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The best alternative depends on the boundary you need to enforce. Choose GitHub Copilot Business or Enterprise for organization controls inside a familiar IDE and GitHub workflow; evaluate Cursor Enterprise for centrally managed models and agents in a hosted AI-first editor; consider Sourcegraph Enterprise when code search, cross-repository understanding and self-hosting matter; investigate Amazon Q Developer for an AWS-centered environment; and treat a local or open-source stack as a separate architecture project when code must remain under your infrastructure.
None of these choices automatically proves that your legal, security or residency requirements are met. “Not used for training” does not mean code never leaves a developer’s machine, and a “self-hosted” product may still rely on external model inference or telemetry. Define the non-negotiable boundary first, then verify the complete data flow and contract.
Start with the control you cannot compromise
Teams often say they need “more control” when they actually mean different things. A regulated company may require code and prompts to stay inside a private network. A platform team may mainly need central identity, repository restrictions and approved-model policies. Another engineering group may want to keep its existing editor and Git workflow while adding better codebase context. These are different buying problems.
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- Data handling: Which source files, prompts, terminal output, telemetry and retrieved context may leave the developer environment?
- Retention and training: Which settings govern storage and model training, and do they apply to every model, extension and agent feature?
- Deployment: Must the service be self-hosted, single-tenant, or merely hosted in an approved cloud? Where do inference, logging and support components run?
- Administration: Can administrators enforce identity, provisioning, model access, repository access, agent behavior and usage limits?
- Workflow: Must developers stay in their current IDE and source-control system, or is an editor change acceptable?
- Codebase scope: Is the requirement inline completion, or cross-repository search and architectural understanding for people and agents?
Use those answers as pass/fail gates. Vendor feature pages describe advertised capabilities; they are not a security audit or a substitute for contractual review.
Shortlist by control model
| Option | Best fit | Established controls or capabilities | Boundary to verify |
|---|---|---|---|
| GitHub Copilot Business or Enterprise | Teams wanting broad IDE support and organization controls integrated with GitHub | Inline suggestions and chat; license and policy management; Business and Enterprise subscriber data not used to train models; Enterprise GitHub.com integration and organization codebase indexing | Code and surrounding context are sent to the service to generate assistance; review the full data flow and contract |
| Cursor Enterprise | Organizations that want centralized control of an AI-first editor and its agents | SAML SSO, SCIM, role permissions, model and MCP controls, repository allow/block lists, usage controls and analytics | Hosted on AWS; no on-premises deployment; Privacy Mode must be enabled organization-wide for the stated no-training and provider-retention commitments |
| Sourcegraph Enterprise | Large codebases where search, navigation and code understanding are central | Code search, AI-powered Deep Search, batch changes, monitoring, security and administration; single-tenant cloud and self-hosted deployment are listed | Current pricing lists a $16,000 minimum annual contract including AI feature credits; confirm inference, support and data flow for each deployment mode |
| Amazon Q Developer | AWS-focused teams assessing an AWS coding assistant | Official AWS material identifies it as an AWS developer coding assistant | Verify current retention, governance, deployment and organization-control terms before selection |
| Local or open-source stack | Teams needing model and hosting choice under their own operational control | Can be designed around locally hosted models or selected APIs and editor integrations | Security depends on the chosen model, runtime, editor, telemetry, update process, access controls and licenses; no particular stack is established here as secure by default |
The table is a screening tool, not a ranking. A product can be a strong fit on administration while failing a strict network-isolation requirement.
GitHub Copilot: the lowest-friction organizational step
What organization plans add
GitHub lists support for Visual Studio Code, Visual Studio, Vim, Neovim, JetBrains IDEs, Azure Data Studio and GitHub CLI. All plans include inline suggestions and chat. Business and Enterprise plans add organization-oriented license and policy management and IP indemnity. Enterprise also integrates Copilot into GitHub.com and can index an organization’s codebase.
That combination makes Copilot a sensible first evaluation when developers already use GitHub and need consistent access management without replacing their editors. Enterprise’s GitHub.com integration and codebase indexing are particularly relevant if the team wants assistance grounded in organization repositories rather than only the open file.
The data-boundary distinction
GitHub says Business and Enterprise subscriber data is not used to train its models. That statement addresses training use; it does not say that code remains on the developer’s machine. The extension examines nearby code and other context, then sends contextual information to a model to generate a suggestion or answer. Your review therefore needs to cover transmission, retention, subprocessors, residency and model-provider terms, not only training.
Do not transfer the individual-user policy to organization accounts. GitHub describes a separate training-use policy for individual Free, Pro and Pro+ users, including an opt-out. Business and Enterprise terms are the relevant basis for a company deployment.
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When to choose it
- Developers need to stay in a mixture of supported IDEs.
- GitHub is already the identity, repository and review hub.
- Central license and policy administration is more important than self-hosting.
- The organization accepts a hosted service after reviewing its complete data flow.
