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Short answer: If your notes must stay on one device, run a local model through Ollama and use it in VS Code. VS Code can provide local chat without a GitHub account, Copilot plan, or internet connection, but Copilot-powered semantic search, embeddings, and inline suggestions do not work offline. Claude Code is the strongest choice for an agent that can edit files and use tools, but a hosted Claude workflow is governed by Anthropic’s account, retention, and organization policies. The model endpoint—not the editor’s name—sets the privacy boundary.
The decision in one view
These are not three equivalent products. Claude Code is an agentic coding and file-work interface, VS Code is an editor that can host several AI providers, and a local large language model (LLM) is the inference engine running on your hardware. Ollama is a local model runner; it is not itself a model. You can therefore use VS Code with a cloud model, VS Code with Ollama, or launch Claude Code against a local Ollama model. The same notes can have very different privacy properties depending on that endpoint.
| Question | Claude Code | VS Code with a cloud model | VS Code with Ollama and a local model |
|---|---|---|---|
| Where inference occurs | Usually Anthropic’s hosted service; account and organization policy apply. | At the provider selected in VS Code; Copilot services handle cloud features. | On your computer after the model is installed; extensions and telemetry still need review. |
| Privacy boundary | Anthropic retention, deletion, training, and enterprise settings. | Provider and GitHub/Copilot handling for enabled features. | Your device and local configuration, plus any networked extensions or telemetry. |
| Offline chat | Not available for a hosted Claude session without a network connection. | Only available when a local provider is configured. | Available after downloading a model and dependencies. |
| Semantic search | Depends on connected tools and your indexing setup. | Most capable when Copilot semantic search and embeddings are enabled. | Possible with local text search or a local retrieval/indexing extension; Copilot semantic search is unavailable offline. |
| History control | Anthropic account and organization retention rules. | Copilot sessions sync to GitHub by default; the setting is configurable. | Local files and runner logs are under your control, subject to your operating system and extensions. |
| Setup effort | Low initial setup, with continuing service and account dependence. | Moderate; convenient if VS Code is already your editor. | Highest; you must download models, reserve storage, and have hardware that can run them. |
What each option actually is
Claude Code: an agent interface
Claude Code gives a model permission to inspect and work across files, invoke tools, and maintain a task-oriented conversation. For a note vault, that can mean asking an agent to find every note mentioning a project, extract action items, or update a set of Markdown files. The convenience comes from allowing a service to receive the context required for each operation. A hosted Claude Code workflow should therefore be treated as cloud-connected unless you have deliberately configured a local endpoint.
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Privacy is controlled by the Anthropic account mode, your organization’s policy, retention configuration, and model-improvement choices. Anthropic’s Privacy Center says deleted consumer conversations disappear from history immediately and are deleted from backend systems within 30 days. If you allow chats or coding sessions to improve Claude, Anthropic says it may retain de-identified data for up to five years in model-training pipelines. Those are separate controls: deleting a conversation does not mean the same thing as opting out of improvement processing.
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Anthropic’s platform documentation also describes local Claude Code session transcripts being stored for six years by default in compliance contexts, unless an organization sets a finite custom retention period. That statement applies to the platform/compliance arrangement it documents, not automatically to every consumer account. Ask which account type and policy govern your session before putting regulated or confidential notes in it.
VS Code: the flexible shell
VS Code can be an editor, a chat client, and a host for local or cloud model extensions. Microsoft’s documentation states that locally hosted models work without a GitHub account, without a Copilot plan, and without an internet connection. This makes VS Code the most adaptable option: you can start with a hosted provider, then switch a workspace to a local runner without changing your note format.
The trade-off is that not every AI feature follows the model you select. VS Code documents that semantic search and embeddings tied to Copilot services are unavailable offline. It also does not currently route a local model through its built-in Copilot inline-suggestions path. Local chat can therefore work while a feature that feels like “search” or “autocomplete” silently remains cloud-dependent or disappears.
