Open-source does not automatically mean private. To find where a document or prompt goes, trace three separate choices: the editor, the storage and collaboration server, and the AI backend. A desktop app can edit a local file while sending an AI request to a hosted provider; a self-hosted office service can keep files on your infrastructure while using an external model.
The practical question is therefore not simply “Is this tool open source?” It is: where is the file stored, where does editing run, which model receives content, and who administers each system?
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What “open source” tells you—and what it does not
Open-source licensing and inspectable code can help you understand how software is built, audit it, and run it yourself. They do not, by themselves, create a data-flow diagram. An application may be open source while its optional AI feature calls a commercial API. Conversely, a service may be self-hosted while its administrator has configured an external model that receives document content.
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The three layers that determine your data path
1. The editor
The editor is the application that opens, changes or generates a document. ONLYOFFICE Desktop Editors can work with files stored on a computer and does not require a constant internet connection for basic document work. That describes document editing, not necessarily AI processing. Its desktop documentation includes an AI Agent area where an administrator or user can select an assistant provider. If that provider is remote, an AI operation can still send information outside the computer even while the document itself is opened locally.
2. Storage and collaboration
A file can remain on a workstation, live on a self-hosted service such as Nextcloud, or be held by a third-party cloud. In a Nextcloud deployment using the ONLYOFFICE connector, the editor server connects to Nextcloud so people can collaborate. The administrator can control which groups use the editor, whether files in external storage are allowed, document permissions and whether intermediate versions are saved.
Those settings affect who can open or modify a file and where collaboration traffic travels. They do not determine which AI model handles a summarization or rewriting request; that is a separate layer.
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3. The AI backend
An AI feature may run a model on the same computer, on an organization’s own server, or through an external provider. Nextcloud’s AI documentation lists local large-language-model and LocalAI options alongside Ollama, an OpenAI API integration and other hosted services. Its comparison table distinguishes configurations that keep inference on premises from those that do not.
Do not assume that every operation sends the entire file, or that only highlighted text is sent. The available product documentation establishes that backends are configurable, but the exact payload can vary by feature and version. Confirm the implementation details for the operation you intend to use.
What the Nextcloud AI choices imply
The Nextcloud 35 Administration Manual presents AI as a set of selectable backends rather than one fixed privacy behavior. A single Nextcloud installation can therefore have different data paths depending on its administrator’s choice.
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| Backend or approach listed in the manual | What it means to investigate | On-premises question |
|---|---|---|
| Local large-language model 2 ExApp | A local-model route for text processing inside the Nextcloud ecosystem. | Verify where the model service runs, who operates it and whether network access is restricted. |
| LocalAI integration | A connector to a model-serving layer that can be deployed under your control. | Confirm the server location, model files, logging and any outbound connections. |
| Ollama integration | A route to models served by an Ollama installation. | Check whether Ollama is on the same host, another internal host or a managed service. |
| OpenAI API integration | An external API path in which the provider processes the request. | Read the provider’s current terms, retention settings and regional commitments. |
| Other hosted providers | Remote inference managed outside your storage environment. | Identify the legal entity, processing region, contract and administrator controls. |
Nextcloud also describes an ethical-AI rating based on three questions: is the inference and training software open source, is the trained model available for self-hosting, and is training data freely available? Its scale uses green when all 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. A favorable rating does not remove the need to inspect your actual network and storage configuration.
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What ONLYOFFICE changes in the picture
ONLYOFFICE has two relevant, but distinct, behaviors. Its desktop suite is designed for local files and can continue basic editing without a constant connection. Its AI Agent interface lets you choose an assistant provider, so “the desktop app is offline-capable” is not equivalent to “AI requests stay on the device.” The project also describes connections to local models, but that broad capability does not mean every provider or feature uses local inference.
When ONLYOFFICE is connected to Nextcloud, the document server and the storage service become separate components. The connector’s administration options include:
- limiting editor access to selected user groups;
- restricting whether files in external storage can be opened;
- setting advanced document permissions; and
- choosing options for saving intermediate versions.
The integration guide warns that converting some formats into OOXML can result in data loss. Keep an original copy and test your important file types before making a production workflow depend on conversion.
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Compare a configuration on the same axes
Use the following questions for every candidate setup. A “yes” to self-hosting does not answer the storage or model questions, and a “yes” to local editing does not answer the AI question.
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| Axis | Questions to answer | Evidence to request |
|---|---|---|
| Document storage | Is the file on a local device, your server or a provider’s cloud? | Storage configuration, host location and backup policy. |
| Editor deployment | Is editing performed by a desktop app, a self-hosted document server or a hosted editor? | Connection settings and server ownership. |
| AI inference location | Does the model run on the device, on an internal server or externally? | Backend name, endpoint, network route and provider terms. |
| Model openness | Is the inference software open source? Is the trained model available for self-hosting? Is training data available? | Licenses, model availability and training-data documentation. |
| Administrative controls | Can groups, external storage and document permissions be limited? | Admin settings and an audit of effective permissions. |
| Operational burden | Who patches, backs up, monitors and secures each component? | Named owners, update procedures and incident plans. |
Three common deployment patterns
All-local editing and AI
A desktop editor opens files from local storage and sends AI tasks to a model service running on the same computer or an isolated internal machine. This can minimize external disclosure, but it shifts responsibility to you: model downloads, updates, access control, logs, backups and hardware suitability all become your job. Product documentation does not prescribe a universal computer specification, so test the model you intend to run with realistic documents.
