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Continue is the better-documented choice for genuinely offline AI coding. Its setup guide describes installing the extension from a downloaded VSIX, disabling anonymous telemetry, and configuring a local model; its local-model documentation says that, once configured, it can work without an internet connection. Cursor’s cited documentation describes AI features sending prompts and code context to model providers. Privacy Mode and using your own API key do not make those requests offline.
That is a verdict about documented offline operation, not a claim that Continue writes better code. Local model capabilities vary, and the available documentation does not establish a controlled quality or speed winner.
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What does “offline” mean for an AI coding assistant?
For this comparison, offline means the assistant can provide AI features while the development machine has no internet connection. That is different from preventing training use, limiting vendor retention, or running a model on a private network that still has connectivity.
Those distinctions matter because a privacy setting can change how a service handles data without changing where inference happens. Continue documents a local-model workflow intended to work without internet after setup. Cursor’s cited materials describe AI requests being sent to providers, so its privacy controls should not be mistaken for disconnected operation.
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- No internet during use: the model and required software must be available locally.
- No training use: a provider may receive a request but operate under terms that restrict training use.
- No vendor retention: a request can still leave the machine even if it is not retained long-term.
- Private network hosting: a model can be locally or internally hosted while the environment remains networked.
If the policy requires that prompts and code never leave the workstation, focus on local inference and verify the full data flow, including telemetry and optional agent features—not just the product’s privacy label.
It helps to turn the word “offline” into a testable policy. Does the laptop have to work with Wi-Fi disabled, or is an internal model server on a company network permitted? Is it acceptable for a request to reach a provider if code is not used for training, or must no prompt leave the device at all? These answers change what qualifies. Continue’s local-model setup addresses disconnected use; it does not by itself resolve every organization’s audit, storage, or network-boundary requirements.
Which assistant is better for disconnected development?
Continue is the stronger fit when “offline” means no internet connection during use, because its documentation provides a specific local-model setup path. Cursor is not documented in the cited materials as offering a fully offline AI path; its described AI requests go to providers.
| Decision point | Continue | Cursor | What it means for an offline team |
|---|---|---|---|
| Disconnected AI use | Documented workflow uses a local model and disables anonymous telemetry. | Cited documentation describes AI requests to model providers; it does not document fully offline AI. | Choose Continue for a strict no-internet requirement. |
| Where inference runs | Supports local providers, including Ollama; local configuration is stored on the machine. | Prompts and code context are sent to providers. Requests using a user’s API key still pass through Cursor’s backend. | A privacy mode or personal API key is not the same as local inference. |
| Feature coverage | Roles include chat, edit, apply, autocomplete, embeddings, and reranking, but support varies by provider and model. | Offers chat, agent, autocomplete, and cloud workflows; the cited sources describe networked model requests. | Check the exact feature and model you need rather than assuming every feature behaves alike. |
| Agent workflows | Local tool-calling and reasoning limits can make agent mode challenging. | Cloud Agents are a separate workflow requiring repository access and temporary encrypted repository storage. | Assess model tool-use ability and your organization’s storage boundary. |
| Preparation before disconnecting | Download and install the VSIX, configure telemetry and a local model, and stage artifacts for an air gap. | The described AI features depend on requests to providers. | Do not assume an offline setup guide means every dependency can be downloaded after disconnection. |
The practical comparison is therefore not simply “local product versus cloud product.” Continue can be configured to use local models, but the selected provider, model, and role determine what actually works. Cursor’s cited privacy controls affect data handling, but the described model requests still depend on a provider connection. For an air-gapped team, that difference matters more than feature names or interface similarities.
There is also no documented basis here for declaring one assistant categorically better at code generation. A local model might be adequate for a team’s routine completion and chat tasks, while a different task may expose limits in reasoning or tool use. The documentation does not provide a controlled head-to-head benchmark for code quality, latency, or resource consumption. Treat those as items to test with your own representative projects rather than as settled product rankings.
How do you set up Continue for offline use?
