Short answer: If your code cannot leave a controlled network, start with Continue or Cline and run an approved model locally or on infrastructure you manage. If cloud processing is acceptable but you need strong enterprise controls, Amazon Q Developer Pro is the clearest AWS-governed choice. Cursor can be tightened with Privacy Mode, repository blocklists, customer-managed encryption keys, and private connectivity, but its documented architecture still sends AI requests to Cursor servers and does not provide a self-hosted server deployment.
What “private” must mean for a sensitive codebase
Privacy is not one setting. An AI coding assistant can expose source code through several paths: prompts and retrieved files sent to a model provider, stored conversation or repository context, telemetry, agent workspaces, logs, and support or abuse-review systems. A tool is suitable only when each path fits your threat model and contracts.
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Use these questions to define the boundary before comparing products:
- Data path: Which files, symbols, prompts, and command output leave the workstation? Are they retained, and for how long?
- Model control: Can you select an approved model, provider, or locally hosted model? Does the provider exclude your content from training and service improvement?
- Deployment: Does the assistant run as SaaS, through private connectivity, inside your virtual private cloud, on a local runtime, or without internet access?
- Agent authority: Can it edit files, execute commands, browse, or make network requests? Are actions individually approved?
- Governance: Can security administrators enforce identity, role-based access, repository restrictions, key management, retention, audit, and regional processing?
- Operational exposure: Who patches the model host, monitors it, handles incidents, and removes data when a developer or repository is deprovisioned?
“Open source,” “BYOK,” and “local model” describe parts of this picture, not a complete guarantee. A locally run editor may still send telemetry, extensions may call external services, and a bring-your-own-key arrangement can still use a provider’s hosted infrastructure. Confirm the complete request path for the exact version and plan you will deploy.
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At-a-glance comparison
| Option | Deployment and model path | Privacy and governance evidence | Workflow and controls | Best fit |
|---|---|---|---|---|
| Cursor | Cloud service; AI requests send code data to Cursor servers. No self-hosted server deployment is documented. | Privacy Mode, enforced organization controls, repository blocklists, customer-managed encryption keys (CMEK), and private connectivity. Privacy Mode says code is not used for training by Cursor or other AI model providers. | Editor-based completion, chat, edits, and Cloud Agents. Cloud Agents require repository storage. | Teams that accept a managed cloud boundary and want centralized controls rather than on-premises operation. |
| Continue | Open-source assistant for VS Code and JetBrains; documentation covers offline operation and self-hosting a model. | Control depends on the model host, network design, logging, and the team’s operation of the deployment. | Agent, chat, edit, autocomplete, and CLI capabilities. | Teams that need the strongest path toward locally controlled or self-hosted development. |
| Cline | Supports open-weight models, local runtimes, and bring-your-own-provider (BYOK) configurations. | Enterprise documentation lists SSO, RBAC, model and tool controls, remote configuration, and observability integrations. | Editor and terminal agent can read and write files, run commands, and use a browser; every action requires explicit approval. | Teams that want powerful agent workflows with a human approval gate and flexible model choices. |
| Amazon Q Developer | Managed AWS service. Storage and processing vary by feature, tier, and Region. | AWS says Pro-tier code content is not used for service improvement or model training. TLS 1.2 or higher protects data in transit; customer-managed KMS keys are available for selected Pro features. | Code review can check vulnerabilities, secrets, infrastructure-as-code misconfiguration, quality, deployment risk, and software-composition issues. | AWS-centric organizations that need a governed cloud service and AWS identity and key-management integration. |
Cursor: a hardened cloud baseline, not an on-premises alternative
Cursor’s documented controls can reduce exposure for organizations that are comfortable with a hosted service. Privacy Mode states: “With Privacy Mode enabled your code is never used for training by Cursor or other AI model providers.” Treat that as a training-use commitment, not as a statement that prompts and code remain on your workstation. Cursor’s security documentation says AI requests send code data to Cursor servers.
Controls available in the documented model
- Privacy Mode: prevents code from being used for training by Cursor or other model providers, according to Cursor’s privacy documentation.
- Organization enforcement: administrators can enforce settings rather than relying on each developer to choose them.
- Repository blocklists: organizations can prevent specified repositories from being used with the service.
- CMEK: customer-managed encryption keys are documented for organizations that need control over key lifecycle and revocation.
- Private connectivity: documented networking options can reduce exposure over the public internet.
Boundary that remains
Cursor states that it does not offer a self-hosted server deployment. That means you cannot place the complete Cursor service inside an isolated data center or air-gapped network using the documented product. Cloud Agents also require repository storage, so agent use deserves a separate review from ordinary inline completion or chat.
