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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchEvaluate a self-hosted AI coding assistant by tracing every place code, prompts, outputs, logs, credentials, and agent actions can go—not just by checking where the model runs. Set hard data-boundary requirements first, then compare developer workflow, security controls, model quality, and operating effort in a pilot using representative repositories and tasks.
What does “self-hosted” need to mean for your organization?
Deployment labels can conceal important differences. Decide whether your requirement is that inference runs on infrastructure you control, that data stays in a permitted region, or that the complete workflow works without external network access. Those are not interchangeable guarantees.
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| Deployment model | What it establishes | What to verify |
|---|---|---|
| Self-hosted inference | The organization operates the model-serving environment. A product may also describe itself as self-contained; Tabby’s project repository, for example, says it does not require a DBMS or cloud service. | Whether the IDE extension, assistant server, model endpoint, retrieval or indexing services, logs, telemetry, updates, and administration also remain inside the approved boundary. |
| Local BYOK | A client uses a key for a model service. GitHub’s documentation says local keys are handled client-side and can remove dependence on the Copilot API for listed clients; it describes this configuration as suitable for air-gapped environments. | The exact client and model route, required network connections, key storage, and organizational policy. Do not apply the description to clients or product surfaces that are not listed. |
| Enterprise BYOK | GitHub documents an enterprise, server-side BYOK option. | GitHub documents this option as public preview; it requires a Copilot license and internet access. It is not equivalent to a disconnected deployment. |
| Regional cloud processing | GitHub documents Copilot data residency for GitHub Enterprise Cloud with data residency, with the United States and European Union currently listed. Requests are routed to model endpoints in the designated region. | Model availability varies by region and can change. Regional processing may address a jurisdictional requirement, but does not establish on-premises hosting, air-gap compliance, or suitability for a particular organization’s regulatory obligations. |
| Disconnected or air-gapped workflow | The workflow is designed to function without external network access, subject to its documented dependencies. | Whether all required features, model access, updates, licensing, authentication, telemetry, and administration work in the disconnected environment. GitHub documents Copilot CLI use with GHES in disconnected or air-gapped environments as a technical preview subject to change. |
Write the boundary as a flow, not a slogan: identify which systems receive source code and prompts, where completions and chat responses are generated, what is retained, and what must connect to an external service. A product’s deployment label is a starting point for that review, not evidence that a specific configuration meets your controls.
Which requirements should you write down before comparing products?
Separate hard gates from scored preferences. A hard gate rules a candidate out—for example, no source code may be sent to external inference. A scored preference helps distinguish viable candidates—for example, support for several IDEs. Record requirements with enough detail that the pilot can test them.
- Data and network: data classifications permitted for assistant use; allowed egress; required hosting location; whether external inference is prohibited; and whether the workflow must function while disconnected.
- Developer workflow: required IDEs and editors, languages, completion and chat workflows, repository and documentation context, source-control systems, accessibility needs, and onboarding expectations.
- Identity and governance: identity provider and SSO requirements, roles and administrative boundaries, repository access, audit events, retention and deletion, and policy enforcement.
- Agent permissions: which features may read or write files, execute shell commands, access the network, use credentials, or invoke connected tools; when approval is required.
- Operations: deployment ownership, availability expectations, update and rollback cadence, vulnerability handling, support boundaries, staffing, and budget.
Ask vendors or project teams to answer the same data-flow and control questions for the exact edition, client, and deployment you would pilot. Mark each answer as documented, demonstrated, or still unverified rather than treating a broad feature claim as proof.
How should candidates be compared?
Use one scorecard across products, but do not let a feature matrix make the decision on its own. A candidate that passes every hard gate still needs to work for developers and be supportable by the teams who will run it.