Self-hosted storage with an external AI provider
Nextcloud can keep files and collaboration infrastructure on an organization’s servers while the selected AI backend is a hosted API. In this arrangement, storage residency does not equal AI-data residency. The prompt or document material used for an operation may travel to the provider according to that provider’s current terms. Confirm the provider, processing region, retention and whether submitted content is used for training before approving sensitive work.
Local desktop editor connected to cloud services
ONLYOFFICE Desktop Editors can open local files, while a connection to a cloud service enables collaboration and online features. If the AI Agent is configured with a remote provider, the document’s editing location and AI-processing location differ again. Record both settings in your organization’s data-flow documentation.
How to audit a tool before using a sensitive document
- Identify the exact versions. Record the desktop editor, document server, storage platform, Assistant or AI app and connector versions. Capabilities and defaults can change between releases.
- Locate the file. Determine whether the working copy, temporary files, autosaves and version history are on a workstation, a self-hosted server, external storage or a provider cloud.
- Name the editor server. For a Nextcloud–ONLYOFFICE deployment, document the Document Server host, its network segment and who administers it.
- Inspect the AI setting. Write down the selected backend and endpoint. Distinguish a local model, an internal model server and an external API; do not rely on a generic “AI enabled” label.
- Trace the network route. Check firewall and proxy logs during a controlled test with a non-sensitive document. Confirm which hosts receive requests and whether unexpected outbound traffic occurs.
- Review access controls. Limit editor use to necessary groups, review external-storage permissions and verify who can view versions or generated documents.
- Read current provider terms. Check retention, training use, deletion, regional processing, subcontractors and account-level controls for any external backend.
- Test output and conversion. Preserve an original, run the intended operation on a copy and compare formatting and content. Pay particular attention when a non-OOXML format is converted.
- Document the decision. Keep a short record of the data path, approved document classes, owner and review date. Recheck it after changing the model, connector or hosting arrangement.
Privacy and security pitfalls—and practical fixes
“The code is open, so the prompt is private”
Open code does not reveal your administrator’s selected endpoint or a provider’s retention policy. Fix this by recording the backend and verifying the network destination for each AI feature.
“The file never leaves the server”
A self-hosted storage server can still call an external model. Fix this by separating storage location from inference location in your risk assessment and firewall policy.
“Offline mode means every feature is offline”
Basic editing may work without a constant connection while an AI assistant remains configured for a remote service. Fix this by testing AI operations independently from ordinary editing.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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“A local connector is automatically secure”
LocalAI, Ollama or another internal service still needs authentication, patching, logging controls and network isolation. Fix this by assigning an administrator and treating the model server like any other production service.
“Permissions are an afterthought”
Broad group access or external-storage permissions can expose files before AI is involved. Fix this by restricting groups, reviewing effective permissions and deciding whether intermediate versions should be retained.
“Conversion cannot change the document”
The ONLYOFFICE integration documentation notes possible data loss when some formats are converted into OOXML. Fix this by testing representative files, retaining originals and making conversion an explicit approval step.
What you gain and what you take on with local AI
| Consideration | Local or on-premises inference | Hosted inference |
|---|---|---|
| Control over routing | Greater ability to keep requests inside a controlled network. | Requests leave your environment according to the provider’s service path. |
| Administration | You operate models, servers, updates, monitoring and backups. | The provider operates much of the model infrastructure, while you manage account and contract controls. |
| Hardware and performance | Depends on the chosen model and available hardware; no universal requirement is established by these product documents. | Hardware is supplied by the provider, subject to its limits and availability. |
| Model choice | You must select, download and maintain a suitable model. | You depend on the provider’s supported models and change policy. |
| Compliance evidence | You can inspect your own logs and infrastructure, but must produce and maintain that evidence. | You must evaluate the provider’s contractual and technical disclosures. |
Neither route is automatically safer. Local deployment reduces dependence on an external processor but increases operational responsibility. Hosted deployment can simplify maintenance but requires careful contractual, regional and technical review.
A decision rule for teams
For public or low-sensitivity material, a hosted backend may be acceptable after ordinary account and permission checks. For confidential material, require an explicitly approved backend and document its processing terms. For regulated or highly sensitive material, prefer a demonstrably local or on-premises path only when your team can secure, patch and monitor it; otherwise, the label “self-hosted” may create false confidence.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsBefore approving any workflow, answer four questions in writing: Where is the document stored? Where does editing run? Which service receives the AI request? Who is responsible for securing every component? If any answer is unknown, the data path is not yet understood well enough for sensitive documents.
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