Telemetry Collection And Privacy Controls

via “telemetry collection with opt-out control”

Universal database client for VS Code.

Unique: Implements opt-out telemetry collection with VS Code settings integration, allowing users to disable data collection via `database-client.telemetry.usesOnlineServices` configuration. Respects VS Code's global telemetry settings.

vs others: More privacy-conscious than many extensions because telemetry is documented and can be disabled; however, specific data points collected are not transparent.

via “anonymous usage tracking and telemetry collection”

Open-source dbt-native data observability and anomaly detection.

Unique: Implements opt-out telemetry with explicit privacy safeguards (no SQL, credentials, or table names collected), enabling product insights without compromising user data. Telemetry module is pluggable (elementary/tracking/tracking_interface.py), allowing users to implement custom tracking backends.

vs others: More privacy-conscious than many open-source projects (explicitly excludes sensitive data) but less privacy-friendly than fully opt-in telemetry. Provides transparency about what data is collected.

via “telemetry and usage tracking with privacy controls”

Unity MCP acts as a bridge, allowing AI assistants (like Claude, Cursor) to interact directly with your Unity Editor via a local MCP (Model Context Protocol) Client. Give your LLM tools to manage assets, control scenes, edit scripts, and automate tasks within Unity.

Unique: Implements optional telemetry with explicit privacy controls, allowing users to opt-out completely while providing developers with usage insights for tool improvement

vs others: More privacy-conscious than always-on telemetry because it provides explicit opt-out controls and doesn't collect sensitive data by default

via “telemetry collection with vs code integration”

Official GitLab-maintained extension for Visual Studio Code.

Unique: Integrates with VS Code's native telemetry system, respecting global telemetry settings and avoiding duplicate telemetry collection across extensions

vs others: More privacy-respecting than custom telemetry systems because it leverages VS Code's centralized telemetry infrastructure and honors user's global privacy preferences

via “telemetry-collection-with-opt-out-control”

This extension is used by the Azure Machine Learning Extension

Unique: Respects VS Code's standard telemetry setting rather than implementing extension-specific telemetry controls, providing consistent privacy behavior across all extensions. Allows users to disable telemetry globally without extension-specific configuration.

vs others: More privacy-friendly than extensions with mandatory telemetry because it provides opt-out mechanism; more transparent than extensions with hidden telemetry because it uses VS Code's documented telemetry setting.

via “telemetry-collection-with-user-opt-out”

Microsoft Fabric VS Code experience for Data engineering and Data science of Microsoft Fabric (Previously Synapse VS Code)

Unique: Integrates with VS Code's standard telemetry framework, allowing users to disable data collection via a single setting while maintaining extension functionality. Follows Microsoft's privacy practices for VS Code extensions.

vs others: Transparent opt-out mechanism consistent with VS Code ecosystem, but less granular than some third-party tools offering per-feature telemetry control

via “telemetry collection with global opt-out via vs code settings”

IntelliCode Completions: AI-driven code auto-completion

Unique: Integrates with VS Code's global telemetry setting rather than implementing extension-specific telemetry controls, reducing configuration complexity but limiting granular control. This design choice prioritizes simplicity over transparency, as users cannot selectively disable IntelliCode telemetry while keeping other VS Code telemetry enabled.

vs others: Simpler than Copilot's separate telemetry settings but less transparent than some open-source completion tools that document exact telemetry fields; comparable to Tabnine's telemetry approach but with less granular control options.

IDE support for Databricks

via “telemetry and usage analytics with opt-out control”

Build, test, and use Stripe inside your editor.

via “optional telemetry collection with granular privacy control”

Fast codebase understanding and navigation

Unique: Explicitly guarantees zero-day retention for all data sent to Anthropic, GCP, and AWS, and commits to not storing raw code or prompts, providing stronger privacy guarantees than many AI tools. However, session replay and query collection practices are less transparent than competitors.

vs others: More privacy-conscious than tools that retain code for model improvement, though less transparent than tools with detailed data retention policies and audit logs.

via “telemetry-free-operation-with-privacy-guarantee”

Chat via OpenAI-Compatible API

Unique: Explicitly disables all telemetry and usage data collection, with transparent privacy guarantee that only LLM provider APIs receive conversation data; differentiates from commercial tools collecting analytics

vs others: More privacy-preserving than GitHub Copilot or other commercial tools with usage analytics; relies on user trust in extension code rather than independent verification

via “telemetry collection for product improvement with undocumented opt-out”

AI Coding Agent, Chat, and Code Completion

Unique: Collects telemetry by default without prominent opt-out UI in the extension, relying on external privacy policies for disclosure; specific data collection practices are undocumented.

vs others: Enables JetBrains to improve products based on real usage data, but less transparent than tools with explicit telemetry controls and documented data practices.