Choose another path if policy requires all inference and telemetry to remain inside your own network, or if your organization needs controls that GitHub’s current plan documentation does not expose.
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Administrative scope
Cursor’s Enterprise documentation describes SAML-based single sign-on, SCIM provisioning, role permissions, centralized controls for models, MCP servers and agents, repository allow/block lists, usage controls and analytics. Those controls are useful when the main risk is unmanaged experimentation: administrators can define which models, repositories and agent capabilities are available instead of relying on individual developer settings.
Privacy Mode is a condition, not a footnote
Cursor says that when Privacy Mode is enabled organization-wide, code is not used for training and Cursor has zero-data-retention agreements with model providers. Treat the organization-wide requirement as operationally significant. A team should verify that the setting is enforced for every user and feature, understand what happens during support or abuse investigations, and obtain the provider-retention commitments in current contract language.
Deployment limitation
Cursor says its service runs on AWS and does not currently offer on-premises deployment. It is therefore a candidate for teams that accept a hosted architecture and want strong centralized editor controls, not for a policy that requires code processing to stay inside company infrastructure. Ask which components remain external, including model inference, telemetry, crash reporting and administrative analytics.
When to choose it
- The organization is willing to standardize on an AI-first editor.
- Model, MCP and agent policy enforcement is a primary requirement.
- Hosted AWS operation is acceptable after residency and subprocessor review.
- SCIM, SSO and repository controls matter more than self-hosting.
Sourcegraph Enterprise: codebase understanding and deployment choice
Why it is different
Sourcegraph Enterprise is oriented toward code search and codebase understanding as well as assistance. Its current Enterprise material lists code search and navigation, AI-powered Deep Search, batch changes, monitoring, security controls and administration. It also lists integrations with major code hosts and compatibility with tools such as Claude Code, Cursor, Codex and Amp.
This profile suits organizations where developers and agents must understand relationships across many repositories, not merely complete the current function. Search and navigation can be valuable for migrations, dependency tracing and locating implementation patterns before an agent proposes a change.
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Deployment modes require a technical review
The Enterprise offering lists both single-tenant cloud and self-hosted deployment. Those labels are not interchangeable. For each mode, document where indexing runs, where prompts and retrieved code are processed, which model performs inference, how telemetry leaves the environment, and what support access is possible. “Self-hosted” should not be treated as proof that every component, including inference, is local.
Commercial checkpoint
The current pricing page lists a $16,000 minimum annual contract, including AI feature credits. Pricing, included credits and contract terms can change, so reconfirm them during procurement and model expected usage rather than assuming the minimum covers all teams or workloads.
When to choose it
- Cross-repository search and code intelligence are central to the use case.
- The organization can operate or approve a single-tenant cloud or self-hosted deployment.
- Administrative, monitoring and security capabilities need to accompany AI features.
- The team is prepared to evaluate inference and support paths separately from the deployment label.
Amazon Q Developer: an AWS-centered candidate to investigate
Amazon Q Developer is identified in AWS material as an AWS coding assistant. That makes it a natural item for an AWS-focused team’s evaluation list, particularly when the existing identity, cloud and developer-tooling strategy is already centered on AWS.
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The available product information does not establish enough detail for a fair comparison of its retention, governance, deployment options or organization controls against the other choices in this guide. Before approving it, request current documentation covering training use, prompt and code retention, model-provider subprocessors, administrator policies, repository restrictions, regional processing, audit evidence and network options. Treat those answers as decision inputs, not assumptions based on the AWS name.
Local and open-source workflows: maximum choice, maximum responsibility
A local-model or open-source approach can give a team more choice over the model and where inference runs. It may also preserve an existing editor through an integration layer. That flexibility is attractive when the non-negotiable requirement is local processing or when procurement needs to avoid a single hosted provider.
It is not a ready-made security boundary. The result depends on the selected model and license, the runtime host, editor extension, update process, authentication, network egress, telemetry and logging. A model running locally can still leak code through an extension, diagnostic service or remote API fallback.
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Evaluate the stack as an architecture
- Map components: list the editor, extension, model runtime, model files, API endpoints, vector or code index, telemetry service and update channel.
- Set egress rules: block or approve outbound connections explicitly, including model downloads, crash reports and usage analytics.
- Control access: require enterprise identity, least-privilege repository access and auditable administrator changes.
- Inspect retention: determine whether prompts, completions, embeddings, logs or cached files persist and for how long.
- Review licenses: confirm that the model, code, editor integration and redistribution terms fit commercial use.
- Operate updates: define who patches the runtime and extension, how versions are tested and how a compromised package is removed.
Use this route when the organization can own those operational duties. Do not select a named local tool solely because it advertises local-model support; verify current project and hosting documentation first.
Compare every candidate on the same six axes
1. Data boundary
Ask for a diagram showing what leaves the IDE, when it leaves, and which providers receive it. Include open files, neighboring files, repository metadata, prompts, terminal output, embeddings, telemetry and support diagnostics. Confirm whether settings apply to every model and agent mode.