Configure Continue while connected, then test the complete workflow with networking disabled. The documented process uses a VSIX, a local model, and a restart; an air-gapped setup also requires staging the software and model artifacts in advance.
- Download the extension package. While connected, get the latest Continue VSIX from its GitHub Releases page. Install that package in VS Code. The workflow depends on having the VSIX available; do not plan to fetch it once the machine is disconnected.
- Disable anonymous telemetry. In user settings, turn off Allow Anonymous Telemetry. Continue’s offline guide includes this step so the extension does not attempt its anonymous PostHog telemetry request.
- Choose a local model and provider. Configure the model in
config.yamlor through the Continue UI. Continue documents local provider options, including Ollama. Pick a model for the role you need and confirm the provider and model are supported by your installed configuration. - Restart VS Code. Restart the IDE after configuring the model so the settings take effect.
- Stage and test for an air gap. Arrange the extension, model runtime, model files, and any other required artifacts before disconnecting. The documentation describes the setup steps but does not guarantee every dependency can be acquired offline. Once staged, disable networking and test the tasks your team actually needs.
A successful chat test alone does not prove that autocomplete, embeddings, edits, or agent actions will all work offline. Test each required role with the selected model and provider.
For a team deployment, make the test repeatable. Record which VSIX version, provider, model, and configuration were tested, and check the setup again after changing any of them. The extension package is only one component: a local inference runtime and the model files it needs must also be accessible. If those pieces are missing when the machine is disconnected, the extension cannot fetch them for you. The documented workflow is a preparation path, not a promise that every dependency has an offline installer.
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Run the test in the same environment where the assistant will be used. Turn off networking rather than relying on a remembered setup, then try the tasks that matter to the project: ask a chat question, request a completion, and verify any edit or apply workflow the team plans to use. If the workflow includes embeddings or agents, test those separately too. A single successful prompt only demonstrates that one path worked; it does not certify every role or optional feature.
Disabling anonymous telemetry is a specific setup step, not a substitute for inspecting all network activity or organizational policy. If the requirement is strict isolation, validate the whole machine and its configuration according to the organization’s own controls. The cited setup guide does not claim that every possible dependency, extension behavior, or third-party component is automatically covered by the telemetry toggle.
Which local models and features can Continue use?
Continue supports multiple local-model roles, but feature availability depends on the provider and model. A model that works for chat may not be suitable for autocomplete or agent tool use.
| Role or task | Documented example or qualification | Practical implication |
|---|---|---|
| Chat | Llama 3.1 8B is an example in Continue’s Ollama guide. | Treat this as a documented example, not a universal quality recommendation. |
| Autocomplete | Qwen2.5-Coder 1.5B is an example in the same guide. | Check autocomplete behavior separately from chat. |
| Embeddings | Nomic Embed Text is an example in the guide. | Confirm the local embedding model is configured if your workflow relies on it. |
| Reranking | Support depends on provider; the cited Ollama guide says Ollama does not offer reranking models. | A local setup using Ollama should not be assumed to provide reranking. |
| Agent tool use | Continue warns that local models may have limited tool-calling and reasoning ability. | Agent mode can be challenging even if basic chat works. |
Continue also notes that local models need sufficient VRAM, but the cited material does not establish a universal GPU, memory, or computer requirement. Hardware needs depend on the model being run; check the selected model’s requirements and validate performance on the actual machine.
Think of these examples as role-specific starting points, not a single package that makes every feature equivalent. Chat, autocomplete, embedding, and reranking perform different jobs, and Continue’s support depends on the chosen provider and model. In particular, the Ollama example does not mean Ollama supplies every role: the cited guide says it does not offer reranking models. If reranking is a requirement, check the provider you intend to use instead of assuming the rest of the local configuration carries over.
Agent workflows deserve a separate proof of concept. An agent’s usefulness depends on the model’s ability to reason through a task and make tool calls, and Continue cautions that local models can be limited in both. A local model can still be suitable for simpler tasks, but do not infer from a good chat response that it can reliably handle a multi-step agent workflow. Decide what level of tool use your project needs and test that exact task.
