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A local LLM runs inference on your computer or infrastructure you control; a cloud AI assistant sends prompts and files to a remote provider. Neither option is automatically more private or more capable. The practical choice depends on the complete data path, the setup you can maintain, and whether your required tools work with the selected model.
What “local” and “cloud” mean in practice
“Local” describes where inference happens, not an entire product or workflow. A model is local when the model files and inference server run on a device or private infrastructure under your control. If your client, plugins, search service, telemetry system, or file connector sends content elsewhere, that part of the workflow is no longer local.
A cloud assistant runs the model on a provider’s infrastructure. Your application transmits prompts, uploaded files, conversation context, and sometimes tool results to that provider. The provider’s retention, training, identity, residency, and abuse-monitoring policies then apply. A cloud service can still offer strong controls, while a poorly secured local installation can expose data through a network, logs, backups, or an untrusted extension.
Many real deployments are hybrid. You might use a local model for source code, a cloud model for a difficult reasoning task, and a hosted search or calendar connector for current information. Evaluate every route separately rather than treating the model label as a privacy guarantee.
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Privacy starts with the data path
When local processing can keep prompts on the device
If the model, client, and connected features remain on the same computer or private network, prompts and responses can be processed without sending their contents to a model provider. Ollama’s privacy policy states: “We do not collect, store, transmit, or have access to your prompts, responses, model interactions, or other content you process locally.” That is the company’s statement about locally processed content, not a guarantee covering every client, plugin, operating-system service, or network configuration.
Ollama also states that it collects limited device and usage metadata. Its hosted models process prompt and response content transiently. Therefore, installing a local runner does not make every feature of that runner cloud-free. Check whether cloud models, web search, account synchronization, diagnostics, or update services are enabled.
How to prevent an Ollama installation from using cloud features
Ollama documents a cloud-disable setting for users who want local-only operation. Add disable_ollama_cloud to ~/.ollama/server.json, or set the OLLAMA_NO_CLOUD=1 environment variable, then restart Ollama. Its FAQ says that disabling cloud features removes cloud models and web search. Confirm that your client is pointed at the local server after restarting.
The default Ollama server binds to 127.0.0.1:11434. That loopback address accepts requests from the same machine. Changing the bind address exposes the server on another network interface; do so only when you understand firewall rules, authentication, and which devices can reach it. An exposed inference endpoint can allow anyone with network access to submit prompts or consume your computer’s resources.
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What cloud processing does and does not imply
Sending a prompt to a cloud provider does not by itself mean the prompt is used to train a model. OpenAI’s API data-control documentation says API data is not used to train or improve models by default unless the customer opts in. The same documentation says default abuse-monitoring logs may contain prompts, responses, and related metadata for up to 30 days, except where longer retention is required or reasonably necessary.
“Up to 30 days” is not the same as a promise that every request is stored for exactly 30 days, nor does it describe consumer chat plans. Modified Abuse Monitoring and Zero Data Retention are available only to eligible customers, require approval, and have endpoint and feature limitations. If you need one of these controls, verify eligibility and the exact endpoints your application uses before sending sensitive data.
Connected tools can change the privacy boundary
A local model may still cause remote processing when a connector performs web search, calls a hosted code index, uploads a file for OCR, or invokes a third-party API. Conversely, a cloud assistant may offer contractual controls, regional processing, and administrative restrictions that a personal laptop does not. Inventory each component:
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- Inference: where the model receives and generates text.
- Conversation storage: where chat history, attachments, and generated files are saved.
- Tool calls: which services receive arguments, documents, URLs, or returned data.
- Telemetry: what device, usage, error, or diagnostic data is collected.
- Backups and logs: whether prompts appear in application logs, shell history, crash reports, or cloud backups.
Setup and maintenance compared
| Area | Local model | Cloud assistant |
|---|---|---|
| Initial work | Install a runner or client, download a model, configure an endpoint, and test the connection. | Create or use a provider account, select an available model or plan, and connect through the provider’s application or API. |
| Network dependence | Inference can continue offline after the model is downloaded, unless a feature requires a remote service. | Network access is required for inference and usually for file and tool features. |
| Updates | You manage runner updates, model downloads, compatibility, storage, and security. | The provider manages model-serving infrastructure; model versions and behavior can change under its release policy. |
| Performance variables | Depends on the chosen model, quantization, operating system, available memory, compute hardware, and workload. | Depends on the provider’s capacity, account limits, network path, selected model, and service policies. |
| Administration | You secure the host, endpoint, files, credentials, backups, and network exposure. | The provider supplies some identity, policy, audit, and compliance features, subject to plan and eligibility. |
Installing and connecting a local model
- Choose a runner and model. Install a local provider such as Ollama or another supported runner, then download a model that fits your operating system, available resources, language needs, and workload. The available evidence does not establish a universal memory, graphics, or storage requirement; those values vary by model size, quantization, context length, and task.