For a regulated team, record which features are enabled, what repository data each feature stores, where processing occurs, and which contractual terms apply. A Privacy Mode policy does not by itself answer retention, geographic processing, support access, or agent-workspace questions.
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Continue: the clearest self-hosted and offline path
Continue describes itself as an open-source AI code assistant for VS Code and JetBrains. Its documentation covers agent, chat, edit, autocomplete, and CLI workflows, and provides guides for running without internet access and self-hosting a model. That combination makes Continue the most direct starting point when your security boundary must remain under your team’s control.
What you control
- Editor surface: developers can use the supported VS Code or JetBrains integrations instead of moving code into a separate web application.
- Model host: the documented self-hosting path lets the organization operate the model endpoint and decide where inference occurs.
- Network design: an offline workflow can be built around a model and package set that are already present inside the controlled environment.
- Feature selection: agent, chat, edit, autocomplete, and CLI functions can be evaluated separately; a team does not have to grant every capability to every repository.
Operational responsibilities
Self-hosting moves responsibility to you. Your team must secure the model server, apply updates, restrict administrative access, protect logs and prompts, monitor resource usage, and create a process for importing approved model or extension updates into a disconnected environment. Open-source code does not automatically provide an audited deployment, a retention policy, or a guarantee that every dependency is offline-safe.
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Before calling the result air-gapped, verify that the editor extension, model runtime, authentication, license checks, telemetry, package installation, and update mechanism all function without an external connection. If any component reaches a public endpoint, document that exception or remove the component.
Cline: agent capability with explicit approval
Cline is an editor and terminal agent that can read and write files, run commands, and use a browser. Its defining safety control is an approval gate: Cline’s documentation says, “Every action requires your explicit approval. You’re always in control.” This is useful when the main risk is an agent changing a repository or invoking a tool without a human decision.
Model and deployment choices
Cline documents support for open-weight models, local runtimes, and BYOK arrangements. A local runtime can keep inference inside your environment, while BYOK can let a team select a provider under its own account. Neither phrase alone proves offline operation: confirm whether the selected runtime, model download, authentication, and telemetry work without network access.
Enterprise controls
For larger deployments, Cline documentation lists enterprise SSO, role-based access control, model and tool controls, remote configuration, and observability integrations. These controls help administrators define who can use the agent, which models and tools are available, and how activity is monitored. Map each control to a policy owner: identity administrators for SSO, security administrators for tool restrictions, and platform teams for model hosting and logs.
Approval is not a complete sandbox
Approval prompts reduce accidental actions but do not replace isolation. A developer can approve a destructive command, expose a secret in a prompt, or allow a browser action to reach an external system. Pair Cline with least-privilege credentials, repository-level permissions, protected branches, command allowlists where practical, and network egress controls. Decide whether read-only analysis, file edits, shell commands, and browser access should be enabled independently.
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Amazon Q Developer: governed cloud processing for AWS-oriented teams
AWS states that Amazon Q Developer Pro-tier code content is not used for service improvement or model training. AWS also documents that storage and processing vary by feature, tier, and Region, so procurement must evaluate the exact workflow rather than treating “Amazon Q” as one uniform data policy.
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- Content-use commitment: Pro-tier code content is excluded from service improvement and model training according to AWS documentation.
- Encryption in transit: AWS documents TLS 1.2 or higher for data in transit.
- Customer-managed keys: customer-managed KMS keys are available for selected Pro features; confirm that the feature you need is one of those covered.
- Regional behavior: storage and processing depend on the feature, tier, and AWS Region. Verify the Region and any cross-Region behavior against your residency requirements.
Security review features
Amazon Q can review code for vulnerabilities, secrets, infrastructure-as-code misconfiguration, quality issues, deployment risk, and software-composition problems. These reviews can complement existing scanners, but they do not remove the need to define retention, access, and export policies for findings and source context.
When it is the practical choice
Choose this route when your organization already has AWS identity, networking, logging, and key-management practices and accepts a managed cloud service. It is less suitable when policy requires the model and complete assistant service to run inside an isolated facility with no provider-side processing.
Choosing by deployment requirement
Code must stay inside a controlled network
Begin with Continue’s documented offline and self-hosted model workflows or Cline using a verified local runtime. Build a proof of concept that captures every outbound connection from the editor, model runtime, package manager, and update process. Do not infer air-gapped support merely from an open-source license or BYOK option.
