Choose based on data flow and workflow, not the word “local” or “cloud.” A local LLM can keep prompts on your computer when the model, client, plugins, and connected services all stay local. A cloud assistant sends prompts to a provider’s infrastructure, but that does not automatically mean the provider trains on them or keeps them forever. Setup, tool compatibility, retention policy, administration, and the exact route each feature takes determine which option fits.
This guide compares those decisions without treating either approach as universally more private or more capable.
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What “local” and “cloud” mean in practice
Local inference means the model runs on a computer or infrastructure you control. You install a model runner, download model files, and send requests to a local endpoint. If no connected feature sends content elsewhere, the model provider’s cloud does not process those prompts.
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Cloud inference means a remote provider runs the model. Your application sends prompts, files, or tool results over the network, receives a response, and relies on the provider’s account, retention, security, and administration controls.
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Most real workflows are hybrids. A local model may use a web-search plugin, cloud-hosted embeddings, remote telemetry, or a third-party application. A cloud assistant may process a document locally before uploading selected text, or offer a plan with regional processing. Treat each request path separately rather than classifying an entire product as simply local or cloud.
| Decision axis | Local model | Cloud assistant | Verify before choosing |
|---|---|---|---|
| Data path | Can process locally when the model and connected components remain local. A runner may still collect limited telemetry, and optional cloud features can change the route. | Sends prompts to a remote provider for inference. Retention and training terms depend on the provider and product. | Which features leave the device, what is retained, whether prompts are used for training, and what third-party tools receive the data. |
| Setup | Install a runner or client, download a model, configure an endpoint, and provide adequate local resources. | Usually uses provider-hosted infrastructure; you need network access and an account or API credentials as applicable. | Supported operating system, model download, network requirements, API keys, and administrative setup. |
| Integrations | APIs and selected clients can connect, but tool invocation and feature support may differ by model and client. | Often includes vendor-managed tools and integrations, subject to plan, account, and policy limits. | Exact client, model, file handling, search, voice, tool invocation, and security controls. |
| Governance | You control the device and software environment, but also remain responsible for access, backups, patching, and endpoint security. | The provider may supply identity, audit, compliance, and residency controls; eligibility and scope vary. | Retention settings, identity controls, auditability, residency, endpoint behavior, contracts, and administrator access. |
Privacy is a data-flow question
What a local model can keep on the device
When inference and all supporting components stay on your computer, prompts and responses do not need to pass through a model provider’s inference service. 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 Ollama’s statement about locally processed content, not a guarantee covering every client, plugin, operating-system service, backup, or network connection in your workflow.
Local processing can therefore reduce exposure to a provider’s prompt-retention system, but it does not remove ordinary security responsibilities. Anyone with access to the computer, its backups, swap files, logs, or an application connected to the local endpoint may still access sensitive material. A local server exposed to another device can also turn a private workstation into a network service.
How “local” data can still leave the machine
- A web-search, retrieval, translation, or speech feature may send text or audio to a remote service.
- A plugin or client may transmit diagnostics, usage statistics, account identifiers, or error logs.
- A cloud-hosted model can be selected accidentally when a client offers both local and remote models.
- Remote desktop, synchronization, backup, or file-indexing software can copy the same files outside the device.
- Changing a runner’s bind address can expose its API to a wider network than intended.
Ollama says it collects limited device and usage metadata and processes prompt or response content transiently when you use its cloud-hosted models. Check the runner’s current policy and the settings for every connected component, not only the model name.
Cloud use does not automatically mean training
Cloud providers apply different rules to different products. OpenAI’s API 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.
That is an API policy, not a blanket description of every consumer chat plan. Eligible API customers can request Modified Abuse Monitoring or Zero Data Retention, but approval, additional requirements, and endpoint or feature limitations apply. Zero retention is not automatic merely because an account is paid or because a request is sensitive.
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Residency and organizational controls
For organizations, a cloud service may offer controls that are difficult to reproduce on unmanaged personal computers: centralized identity, access policies, audit records, compliance documentation, and contractual commitments. These controls have boundaries. OpenAI’s business documentation says data residency at rest is available to eligible ChatGPT Enterprise, Edu, Healthcare, and API customers in listed regions. In-region GPU inference or processing is available only to eligible customers and supported configurations.