| Evaluation area | Questions to answer | Evidence to collect |
|---|---|---|
| Deployment and data flow | Where does inference run? Where are prompts, code context, completions, logs, and telemetry processed or retained? What outbound connections and offline update paths are required? | Architecture and network-flow review; observed connections from the piloted configuration; retention and deletion behavior. |
| Developer workflow | How well do completion, chat, and editing fit the team’s IDEs, languages, repositories, and source-control practices? Is repository context useful and onboarding manageable? | Task results and developer feedback across the required tools and representative codebases. |
| Model control and quality | Which models are available, under what licenses and provenance? Can the organization control upgrades and rollbacks? How do quality and latency behave under expected concurrency? Does the assistant signal uncertainty appropriately? | Model and version records; license review; task-level human assessment; latency observations under the pilot’s stated workload. |
| Security and governance | Are identity and role boundaries adequate? How are repository access, secrets, audit, retention, and deletion handled? What can shell commands and other tools access? | Configuration review and permission tests for the exact client, server, model route, and agent features in scope. |
| Operations and economics | What capacity, storage, availability, upgrades, support, user administration, and staff time are needed? What are the measured inference costs, if applicable? | Observed resource use and operating effort in the pilot; a cost model based on the organization’s workload and deployment design. |
Tabby is one relevant self-hosted example. Its official documentation describes an open-source, self-hosted AI coding assistant and points to server setup, IDE extensions, model information, and API references. Its repository says it supports consumer-grade GPUs. Those are project descriptions, not independent comparative results, a minimum hardware specification, or evidence of a particular deployment’s latency or security. Validate the version, model, license, integrations, administration, and architecture you actually intend to use.
How do you review data flow and security?
Trace a prompt from the editor through every component that could handle it. Include code context retrieved from a repository, generated output, diagnostic data, logs, telemetry, and any tools invoked by an agent. Identify which credentials are available to each process and what permissions those credentials grant.
- Map the path: document the editor or IDE extension, assistant server, model-serving endpoint, retrieval or indexing services, logging and telemetry systems, and connected tools.
- Check retention and access: determine what each component stores, who can read it, how long it persists, and how deletion works. Verify whether those settings apply to prompts, code context, outputs, and operational logs.
- Test permissions: exercise file reads and writes, shell execution, network access, credentials, child processes, and any MCP, LSP, or other connected tools that the chosen configuration supports.
- Verify the real configuration: repeat the checks with the exact IDE client, server, model route, policies, and agent features planned for deployment. Record exceptions and compensating controls rather than assuming that a different product surface behaves the same way.
“Local” does not by itself describe an agent’s isolation. GitHub’s documentation says local sandboxing is off by default. Its CLI sandbox constrains process access at the operating-system level; GitHub notes that this is not a separate virtual machine or container. The documentation also distinguishes built-in file tools from sandboxed shell tools and says remote MCP servers are not sandboxed. Some sandbox features are experimental or public preview, so check the status and control surface that apply to the configuration you evaluate.
What should a useful pilot measure?
Use the same task set and repositories across candidates, and keep hardware and environment consistent where feasible. Include routine work as well as tasks that exercise repository context, editing, and the security boundaries you care about. A pilot should produce evidence for your team’s workload, not a universal claim about productivity.
- Correctness and relevance: have reviewers assess whether suggestions solve the task and fit the codebase.
- Practical usefulness: record whether developers accept or adapt edits and whether the result passes existing tests. An accepted suggestion is not automatically correct or secure.
- Security: record exposed secrets, unsafe or unauthorized actions, policy violations, and failures of permission boundaries.
- Responsiveness and availability: observe latency and service behavior under the concurrency and network conditions represented in the pilot.
- Operating effort and cost: track administration, updates, troubleshooting, capacity use, and staff time, along with inference costs where they apply.
Report the sample size, environment, hardware, model and version, tasks, evaluation method, and known limitations. The official and project sources reviewed here do not establish an independent head-to-head benchmark, universal productivity gain, or supported GPU sizing recommendation. Tabby’s consumer-GPU statement is qualitative; it does not specify a card, memory requirement, or performance tier. Choose hardware only after measuring the model, workload, concurrency, and service expectations you need to support.
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How do you make the procurement decision?
Apply hard gates first, then compare the candidates that remain on measured developer fit, security, and operating burden. For each unresolved requirement, name the evidence needed and who owns obtaining it. If a feature is in preview, treat that status as a decision factor: confirm its current availability, support boundaries, and change risk before making it part of a production dependency.
Best Value
Before approval, retain the data-flow map, permission-test results, pilot scorecard, model and license details, and operating assumptions. Revisit them when a model, client, deployment route, regional availability, or agent capability changes. Product capabilities and preview status are volatile; verify the applicable official documentation for the configuration and date of procurement.
Quick Recap
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