Session history needs its own setting. Copilot sessions sync to GitHub by default so that they can be accessed across devices and queried. To keep that history on the machine, set chat.sessionSync.enabled to false in VS Code settings. Repository exclusions are available when you need to prevent particular workspaces from participating. This setting affects Copilot session synchronization; it does not automatically disable every extension’s telemetry or every provider’s logging.
Local LLMs with Ollama: an on-device endpoint
Ollama downloads and runs models on your computer. In its March 2026 privacy policy, Ollama says it does not collect, store, transmit, or access prompts, responses, model interactions, or other content processed locally. The same policy allows limited device and usage metadata collection, and cloud-hosted models have separate handling. “Local” therefore describes where inference and note content are processed, not an unconditional promise that no network traffic exists anywhere in your installation.
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A local setup has three layers to audit:
- Model endpoint: the model process receiving note text. With Ollama local mode, that process runs on your device.
- Client or interface: VS Code, Claude Code, or another application that sends prompts to the endpoint.
- Supporting software: extensions, indexers, update checks, crash reporting, and operating-system services that may have their own network behavior.
Ollama documents an ollama launch workflow that configures Claude Code to use local models. In that arrangement, Claude Code remains the interface and Ollama is the runner; Anthropic-hosted Claude inference is not being made offline by changing the interface name.
Privacy: what leaves the computer
Hosted Claude Code
Assume note content leaves the computer whenever a hosted Claude Code session needs to read it. The practical questions are retention, deletion, training use, and administrative access:
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- Allowing model improvement can permit de-identified retention for up to five years in training pipelines.
- Platform/compliance documentation lists six-year default storage for local Claude Code session transcripts in the described compliance context, with organization-controlled alternatives.
- Team or enterprise administrators may impose rules that differ from an individual consumer account.
For private notes, read the policy attached to the account you will actually use, disable model-improvement use when available, set the shortest organization retention that meets your obligations, and avoid pasting secrets that the workflow does not need. These steps reduce exposure; they do not turn a hosted session into an offline one.
VS Code and Copilot history
Using a local model inside VS Code can keep prompts and responses local, but Copilot features have a separate path. Copilot session synchronization is enabled by default and sends sessions to GitHub for cross-device access and querying. Set chat.sessionSync.enabled to false before creating sensitive sessions if local-only history is a requirement. Then review workspace exclusions and extension permissions. A local provider does not retroactively remove sessions already synchronized.
Ollama local mode
Ollama’s explicit local-mode statement is the clearest boundary in this comparison: prompts, responses, model interactions, and other locally processed content are not collected, stored, transmitted, or accessible to Ollama. Device and usage metadata may still be collected, and selecting a cloud-hosted model changes the analysis. Keep the runner in local mode, block unsolicited outbound access if your policy requires it, and verify that your VS Code extensions point to the local endpoint rather than a hosted fallback.
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Search: text, semantic, and retrieval differences
Plain-text search remains the dependable baseline
Every option can work with ordinary files and deterministic search. Markdown headings, tags, filenames, and exact phrases are searchable without an LLM. Keep important metadata in plain text—dates, people, project IDs, and status fields—so that a model is not your only index. This also makes a note vault usable when the model is stopped or the network is unavailable.
Semantic search needs an embedding service
Semantic search converts notes and a query into vectors, then finds passages that are conceptually related even when they share few words. Embeddings and the index must be generated somewhere. VS Code says Copilot-backed semantic search and embeddings are unavailable offline. A local model can support semantic retrieval only when you add a local embedding model and an index or extension that stores vectors on the device. That setup is possible, but it is not supplied automatically by turning on local chat.
Claude Code has no single, universal note-search behavior. Its results depend on the tools, repository index, and connected services available in your session. If you need reproducible search, maintain a local index or use deterministic search first, then ask the agent to interpret the returned passages.