2. Deployment and residency
Distinguish hosted multi-tenant service, single-tenant cloud and self-hosted software. Identify the location and operator of inference, indexing, logs, backups and support access. If a restricted network is mandatory, test the product without Internet access rather than accepting a deployment label.
3. Administration
Check SSO, SCIM, roles, policy inheritance, model allowlists, repository allow/block lists, agent and MCP controls, quotas, suspension and audit logs. A control that exists only as a developer preference is not an enforceable organizational control.
4. Workflow fit
List the IDEs, source-control hosts, review tools and command-line workflows the team actually uses. A product that requires a new editor may deliver more capability but also creates migration, training and support cost.
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5. Codebase context
Separate inline completion from repository search, indexing, deep code understanding and multi-file changes. Test representative monorepos, generated code, private dependencies and access boundaries; a completion demo does not establish useful organization-wide context.
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Calculate seats, included usage, overage rules, model-specific charges, contract minimums, support tiers, infrastructure and staff time. Usage allowances and prices change frequently. Sourcegraph’s listed $16,000 annual minimum is a concrete example of why a per-seat estimate alone can be misleading.
A control-first evaluation process
- Write the prohibited outcomes. Examples: no code sent to an external model, no unapproved repository access, no provider retention, or no agent execution without approval.
- Classify repositories. Mark public, internal, confidential and regulated code so pilots do not accidentally use the most sensitive material.
- Request vendor evidence. Obtain data-flow diagrams, retention by model and provider, subprocessor lists, residency commitments, administrator enforcement details and audit reports.
- Configure a narrow pilot. Use test repositories, approved models and a small group. Enable the strongest privacy and access settings before inviting developers.
- Observe the network. Verify actual endpoints, payload categories, retention behavior and fallback paths under normal completion, chat, indexing and agent tasks.
- Test failure modes. Disable the network, revoke a user, block a repository, remove a model and exceed a usage limit. Record whether the control fails closed or silently changes behavior.
- Measure workflow value. Test completion, debugging, refactoring, cross-repository search and review assistance against representative tasks. Do not treat vendor adoption claims as independent benchmarks.
- Approve with conditions. Document allowed repositories, models, features, user groups, data regions, logging, incident response and the owner responsible for periodic review.
Questions procurement should get answered in writing
- What exact code, prompts, context, embeddings and telemetry are transmitted for each feature?
- Which provider receives each request, and can administrators restrict models or providers?
- How long is each data type retained, including backups, abuse-monitoring logs and support copies?
- Is customer data used for training, and does the commitment cover every model, feature and subprocessor?
- Where are inference, indexing, storage and support personnel located?
- Can SSO, SCIM, role permissions, repository restrictions, model policies and agent settings be enforced centrally?
- What happens when a user loses access, a repository is blocked or a contract ends?
- For self-hosted or single-tenant options, which components still call an external service?
- What audit evidence, incident-notification terms and subprocessor-change notices are available?
- How are usage limits, overages, model charges, credits and contract minimums calculated?
Common mistakes and how to avoid them
Confusing training policy with transmission policy
A provider can state that data is not used to train models while still processing code to produce a response. Evaluate both claims separately.
Assuming a privacy toggle covers every path
Check whether the setting is organization-enforced and whether it covers chat, indexing, agents, MCP servers, telemetry and every selected model. Cursor’s stated commitments, for example, depend on organization-wide Privacy Mode.
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Treating hosted, single-tenant and self-hosted as synonyms
They describe different operational boundaries. Ask who runs inference and who can access logs in each mode.
Choosing on feature count alone
A long feature list does not compensate for a failed non-negotiable. Eliminate candidates that cannot meet the boundary before comparing convenience or completion quality.
Piloting with sensitive production code
Start with classified test repositories and instrument the network. Expand only after the documented controls match observed behavior.
Ignoring ongoing operations
Model changes, extension updates, new agent tools and subprocessor changes can alter risk. Assign an owner and review settings, contracts and network behavior on a defined schedule.
Which path is most likely to fit?
Start with GitHub Copilot Business or Enterprise when broad IDE support and GitHub-integrated administration solve the problem and a hosted service is acceptable. Evaluate Cursor Enterprise when centralized model, MCP and agent controls are the priority and an AI-first editor is a reasonable workflow change. Put Sourcegraph Enterprise at the top of the list when cross-repository code intelligence and a single-tenant or self-hosted option are essential, subject to confirming inference and commercial terms. Add Amazon Q Developer for an AWS-centered investigation, but require the missing governance and retention answers. Choose a local or open-source architecture only when the team is prepared to operate the model, editor integration and security boundary itself.
The responsible decision is not the product with the most impressive demo. It is the option whose actual data flow, deployment, administration and operating obligations fit the boundary your organization is unwilling to compromise.
Quick Recap
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