- Keep the endpoint local unless remote access is deliberate. For Ollama, local clients can use
http://localhost:11434/apior its OpenAI-compatible endpointhttp://localhost:11434/v1. Do not replacelocalhostwith a LAN address unless you have configured access controls and a firewall. - Test a simple request. Confirm that the runner is running, the model is downloaded, and a short prompt returns a response. A successful test proves connectivity, not that every client feature is supported.
- Configure the application. In a client’s AI settings, select the local provider, enter its provider URL, test the connection, and apply the setting. Use a dedicated local profile so it is obvious when a task is routed to a cloud model instead.
- Review every optional feature. Disable cloud models, web search, remote file processing, or telemetry where your policy requires it. Recheck settings after runner or client upgrades.
- Secure the installation. Restrict operating-system accounts, protect model and conversation directories, encrypt backups, patch the runner, and monitor which processes can reach the inference port.
JetBrains AI Assistant as a concrete local-client example
JetBrains documents a workflow for local providers such as Ollama and LM Studio. Install the provider and download the model first. In AI Assistant settings, select the provider, enter its URL, test the connection, and apply it. After connection, local models can be selected in AI Chat and assigned to certain features.
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Feature support is not identical to the support of JetBrains-hosted or other third-party models. JetBrains states that AI Assistant cannot currently invoke tools from configured Model Context Protocol (MCP) servers when a local model is selected. A chat response may work while an action that depends on an MCP tool does not. Check the feature-level model selector rather than assuming that a connected model can perform every action available in the product.
What cloud setup usually requires
- Confirm the service and account. Check whether the needed model, file size, context window, voice mode, search, or automation feature is available on your account and plan.
- Set data controls before use. Review retention, training opt-in, logging, residency, and administrator settings. For API workloads, distinguish default abuse-monitoring retention from approved Modified Abuse Monitoring or Zero Data Retention.
- Create credentials safely. Use scoped API keys or the provider’s managed identity mechanism, store secrets outside source code, rotate them, and revoke unused keys.
- Test the exact workflow. Send a non-sensitive prompt through the same application, endpoint, file connector, and tool chain that production work will use. Different endpoints can have different retention or feature rules.
- Document routing. Record which data goes to the provider, which region or account handles it, and which tools receive the model’s output.
Integrations: compatibility is specific, not universal
APIs and OpenAI-compatible endpoints
Ollama documents a local API at http://localhost:11434/api and an OpenAI-compatible endpoint at http://localhost:11434/v1. Its cloud API uses hosted endpoints on ollama.com and requires an API key, while local requests do not. An OpenAI-compatible URL can make a client connect, but it does not guarantee identical tool calling, structured-output behavior, streaming semantics, context limits, or error handling.
Before migrating a workflow, verify the client’s supported authentication method, endpoint path, model-name format, streaming mode, function or tool-calling protocol, file-upload behavior, and maximum context. A text chat that works through a compatibility layer may still fail when the client requests embeddings, vision, audio, background jobs, or a proprietary tool.
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JetBrains’ documented limitation is operationally important: local models in AI Assistant cannot invoke tools from configured MCP servers. If your workflow depends on an MCP server to inspect issues, query a database, edit a ticket, or access a private knowledge base, selecting a local model may remove that action even though ordinary chat remains available. Keep a model assignment per feature and test each tool-dependent action after changing providers.
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Ollama with ChatGPT Desktop’s Codex mode
Ollama documents a ChatGPT Desktop integration for Codex mode that requires Ollama version 0.34.0 or newer. In Ollama’s Apps settings, users enable ChatGPT Desktop and select up to five compatible models. Requests made with an Ollama model route through Ollama; native ChatGPT models continue using their normal provider.
This integration is limited to Codex mode. Ollama says regular Chat and voice continue to use the usual ChatGPT connection, so selecting an Ollama model does not turn every ChatGPT feature into local inference. Version requirements and supported-model lists can change; check both applications’ current documentation immediately before deployment.
Files, search, voice, and automation
Ask four separate questions for each integration:
- Files: Is the file parsed locally, uploaded to a provider, or sent to a separate extraction service?