Search quality and privacy trade-offs
| Search method | Strength | Privacy/offline profile | Typical failure |
|---|---|---|---|
| Filename, tag, or exact-text search | Predictable and auditable. | Works offline and can remain entirely local. | Misses synonyms and implied relationships. |
| Cloud semantic search | Strong conceptual matching and managed indexing. | Notes or embeddings may be processed by the provider; network required. | Unavailable during outages or when policy blocks cloud processing. |
| Local embeddings plus index | Conceptual matching without sending notes away. | Offline after installation; storage and compute are local. | Index quality, model choice, and extension behavior require maintenance. |
| Agent-driven search over files | Can combine search, summarization, and edits. | Privacy follows the agent’s endpoint and tools. | May read more files than intended unless scope is constrained. |
Offline use: what still works
Claude Code
A hosted Claude Code workflow is network-dependent. If the service, account authentication, or tool connection is unavailable, the agent cannot provide its normal responses. A local Ollama endpoint can be used with the Claude Code interface through the documented launch workflow, but the resulting capabilities depend on the local model and the tools you expose.
VS Code
VS Code itself and a configured local chat provider can work without an internet connection. Copilot-dependent semantic search, embeddings, and built-in inline suggestions do not. Plan an offline workflow around local files, local chat, and local search rather than assuming every Copilot feature has an offline equivalent.
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Ollama
After the runner, model files, and any local extensions are installed, inference can continue offline. Initial downloads consume storage and may take substantial time; performance depends on the model and hardware you choose. Keep a smaller model available for quick indexing or short queries if your primary model is too slow, but do not assume a particular laptop or GPU is universally required—the suitable choice depends on model size, context length, and your response-time expectations.
How to build a private, offline note workflow
Step 1: define the boundary
- Classify your notes: public, personal, confidential, regulated, or containing credentials.
- Decide whether any content may be sent to a hosted provider. If the answer is no, exclude hosted Claude and Copilot features from the sensitive workspace.
- Choose a note format that remains readable without AI, such as Markdown or plain text, and keep the vault in a location covered by your normal backup and disk-encryption policy.
Step 2: install and test a local endpoint
- Install Ollama and download a model appropriate for your hardware and language needs.
- Run a harmless test note containing no personal data. Confirm that the client is configured for the local Ollama endpoint and that it does not ask you to sign in to a hosted provider.
- Disconnect the network and repeat the test. Local chat should continue; any feature that fails is relying on a cloud service or an online extension.
- Inspect the runner and extension settings for cloud-model fallbacks, telemetry, update checks, and diagnostic uploads.
Step 3: configure VS Code for local-only history
- Open VS Code Settings and search for
chat.sessionSync.enabled. - Clear the setting so Copilot sessions are not synchronized to GitHub. Apply the change at the user or workspace level that matches your threat model.
- Add repository exclusions for folders that should never be opened by AI tools.
- Disable or uninstall extensions that send content to an unapproved service. Treat each extension as a separate data processor.
Step 4: add local retrieval only if you need it
- Start with exact-text search and a consistent tag or heading scheme.
- If conceptual search is necessary, install a local embedding model and a local indexer that documents where vectors and cached passages are stored.
- Keep the index beside the vault only if your backup and encryption policy covers it; otherwise place it in an encrypted data location.
- Test retrieval with paraphrased queries and with notes that should not match. Measure false positives before trusting automated summaries.
Step 5: restrict agent actions
- Give the agent a workspace containing only the notes it needs.
- Use read-only access for exploratory searches; enable writing only for a deliberate editing task.
- Require a diff review before accepting changes to multiple files.
- Never store API keys, recovery codes, or passwords in prompts or notes used for model testing.
Using Claude Code safely when cloud access is acceptable
Claude Code is useful when the value of an agent that can traverse files and perform multi-step edits outweighs the cloud dependency. Separate routine notes from sensitive material, and point the session at a narrowly scoped directory instead of an entire drive. Before a project begins, confirm whether the account permits model improvement, how long deleted sessions remain in backend systems, and whether an organization policy imposes the six-year compliance retention described in Anthropic’s platform documentation. Delete sessions you no longer need, but do not represent deletion as immediate erasure from every backup or compliance store.