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Local setup: what you actually have to do
1. Choose a runner and a model
A runner such as Ollama or LM Studio supplies the local API and manages downloaded model files. The model choice affects memory use, response speed, context handling, and tool compatibility. The available evidence does not support one universal computer specification or a hardware ranking; requirements depend on model size, quantization, operating system, and workload.
2. Install the runner and download the model
Install the runner for your operating system, then download a model it supports. Model files can be large, so plan for disk space, update time, and local storage protection. Keep the model source and version recorded if reproducibility or auditability matters.
3. Configure the application that will use it
In JetBrains AI Assistant, the documented workflow is:
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- Install and configure the local provider, such as Ollama or LM Studio.
- Download at least one model before attempting the connection.
- Open AI Assistant settings and select the local provider.
- Enter the provider URL.
- Use the connection test, then apply the settings.
- Choose the connected local model in AI Chat or assign it to supported features.
The labels and supported features can change between JetBrains releases. A successful connection only proves that the client can reach the endpoint; it does not prove that every AI Assistant feature supports that model.
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4. Understand the local endpoint
Ollama documents a local API at http://localhost:11434/api and an OpenAI-compatible endpoint at http://localhost:11434/v1. Local requests do not require an API key. Compatible clients can use these endpoints, but API-shape compatibility does not guarantee identical context limits, streaming behavior, vision support, structured output, or tool calling.
5. Keep the service bound safely
Ollama says its server binds to 127.0.0.1:11434 by default. That limits access to the same machine. Changing the bind address exposes the service on another network interface, so do not make that change casually. If another device must connect, restrict firewall rules, require appropriate authentication where available, and treat the endpoint as an internal service rather than an open port.
6. Disable optional cloud features when required
Ollama documents two ways to disable its cloud features:
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Restart Ollama after changing the setting. Ollama says disabling cloud removes cloud models and web search. Test the workflow afterward: a client may fail, fall back to another provider, or lose a feature that depended on remote processing.
Cloud setup is simpler, but access still has conditions
A hosted assistant usually removes model downloading and local runtime maintenance. You create or use an account, select an available model, and provide network access. API-based applications additionally need an API key, endpoint configuration, usage limits, and a plan that permits the required features.
That convenience shifts work rather than eliminating it. Administrators must configure identity, billing controls, retention options, approved regions, logging, and third-party connectors. Individual users should confirm which workspace they are in, whether a file is being uploaded, and whether a feature uses a different provider than the chat model.
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Cloud services can also change availability without a local installation update. A model may be retired, a tool may become plan-gated, or a provider may alter retention eligibility. Record the model and feature settings used for regulated or repeatable work.
Integrations are specific to the client and model
JetBrains AI Assistant and local models
JetBrains supports local providers after the runner is installed, the model is downloaded, and the provider URL is tested. Local models can be used in AI Chat and assigned to certain features. JetBrains also states that AI Assistant does not currently support invoking tools from configured MCP servers when using local models.
That limitation matters if your workflow depends on repository actions, database tools, ticket systems, or other MCP servers. A model may answer coding questions locally while being unable to call the same tools available to a hosted model. Confirm support for each feature rather than assuming that a successful chat connection provides full parity.
OpenAI-compatible APIs
An OpenAI-compatible endpoint can reduce client configuration work because applications already written for that API shape may connect to a local runner. It is an interface convention, not a promise of identical behavior. Tool schemas, authentication, streaming, image inputs, safety filters, context windows, and error handling can differ.
Ollama separates its local endpoints from cloud endpoints hosted on ollama.com. Cloud API requests require an API key; local requests do not. If a client contains both endpoint and model settings, inspect both before sending confidential content.
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Ollama’s ChatGPT Desktop Codex integration
Ollama documents a ChatGPT Desktop integration for Codex mode that requires Ollama version 0.34.0 or newer. In Ollama’s documented setup, you enable ChatGPT Desktop under Ollama Apps and select up to five compatible models.
The routing distinction is important: only requests made with an Ollama model route through Ollama. Native ChatGPT models continue using their usual provider. Ollama models are used for Codex mode, while regular Chat and voice continue using the usual ChatGPT connection. Therefore, seeing a local model in one part of an application does not mean every conversation or voice request is local.
