, unless the implementation documentation for that exact operation says so.
What open source tells you—and what it does not
Open-source software generally gives you licensing rights and the ability to inspect, modify or redistribute code under its license. It does not automatically answer four operational questions:
- Where is the document stored while you edit it?
- Where does inference run?
- Which service receives prompts or document content?
- Who patches, secures and administers the servers?
A self-hosted application can still be configured to use a hosted model. Conversely, a local model can be connected to software whose collaboration server is hosted elsewhere. Treat “open source” as one property in a larger data-flow map, not as a privacy certification.
How Nextcloud’s AI choices change the result
The Nextcloud 35 Administration Manual presents several AI configurations rather than one fixed privacy profile. Its table covers text processing through a Local large language model 2 ExApp and integrations with LocalAI, Ollama, the OpenAI API and other providers. Some approaches are marked as keeping data on premises; others are marked as not doing so. The same platform can therefore produce materially different data paths depending on the backend an administrator enables.
Local large-language-model options
A Local large language model 2 ExApp or a locally deployed LocalAI or Ollama service can keep inference within infrastructure you control when the model, runtime and network are actually hosted there. That arrangement reduces dependence on an outside inference provider, but it does not remove administrative responsibilities. You still control access, logging, updates, backups and the machine that runs the model. Hardware capacity and model suitability vary; the Nextcloud documentation does not prescribe a universal computer configuration.
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Hosted API integrations
Choosing an external API, such as the OpenAI API integration listed in the manual, means the request crosses the boundary of your own deployment to that provider. The provider’s current contract, retention terms, regional processing and training policy must be checked separately. The product documentation identifies the integration and its on-premises classification; it does not settle the legal or retention terms of every provider.
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Nextcloud’s ethical-AI rating
Nextcloud describes an ethical-AI rating based on three questions: is the inference software open source, is the trained model available for self-hosting, and is training data freely available? Its scale assigns green when all three criteria are met, red when none are met, orange when one is met and yellow when two are met. This is Nextcloud’s comparison framework, not an independent security certification or an audit of a particular installation.
Document generation in the Assistant app
Nextcloud’s manual notes that, since Hub 11, the Assistant app can generate office documents with content using the Nextcloud Office app. That capability is version-specific. Confirm the Hub and app versions in your deployment and identify the backend attached to the Assistant before sending confidential material.
What ONLYOFFICE tells you about local and cloud work
ONLYOFFICE Desktop Editors is an open-source suite intended for documents stored on a computer. It can work without a constant internet connection for basic editing, while connecting to cloud services enables collaboration and online features. The desktop documentation also exposes an AI Agent area for selecting an assistant provider.
The project overview advertises connections to local models and desktop agents for local document-editing tasks. Those descriptions show that local connections are possible; they do not mean that every provider, feature or default configuration runs locally. Verify the provider selected in the AI Agent settings and the network endpoint it uses.
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What changes when ONLYOFFICE is connected to Nextcloud
The ONLYOFFICE connector links Nextcloud with Document Server so users can edit and collaborate on files through the server. Administrator documentation describes controls that affect the path and scope of access:
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- Limit which user groups can use the editors.
- Restrict whether files in external storage may be opened through the editor.
- Configure advanced document permissions.
- Choose options for saving intermediate versions.
These controls do not turn a remote AI service into a local one. They govern who can open files, which storage locations are allowed and how versions are handled. The guide also warns that converting some formats into OOXML can cause data loss. Preserve originals and test important formats before adopting a conversion-heavy workflow.
Three common deployment patterns
All-local editing and inference
A desktop editor opens a local file, and its AI provider points to a model and runtime on the same computer or on an internal server. In this design, the document need not leave your controlled network for inference. You still need to verify that optional features, telemetry, updates or account services are not sending content elsewhere, and you remain responsible for securing the machine and model endpoint.
Self-hosted storage with hosted inference
Nextcloud and the editor server run on infrastructure your organization controls, but the selected AI integration calls an external provider. Storage and collaboration remain under your administration while prompts or model inputs cross the provider boundary. This hybrid arrangement can be useful for model quality or capacity, but it must be described as hybrid rather than fully on premises.