Cloud processing is allowed, but training use is prohibited
Evaluate Amazon Q Developer Pro and Cursor with their documented controls. Amazon Q Pro excludes code content from service improvement and model training according to AWS. Cursor’s Privacy Mode makes the same training-use statement, while its requests still go to Cursor servers. Compare retention, regional processing, agent storage, and contract terms before approval.
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Agents must be powerful but reviewable
Cline’s explicit approval model is the most direct fit in this set. Define which actions require approval, ensure credentials are least-privilege, and log approvals and resulting changes. Continue can also provide agent workflows, but the approval and isolation model will depend on how your team hosts and configures it.
AWS governance is already standardized
Amazon Q Developer Pro may reduce operational friction when AWS SSO, KMS, Regions, and existing audit processes are already in place. Validate feature-specific storage and key coverage rather than assuming every capability inherits the same controls.
You want a familiar hosted editor with additional safeguards
Cursor can serve as a cloud-governed baseline when self-hosting is not a requirement. Enforce Privacy Mode, apply repository blocklists, configure organizational controls, and review private connectivity and CMEK options. Exclude it when policy requires a self-hosted server deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implementation checklist for a sensitive repository
- Classify the repository. Identify source, secrets, personal data, regulated data, export-controlled material, and third-party code. Mark which files an assistant may read.
- Choose the allowed boundary. Decide whether SaaS, private connectivity, a private cloud, a local runtime, or a disconnected environment is acceptable.
- Map the data path. Record what is sent for autocomplete, chat, indexing, agent actions, browser use, code review, diagnostics, and support.
- Set model policy. Approve specific providers or locally hosted models. For hosted options, obtain written terms for training exclusion, retention, subprocessors, and deletion.
- Limit identity and permissions. Require SSO where available, apply RBAC, use repository blocklists or allowlists, and issue agents only the credentials they need.
- Constrain tools. Separate read, edit, shell, browser, and network permissions. Keep explicit approval for consequential actions.
- Protect secrets. Prevent secret files and environment values from entering prompts or indexing. Rotate any credential that appears in an assistant context.
- Control retention and keys. Set log and conversation retention, restrict administrator access, and use customer-managed keys where the selected feature supports them.
- Test egress. Monitor DNS, HTTPS, package downloads, telemetry, update checks, and model traffic. Repeat after upgrades.
- Audit outcomes. Review generated changes, command approvals, model usage, repository access, and exported findings. Keep an incident process for accidental disclosure.
Common mistakes and how to avoid them
Equating privacy mode with local execution
A no-training promise addresses how content is used, not where requests are processed. Cursor’s documented request path still sends code data to its servers. Ask separate questions about provider access, retention, region, and storage.
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Assuming BYOK means the provider cannot see code
A customer-supplied key can control billing or account ownership while the provider still hosts inference. Confirm the network and processing location of the selected model endpoint.
Calling an open-source assistant air-gapped without testing it
Extensions, runtimes, authentication, update checks, and dependencies may make outbound requests. Test the complete installed stack in the target network and document any required import process.
Granting an agent broad credentials
Explicit approval helps, but a user can approve an unsafe action. Use short-lived, least-privilege credentials, protected branches, isolated workspaces, and egress restrictions.
Ignoring feature-specific storage
Autocomplete, chat, indexing, Cloud Agents, and code review can have different storage behavior. Evaluate each enabled feature, not just the product’s headline privacy statement.
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Processing geography, subprocessors, deletion terms, data-protection agreements, and breach notification obligations can determine whether a tool is permitted. Amazon Q’s documented storage and processing vary by feature, tier, and Region; obtain the same level of specificity for every candidate.
A practical decision framework
| Your governing constraint | First option to evaluate | Validation focus |
|---|---|---|
| No provider-side model processing | Continue with a self-hosted model; Cline with a verified local runtime | Offline operation, telemetry, update path, model-server security, and dependency provenance |
| Agent actions require human approval | Cline | Approval coverage, credential scope, tool restrictions, logs, and remote policy enforcement |
| AWS-managed identity and keys are preferred | Amazon Q Developer Pro | Feature-specific Region, retention, KMS coverage, and Pro-tier terms |
| Hosted service is acceptable with organization controls | Cursor | Privacy Mode enforcement, blocklists, CMEK, private connectivity, and Cloud Agent repository storage |
For most highly sensitive repositories, the ordering is straightforward: first test a locally controlled Continue or Cline deployment; if cloud processing is permitted, compare Amazon Q Developer Pro and a hardened Cursor configuration against your retention and regional requirements. Make the final decision from the complete data path and contract, not from a single privacy label.
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