- Search: Does the model receive search results from a hosted index, and are the original query and selected documents logged?
- Voice and images: Are audio and image transformations available locally, or do they use additional hosted models?
- Actions: Can the selected model call functions, MCP servers, IDE actions, or business APIs, and under whose credentials?
A provider’s marketing label rarely answers all four. Read the client’s model-support matrix and run a small, non-sensitive test for every capability you need.
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Local deployments put more responsibility on the device or organization operating them. You control the host, network, user accounts, disk encryption, backups, and software updates, but you must also configure and audit each one. A local model on an unmanaged laptop can be less defensible than a cloud service with enforced identity, logging, and retention policies.
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Cloud services may provide centralized administration, security controls, compliance documentation, and regional processing. OpenAI’s business data documentation describes security, access-management, compliance, and data-residency controls. Residency at rest is available only to eligible ChatGPT Enterprise, Edu, Healthcare, and API customers in listed regions. In-region GPU inference or processing is likewise limited to eligible customers and supported configurations. Treat “regional availability” as a contract and configuration question, not a universal property of the service.
For either approach, write a short data-handling policy that names approved models, prohibited data, retention limits, allowed connectors, identity requirements, incident procedures, and the person responsible for reviewing changes.
Choosing by workload rather than ideology
| If your priority is… | Investigate first | Why the choice is conditional |
|---|---|---|
| Keeping sensitive drafts on a controlled machine | Local inference with cloud features disabled and a verified local-only client path | Plugins, backups, exposed ports, and remote connectors can still move data. |
| Central administration across many employees | A managed cloud service or a centrally operated private deployment | Cloud governance depends on plan and eligibility; local governance depends on your operational maturity. |
| Offline or intermittent connectivity | A downloaded local model and offline-capable client | Search, voice, updates, and other features may still require network access. |
| Deep integration with an existing hosted assistant | The provider’s native model and supported tools | Replacing the model can remove tool calls, file features, or voice modes. |
| Experimenting with many models quickly | A client that supports multiple provider endpoints | API compatibility does not ensure equal quality or feature behavior, and each model has different resource needs. |
| Strict regional processing requirements | A provider and plan with documented residency, or infrastructure located in the required region | Eligibility, endpoint, and processing-location details must be verified in writing. |
Troubleshooting common failures
The client cannot connect to the local model
- Confirm the runner process is running and the model name is installed.
- Use the exact provider URL expected by the client; distinguish
/apifrom the OpenAI-compatible/v1endpoint. - Check whether a firewall, VPN, proxy, or security tool blocks local port 11434.
- If you changed the bind address, verify that the address is reachable and that the server is not unintentionally exposed.
- Restart the runner after changing
disable_ollama_cloudorOLLAMA_NO_CLOUD.
Chat works but a tool or action fails
- Check whether the selected model supports function or tool calling.
- In JetBrains AI Assistant, confirm whether the action requires an MCP server; local models cannot invoke configured MCP tools there.
- Verify that the client is not silently switching models for a feature.
- Inspect the tool’s own authentication, network, and data policy separately from the model’s policy.
A “local” workflow still shows network traffic
- Identify whether web search, model downloads, update checks, telemetry, crash reporting, or account synchronization is enabled.
- Review the client, runner, operating system, and browser extension settings.
- Use network monitoring approved by your organization to confirm which destinations receive content.
- Do not assume that a loopback inference endpoint prevents other components from uploading files or prompts.
A practical decision checklist
- List the prompt, file, image, audio, and tool data your workflow handles.
- Draw the route for each item: device, local runner, provider endpoint, connector, storage system, and backup.
- Classify the data under your organization’s policy and identify any residency requirement.
- Choose a specific model and client, then verify the model’s tool, file, vision, voice, and context support.
- Estimate local resource fit from the chosen model and workload; do not rely on a generic “local LLM” hardware rule.
- Test with non-sensitive data, including failure cases and tool calls, before production use.
- Configure retention, logging, identity, network exposure, and update controls.
- Document how users can tell whether a request is local, cloud-routed, or hybrid.
- Recheck volatile items—provider policies, residency eligibility, client limitations, integration versions, and supported models—before each major rollout.
The most defensible answer to “local or cloud?” is therefore a routing decision. Use local inference when you can keep the complete required workflow under your control and can maintain the endpoint securely. Use a cloud assistant when its managed integrations or governance controls are necessary and its retention and residency terms fit your data. In both cases, validate the exact client, model, tools, and policies rather than relying on the label alone.
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