Sometimes. An on-premises coding agent can keep code and prompts inside your network if both the agent and the model run there and the installation makes no external calls. But “on-premises” alone is not proof that data stays local: a client may send prompts to a hosted model, while telemetry, integrations, or session syncing create separate outbound paths.
What “on-premises” does—and does not—tell you
The label describes a deployment, not every data flow. Check two separate things: where the coding agent runs and where model inference happens. A locally installed agent can still send code context and prompts to an external model provider if that is the configured endpoint.
Tabby is one example of a self-hosted setup capable of local inference: its project describes a self-contained, on-premises system and documents serving a model with a local server command. That establishes a deployment option, not a guarantee that every product called on-premises uses local inference. Tabby project documentation
Even within one product, different features can use different destinations. The endpoint, model, extension, integrations, and settings in the specific installation determine what leaves the network.
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Which data flows should you check?
Model prompts and code context
Find the exact inference endpoint and determine what the agent sends to it. Depending on the product and task, context may include selected code, nearby files, repository excerpts, terminal output, screenshots, or earlier conversation. Do not assume the agent sends only the text currently visible in the editor; confirm the behavior and controls for the deployed product.
Tabby’s privacy policy, for example, says completion prompts are sent from the device directly to the configured LLM provider using the user’s API key. In that configuration, the provider’s policy governs its handling of those prompts. Tabby privacy policy
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Telemetry and diagnostics
Usage telemetry is distinct from source code and prompt content. Tabby’s IDE extension documentation says it collects aggregated anonymous usage data by default, including system and extension versions, completion counts, accepted-completion counts, and HTTP request latency. It also documents an opt-out setting and says code and generated completions are not tracked or transmitted. That inventory is dated November 6, 2023, so check the current settings and documentation for the release you deploy. Tabby IDE extension documentation
For any product, inspect telemetry, crash-reporting, logging, and diagnostic controls separately. Establish what fields are collected, who receives them, how long they are kept, and whether administrators can disable collection.
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Integrations, tools, and session history
Model inference is not the only possible route out. A coding workflow may contact a hosted source-control service, issue tracker, package registry, documentation index, search service, or remote agent tool. Session history can also sync to an account for cross-device use, analytics, or collaboration.
GitHub says locally run Copilot CLI and app sessions sync to an account by default, subject to controls and enterprise policy; cloud-agent sessions run on GitHub and are shared by default with repository users. These are separate session-storage paths to check, not evidence about every on-premises agent. GitHub Copilot sessions
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How to verify a particular deployment
- Identify the inference destination. Inspect the agent’s model-provider configuration and record the hostname or endpoint that receives requests. Confirm whether the model process runs on a machine inside the network, in a private cloud, or at an external provider.
- Trace the context sent. Check the product’s documentation and settings for repository access, file selection, terminal output, screenshots, and conversation history. Establish which content is included in requests for the features your team uses.
- Review secondary outbound features. Check telemetry, diagnostics, plugins, integrations, tool calls, and session synchronization. Document their destinations and controls independently of the model endpoint.
- Test the installed release. Use network allowlists, DNS or proxy logs, or an isolated test environment to observe destinations while exercising the relevant features. Compare what you observe with vendor documentation and administrator settings; intended behavior is not a substitute for checking the deployed configuration.
A deployment-specific checklist should capture the following:
| Area | What to establish |
|---|---|
| Inference location | Local server in the network, private cloud, or external model provider |
| Prompt and code path | Data categories sent, destination, and whether repository or tool output is included |
| Telemetry | Fields collected, recipient, retention, and opt-out or policy controls |
| Session and history | Local-only storage, account sync, cloud storage, and sharing defaults |
| Network boundary | Whether data stays within the customer network or only within a provider’s region |
| Verification | Documentation, administrator configuration, client version, and observed network destinations |
Cloud residency is not on-premises processing
A regional boundary and a customer-network boundary are different. GitHub documents a data-residency option for Copilot on GitHub Enterprise Cloud. Its documentation lists the United States and European Union as available regions and says compatible clients are generally from 2025 onward. The page states: “Your code, prompts, and Copilot responses never leave your region during inference processing.” That claim concerns inference under the regional-residency policy; it does not mean data remains inside the customer’s own network. Availability and compatible clients can change, so check the current documentation for the applicable configuration. GitHub Copilot data residency
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Cloud-provider retention depends on product surface
For Copilot Business and Enterprise, GitHub says it does not use customer data to train its models. Its product privacy page states that IDE chat and code-completion prompts and suggestions are not retained by default, while prompts and suggestions for other access and use are retained for 28 days by default. It also states that user engagement data is kept for two years by default. These are GitHub-stated defaults for the named plans and access types, not universal retention rules for coding agents; confirm the terms and settings that apply to your account and features. GitHub Copilot data privacy
What counts as staying “inside”?
Before choosing a deployment, define the boundary that matters. A model hosted in your own network may satisfy a network-containment requirement, but account sync, telemetry, or a hosted integration can still send some information elsewhere. A model hosted in a provider’s designated region may support a regional-residency requirement without meeting a rule that data must never leave your organization’s network.
If the requirement is no external traffic, verify every destination used by the installed version and apply network controls that enforce the policy. A product label or privacy statement alone does not establish that a particular installation is air-gapped.
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