Local desktop editing with a cloud assistant
A user opens a file from the computer in an offline-capable desktop application and then enables an AI assistant configured for a hosted service. The file may be local at rest while the text used for an AI operation is transmitted. Offline editing therefore does not prove offline AI processing.
A practical audit before using sensitive documents
- Inventory the editor. Record the product, edition and version. Distinguish a desktop application from a self-hosted Document Server or a hosted editor.
- Locate the file. Determine whether the working copy is on the local device, in Nextcloud, in another self-hosted service or with a provider. Include synchronized and external-storage locations.
- Identify the AI feature. Note whether the action is summarization, rewriting, translation, generation or another operation. Different features may use different connectors.
- Identify the selected backend. In Nextcloud, inspect the enabled AI integration and its deployment location. In ONLYOFFICE Desktop Editors, inspect the AI Agent provider selection. Do not infer the backend from the editor’s licensing or offline capability.
- Establish the inference location. Confirm the hostname or service boundary and whether the model runtime is on the device, on an internal server or at an external provider.
- Check model openness separately. Ask whether the inference software is open source, whether the trained model can be self-hosted and whether training data is available. These are distinct properties.
- Review administrative controls. In a Nextcloud–ONLYOFFICE deployment, check group restrictions, external-storage access, permissions and intermediate-version settings.
- Read current provider terms. Verify retention, training use, regional processing, logging and deletion commitments for any external service. Product manuals do not establish those contractual details.
- Test with a non-sensitive file. Use a synthetic document and observe the configured services, logs and network boundaries before processing confidential content. Treat the test as deployment verification, not as proof that every future feature has identical behavior.
- Document the configuration. Record versions, enabled apps, backend names, server locations and the date of review. Recheck after upgrades or provider changes.
Comparison checklist for tools and deployments
| Axis | Questions to answer |
|---|---|
| Document storage | Is the file on a local device, a self-hosted server or a provider cloud? |
| Editor deployment | Is editing performed by a desktop app, a self-hosted document server or a hosted editor? |
| AI inference location | Does the model run locally, on an on-premises server or at an external provider? |
| Model openness | Are the inference software, trained model and training data available under the conditions you require? |
| Administrator controls | Can admins restrict groups, external-storage files, permissions and version behavior? |
| Operational burden | Who patches, backs up, monitors and secures the editor, storage and model infrastructure? |
The cited product documentation does not quantify the operational burden. A local deployment can reduce provider exposure while increasing work for your administrators; a hosted model can reduce infrastructure work while introducing another organization into the data path.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Failure modes and how to correct them
Assuming the platform has one privacy mode
Symptom: An organization describes its Nextcloud installation as local without naming the AI backend. Correction: Record the exact integration and whether its inference endpoint is internal or external.
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Confusing offline editing with offline AI
Symptom: A desktop editor is approved for confidential work solely because it opens files without continuous internet access. Correction: Inspect the AI Agent provider and disable or replace remote integrations when policy requires local processing.
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Symptom: Storage is self-hosted, but the location and permissions of Document Server are undocumented. Correction: Map the connector, server placement, user groups and external-storage rules as part of the same review.
Assuming an open model is automatically suitable
Symptom: A model is selected because its code or weights are available, without checking training-data availability, capacity or quality. Correction: Evaluate all three openness criteria, model suitability and hardware independently.
Overlooking format conversion
Symptom: A file changes after being opened through the integration. Correction: Test the format, preserve the original and account for the connector’s OOXML-conversion warning before production use.
Security and privacy decisions that remain yours
Neither the Nextcloud manual nor the ONLYOFFICE guides independently audit a deployment’s security. You must still control authentication, network access, secrets, backups, patching, logging and administrator privileges. A model running on premises can expose documents through a misconfigured server; a hosted provider can be contractually acceptable for one document class and prohibited for another.
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Bottom line for choosing an open-source AI office setup
Map the complete path: editor, storage and AI backend. Use Nextcloud’s backend and on-premises labels as configuration information, not as a blanket guarantee. Treat ONLYOFFICE’s offline desktop capability and local-model connections as options that still require provider verification. Before a sensitive document enters an AI feature, name the exact service that will process it, where that service runs, what administrators control and which provider terms apply. Only that configuration-specific answer tells you where the document and prompt go.
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