Hardware is another variable, not a fixed buying checklist. The documentation says a local model needs enough VRAM but does not give a universal GPU or memory specification. Requirements will depend on the model selected and the machine. Check the model’s own requirements and test on the actual workstation; these sources do not support naming one computer or GPU as the right purchase for every Continue setup.
Does Cursor Privacy Mode make Cursor offline?
No. Cursor’s privacy materials say that AI features send prompts and code context to model providers. Privacy Mode concerns data use under its stated arrangements; it is not local inference or a guarantee that requests stay on the device.
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The data-use overview also says requests made with a user’s API key still go through Cursor’s backend for prompt building, and file contents may be temporarily cached. Therefore, a personal key does not bypass Cursor’s request path or satisfy a strict no-network requirement.
Cursor Cloud Agents are a separate optional workflow. Cursor says they need repository access over time, run in isolated virtual machines, and temporarily store encrypted repository copies while the agent runs. If policy prohibits code storage, do not enable Cloud Agents. This is distinct from foreground editing, but it is still not a local-only workflow.
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Keep the distinctions precise when evaluating policy. A statement about data not being used for training is not a statement that the request remains on the workstation. A statement about limited retention is not a statement that no provider receives the prompt. Cursor’s cited materials say AI prompts and code context are sent to model providers, and its API-key requests still travel through Cursor’s backend for prompt building. That means neither Privacy Mode nor a bring-your-own-key arrangement should be represented as an offline configuration.
Cloud Agents add another distinct data path. Their repository access over time and temporary encrypted repository copies are relevant if a team has rules against code storage. Because this is an optional cloud workflow, teams can assess it separately from foreground editing—but a strict no-storage rule is a reason not to enable it. Consider each feature’s data flow rather than applying one privacy label to all possible use.
What should you check before choosing one for a team?
Write down the actual boundary your policy requires before comparing feature lists. “We prefer not to train on code” and “the machine must have no network connection” are materially different requirements.
- Define the boundary: decide whether internet access is prohibited, whether code may be sent to any provider, whether temporary caching is allowed, and whether a private network is acceptable.
- Test the needed roles: verify chat, autocomplete, embeddings, edit/apply, and any agent workflow with the specific local models your team can run.
- Plan the air gap: stage the extension, runtime, model artifacts, and configuration before disconnecting. The documented steps do not establish that every dependency is available through an offline installer.
- Review optional features separately: telemetry and cloud-agent workflows create different data-flow questions from local inference.
- Separate privacy from capability: a hosted model may offer different reasoning or tool use than a local model, while the reviewed sources provide no controlled head-to-head code-quality or speed results.
A practical team evaluation can follow the same order. First, decide whether the constraint is no internet, no provider access, no training use, or no retention. Next, identify the actual work the assistant must support: perhaps chat and autocomplete, or perhaps edits, embeddings, and agent actions as well. Then test those tasks using the selected local provider and model with connectivity disabled. Finally, record any features that do not meet the requirement, instead of assuming a successful basic test covers the whole product.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesInclude a staging plan in the decision. An air-gapped machine cannot rely on downloading its extension or model after it has been isolated. Continue’s documented steps call for downloading a VSIX and configuring a local model, so arrange the extension, runtime, model artifacts, and configuration before the test. The cited setup guide does not promise that every dependency can be fetched offline. For recurring installs, make sure the team can reproduce the working setup from the materials it has staged.
Do not make the decision on privacy labels alone or on feature names alone. An organization might accept a hosted provider under one set of data-use terms yet reject a request leaving the device under another policy. Likewise, a product may list an agent workflow, but the relevant question for local use is whether the chosen model can perform the required tool calls and reasoning. These are separate checks: data boundary, feature support, model capability, and operational readiness.
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No specific purchase price or universal hardware bill follows from the cited product documentation. The local-model route does require a machine with sufficient VRAM for the model selected, but the sources do not establish a single minimum specification. Teams should compare the requirements of their intended model and environment rather than use an unsupported blanket cost or performance estimate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Is Cursor’s OpenAI model availability changing?