If you want the Claude Code interaction model without sending note content to Anthropic, use the documented Ollama launch integration with a local model. Check the endpoint shown by the client, run an offline test, and expect differences in tool use, context handling, and answer quality between the local model and a hosted Claude model.
Performance, quality, and maintenance trade-offs
Agent capability versus control
Hosted Claude Code generally minimizes setup and provides a mature agent workflow, but you accept provider availability and policy constraints. VS Code with a cloud provider offers a broad editor ecosystem and strong managed search when Copilot services are enabled. VS Code with Ollama maximizes control over note content and offline operation, while shifting model downloads, updates, indexing, backups, and troubleshooting to you.
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Vendor-reported hybrid results
Ollama and Stanford Hazy Research reported results for hybrid Minion and MinionS projects in 2025: a 30.4-times reduction in remote cost with 87% of cloud-model performance, and a 5.7-times reduction with 97.9% of cloud-model performance. These are vendor-reported project figures, not controlled benchmarks of note-taking accuracy. They indicate why a local-plus-remote design can be attractive, but they do not predict how a particular note vault or model will perform.
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Maintenance you own locally
- Model files consume storage and must be updated deliberately.
- Changing models can change summaries, terminology, and retrieval results; keep a record of the model used for important transformations.
- Local indexes need rebuilding when notes change or when you replace the embedding model.
- Back up the original notes separately from generated summaries and indexes so an incorrect edit is reversible.
Common failure modes and fixes
“Chat works offline, but semantic search does not”
This is expected when chat uses Ollama while search still depends on Copilot embeddings. Use exact-text search offline or install a local embedding and indexing path. Verify that both the embedding model and vector database are local.
“I turned off the internet, but the extension still shows a cloud error”
Inspect the provider and extension settings, not just the editor’s chat model. A local chat selection does not disable Copilot, telemetry, update checks, or another extension that runs concurrently. Remove cloud fallbacks and test again with a clean, restricted workspace.
“A local model is slow or runs out of context”
Reduce the files included in the prompt, shorten retrieved passages, choose a model that fits available memory, or keep a smaller model for routine tasks. More hardware is not the only fix; narrower context often improves both speed and answer relevance.
“A note was synchronized before I changed the setting”
Disabling chat.sessionSync.enabled prevents future synchronization; it does not retract a session already uploaded. Review and delete the synchronized history through the account that received it, then check other extensions and backups.
“Claude Code appears local, so I assume Anthropic cannot see the notes”
Check the actual endpoint. Claude Code is an interface; Ollama is a runner. Only a local model endpoint keeps inference on the device. If the client is authenticated to Anthropic or another hosted provider, the hosted provider’s policy applies.
Which option fits your notes?
| Your priority | Best fit | Why | What to accept |
|---|---|---|---|
| Maximum privacy and reliable offline work | VS Code with Ollama in local mode | Inference and prompts can remain on-device, with local chat and local files. | Model downloads, hardware limits, local indexing, and extension audits. |
| Agentic multi-file work with minimal setup | Claude Code | Strong tool-oriented workflow across a note repository. | Hosted connectivity and Anthropic retention, deletion, and training policies. |
| Managed semantic search and cross-device history | VS Code with Copilot services | Cloud embeddings and synchronized sessions provide convenience. | Notes and session history are handled by the connected services; offline coverage is incomplete. |
| Gradual migration from cloud to local | VS Code as the shell, switching providers | Your files and editor remain stable while the model endpoint changes. | Each provider and extension must be evaluated separately; settings do not create a universal privacy mode. |
Bottom line
Choose the endpoint first, then the interface. For genuinely private, offline note-taking, a local model run by Ollama inside VS Code is the clearest boundary in the available documentation, provided you also control extensions, indexes, and telemetry. Use Claude Code when its agentic file work justifies a hosted workflow—or connect its interface to Ollama when you specifically want a local endpoint. Use Copilot-backed VS Code features when managed semantic search and synchronization matter more than offline completeness. No editor label alone tells you where your notes go; the provider, settings, and account policy do.
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