Version requirements and integration availability are volatile. Recheck the current application and runner documentation immediately before deployment, especially after an upgrade.
Governance and security trade-offs
Responsibilities with a local deployment
- Control who can log in to the computer and who can reach the local API.
- Encrypt disks and backups when prompts or generated files are sensitive.
- Patch the runner, client, operating system, and plugins.
- Define where model files, chat histories, temporary files, and logs are stored.
- Prevent unapproved cloud fallback and document which integrations are allowed.
- Plan recovery if the workstation fails or a model file becomes corrupted.
Local control can be valuable for offline work or strict data boundaries, but it also places operational burden on the user or IT team. A misconfigured endpoint, stolen laptop, or unmonitored plugin can defeat the intended privacy boundary.
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- Choose the correct workspace, API project, or account.
- Review the provider’s training, abuse-monitoring, retention, and deletion terms for that product.
- Use identity controls, least-privilege keys, spending limits, and audit logs.
- Check whether files and tool outputs are retained separately from chat messages.
- Confirm residency and processing eligibility for the selected plan and endpoint.
- Review every connected application because third parties may apply their own policies.
A cloud provider may offer stronger centralized governance than an unmanaged laptop, while a local deployment may offer a simpler physical data boundary. Which is safer depends on the controls you can actually configure and enforce.
How to choose for a real workflow
Choose local first when these conditions dominate
- Your policy requires prompts to remain on a controlled device and your workflow can avoid remote search, plugins, and cloud models.
- You can maintain the runner, model files, endpoint security, backups, and updates.
- Your client does not require tools that local models cannot invoke.
- You accept variable performance and model-specific resource requirements rather than relying on a hosted capacity pool.
Choose a cloud assistant first when these conditions dominate
- You need managed identity, centralized administration, auditability, or contractual compliance features.
- Your team depends on hosted search, voice, file processing, or integrations that are not available with the selected local model.
- You prefer not to maintain model downloads, runtime updates, and workstation capacity.
- Your organization can approve the provider’s retention, residency, and third-party processing terms.
Use a hybrid policy when needs differ
Many teams can keep source code, customer records, or early drafts on a local model while using a cloud assistant for approved, sanitized tasks. A hybrid policy should define which data classes may use each route, which tools are prohibited, how users identify the active model, and what happens when the preferred model is unavailable.
Troubleshooting common failures
The client cannot connect to the local model
- Confirm the runner is running and the model is downloaded.
- Check the provider URL and port; Ollama’s documented local port is 11434.
- Test the connection from the client’s settings rather than assuming an OpenAI-compatible path is enabled.
- Check firewall rules and whether another process is using the port.
The model responds, but a tool does nothing
Check whether the client supports tool invocation for that provider and model. In JetBrains AI Assistant, configured MCP tools are not currently invoked when using local models. Switch only after confirming that the alternative route is approved for the data involved.
A supposedly offline workflow makes network requests
Inspect web search, cloud model selections, telemetry settings, plugins, updates, and backup software. With Ollama, disable cloud features using the documented server setting or environment variable, restart the service, and verify that the client has not silently selected a hosted model.
A cloud request has the wrong retention expectation
Identify the exact product: API controls are not interchangeable with consumer chat-plan controls. Review the applicable abuse-monitoring, training, deletion, residency, and eligibility terms, and obtain approval before relying on Modified Abuse Monitoring or Zero Data Retention.
A practical decision checklist
- Map the complete data path for prompts, files, tool calls, logs, backups, and model updates.
- Classify the data and identify what may not leave the device or approved region.
- List the exact integrations required, including search, files, voice, MCP tools, IDE actions, and structured output.
- Test the chosen model in the actual client instead of relying on API compatibility claims.
- Confirm local resource capacity or cloud plan eligibility for the intended workload.
- Set access, retention, residency, and audit controls before production use.
- Document fallback behavior so users know whether a failed local request can switch to a cloud model.
- Recheck versions, feature limits, and provider policies before deployment and after major updates.
The most defensible choice is the one whose data route, setup burden, and integration limits you can describe precisely. “Local” can reduce provider exposure only when the surrounding workflow stays local; “cloud” can provide strong governance without necessarily training on prompts, but its retention and residency controls are product- and eligibility-specific.
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