As of October 25, 2026, OpenAI’s Help Center says it is planning to wind down its contract providing models to Cursor and proposes November 12, 2026 as a transition date. The Help Center says that date is unconfirmed and Cursor may end access earlier, so availability can change.
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This is a time-sensitive service availability detail, not a reason to treat Cursor as an offline tool. OpenAI’s Help Center proposed November 12, 2026, while noting that the date was not confirmed and access could end earlier. The feature list for API-key use is also specific: local Chat and Agent are included in the article’s description, while Cursor Tab, Auto, Cloud and Background Agents, Automations, the Cursor CLI, and Cursor API/SDK are not. Recheck OpenAI’s Help Center and Cursor’s current documentation before planning around those models or features.
What is the final choice?
For a workstation that must use AI with no internet connection, choose Continue and configure a local model, then verify each required feature with networking disabled. For a team that prioritizes a hosted workflow, Cursor’s documented privacy controls may be relevant, but they should not be treated as a substitute for disconnected operation. Neither the cited documentation nor the available evidence establishes which product produces better code or responds faster under controlled conditions.
The best choice follows from the boundary, not from a blanket claim that one assistant is better for every developer. Continue has the clearer documented path for a disconnected machine; it also asks you to select and validate local models whose capabilities can vary. Cursor’s privacy controls address data handling around a hosted request, not offline execution. If you need a strict air gap, stage Continue’s dependencies and prove the required roles work without networking. If you do not need disconnection, evaluate the products on the specific hosted or local workflow and model you plan to use.
Frequently Asked Questions
Can I use Cursor with my own OpenAI API key without internet?
No. An API key authenticates a hosted request; it does not put the model on your computer. The cited OpenAI Help Center article also limits which Cursor features can use that key, and its availability details may change.
Can Continue autocomplete work offline if chat already works?
Not necessarily. Continue supports different model roles, and provider and model support vary; a working chat configuration does not establish that autocomplete is configured or suitable. Test autocomplete separately while disconnected.
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Can Continue use Ollama for reranking offline?
The cited Continue Ollama guide says Ollama does not offer reranking models. A local Ollama setup therefore should not be assumed to provide that role, even if it supports other local tasks.
Does installing Continue’s VSIX mean an air-gapped install is complete?
No. The documented VSIX installation is only one part of preparation; the runtime and model artifacts also need to be available before the machine is disconnected. The setup documentation does not promise that every dependency can be acquired offline.
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Does Continue work without internet after it is configured?
Continue’s local-model documentation says offline use is possible when local models are used. Configure and test the model and required features before disconnecting; setup and artifact staging still have to be handled in advance.
Does Cursor Privacy Mode keep code on my computer?
No. Cursor’s cited materials say AI features send prompts and code context to model providers. Privacy Mode addresses data use under its stated arrangements; it is not local inference or a no-network mode.
Can I use Cursor Cloud Agents if my team prohibits code storage?
Cursor says Cloud Agents require repository access and temporarily store encrypted repository copies while the agent runs. If your policy prohibits code storage, do not enable that workflow.
Is Continue better than Cursor at writing code offline?
Continue has the clearer documented route to offline use, but the cited sources do not establish a controlled code-quality or speed winner. Results depend on the selected model, provider, features, and machine.
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Continue says local models need sufficient VRAM, but its cited documentation does not establish a universal GPU or memory specification. Check the requirements for your chosen model and test it on the actual machine.
The Bottom Line
Bottom line: Continue is the stronger documented choice when “offline” means no internet during AI coding. Install its VSIX, disable anonymous telemetry, configure a local model, stage required artifacts, and test every feature with networking off. Cursor Privacy Mode and API keys do not make model requests local; its Cloud Agents are also a separate workflow with temporary encrypted repository storage. Neither assistant is proven to produce better code or run faster by the cited documentation.
Quick Recap
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