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Short answer: Tabnine Enterprise is the strongest choice when you need a documented private or completely air-gapped product with vendor support. Tabby is the most direct self-hosted, open-source server. Continue.dev is the most adaptable IDE and CLI layer if your platform team already runs model endpoints. Sourcegraph Cody is compelling when Sourcegraph Enterprise, repository-wide search and cross-repository context are central to your workflow.
All four can keep some or all development traffic inside infrastructure you control, but they are not interchangeable. The critical difference is where the model runs, who operates the Kubernetes or GPU stack, what data is retained, how repositories are indexed, and which enterprise controls are included. Use the comparison and pilot process below to select an architecture rather than choosing on autocomplete quality alone.
What “on-premises” means for an AI coding assistant
On-premises development can describe several boundaries. A product may run on servers in your data centre, in a private cloud VPC, or in a network with no internet route at all. The IDE extension can also be installed locally while sending prompts to a vendor-hosted model, which is not an air-gapped design. Document the boundary you actually require before comparing products.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Private network or VPC: application and model services run in an isolated customer environment, but controlled egress may still be available for updates or telemetry.
- On-premises: the service is hosted on servers your organisation operates, usually with Kubernetes and internal identity, storage and networking.
- Air-gapped: the production network has no live connection to the public internet. Model files, container images, licenses and updates must enter through an approved transfer process.
- Customer-operated model: your team chooses and serves the model. This gives the most control but also makes GPU capacity, patching, latency and model evaluation your responsibility.
Ask each supplier to map every component: IDE plug-in, API gateway, inference server, repository index, logs, authentication, update service and support channel. A “private” label is not sufficient if one of those components still requires outbound access or sends source context to a third party.
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Shortlist at a glance
| Product | Deployment boundary | Who operates the model | Context and integrations | Best fit | Main trade-off |
|---|---|---|---|---|---|
| Tabnine Enterprise | Vendor documents VPC, on-premises and completely air-gapped patterns; private installation is a Kubernetes cluster on the customer’s servers or private network. | Tabnine’s enterprise deployment, subject to the agreed architecture and contract. | Enterprise administration and privacy controls; confirm exact IDE, repository and CI integrations for your edition. | Regulated or classified teams wanting a supported product and a documented air-gap path. | Enterprise access and pricing require a sales process; current comparable prices and SLA terms are not published in the supplied material. |
| Tabby | Self-contained, self-hosted service with no required DBMS or cloud service. | Your team selects and serves the models; the project documents Docker examples using StarCoder and Qwen. | OpenAPI interface and an extensible server; integration and administration remain your responsibility. | Teams that want an open-source, local control plane and are prepared to operate it. | You own model quality, GPU sizing, upgrades, support and enterprise integration work. |
| Continue.dev | IDE and CLI clients connect to endpoints you host, including OpenAI-compatible servers or Ollama. | Your platform team or another infrastructure provider. | Role-based model configuration, custom prompts and rules; can use TGI, vLLM, SkyPilot, Anyscale Private Endpoints or Lambda. | Platform teams that already run model endpoints or need different models for chat, edits and autocomplete. | It does not supply the model, GPU cluster, identity boundary or enterprise support contract. |
| Sourcegraph Cody | Available with Sourcegraph deployments; BYOK with managed services is documented for self-hosted Sourcegraph instances. | Depends on the Sourcegraph edition and selected provider; verify whether each feature can use your internal endpoint. | Search API context, repository-wide and cross-repository understanding, chat, completions, edits and customizable prompts; clients for VS Code, JetBrains, Visual Studio and the web app. | Organisations where Sourcegraph Enterprise and code search are strategic. | Do not assume a fully air-gapped design or universal guardrail support without checking edition, provider and egress requirements. |
Tabnine Enterprise: the clearest documented private path
Tabnine documents three private patterns: VPC, on-premises and completely air-gapped deployment. In its on-premises private installation, the Tabnine cluster is a Kubernetes cluster hosted on the customer’s servers on the customer’s private network. That gives infrastructure and security teams a concrete deployment boundary to review rather than an informal promise that data is “private.”
Tabnine also states that its no-train/no-retain policy applies to enterprise private deployments and that context used for inference is deleted after the response. Treat those as vendor statements to be written into procurement and validated during an architecture review. Confirm what is logged for troubleshooting, how administrative metadata is handled, and whether any support workflow can access prompts or generated code.
Where Tabnine fits
- Use it when a regulated, classified or contractually restricted environment needs a supported product instead of assembling an internal platform.
- Prefer it when an explicit air-gap design, central administration and a defined vendor escalation path matter more than choosing every model component yourself.
- Require the supplier to specify Kubernetes versions, image-delivery procedures, identity integrations, upgrade mechanics and offline licensing before approval.
What to validate
The supplied material does not provide a comparable current enterprise price, SLA or feature-by-feature air-gapped model compatibility matrix. Obtain those terms directly. Ask whether autocomplete, chat, repository context, analytics and support all remain inside the isolated cluster, and test a denied-egress policy rather than relying on a diagram.
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Tabby’s official repository describes it as “a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot.” The service is designed to be self-contained: it does not require a separate DBMS or cloud service and exposes an OpenAPI interface. The repository also describes support for consumer-grade GPUs and includes a Docker example using a StarCoder completion model and a Qwen instruction model.
This architecture is attractive when your organisation wants the control plane, model files and network path under its own ownership. You can choose a model appropriate to your languages and hardware, expose the API only on an internal network, and integrate it with your own identity gateway. The same freedom means there is no vendor-managed answer to model selection, capacity planning or patch scheduling.
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Operating responsibilities
- Choose model families, quantization and context limits for completion and instruction tasks.
- Size GPUs and CPU resources for the number of concurrent developers and your latency target.
- Build image and model-transfer procedures that work without public internet access.
- Monitor errors, queue time, token throughput, storage use and security events.
- Review upstream releases, test upgrades and maintain rollback images.
Tabby’s pricing page lists Community, Team and Enterprise packaging, including local deployment and enterprise security features. Confirm which support, administration and security capabilities are included in the package you would purchase; “local deployment” alone does not describe the operational service level.
Continue.dev: a flexible client for customer-hosted models
Continue is best understood as an IDE and CLI layer rather than a complete private AI platform. Its self-hosting guidance names Hugging Face Text Generation Inference (TGI), vLLM, SkyPilot, Anyscale Private Endpoints and Lambda as possible serving options. Any OpenAI-compatible server can be configured with a custom base URL, and the documentation shows an Ollama configuration as well.
That separation is useful for platform teams. You can route chat to a larger instruction model, autocomplete to a lower-latency completion model and edits to another endpoint, while keeping the client configuration consistent for developers. Continue’s customization features let you define rules and prompts so that generated code follows internal conventions. The endpoint, model, authentication and logging policy remain yours to implement.
Questions to settle before standardising on Continue
- Which endpoint serves each role, and does it support the context length and streaming behavior the extension expects?
- How does the client authenticate to the endpoint: short-lived tokens, mutual TLS, an internal gateway or another method?
- Where are prompts, responses and error logs stored, and how are sensitive files excluded from context?
- Who owns on-call response when the inference service is saturated or unavailable?
Continue is a strong choice when the organisation already has a model-serving platform. It is not, by itself, a replacement for that platform, an identity system or a vendor support agreement.
Sourcegraph Cody: repository-wide context with deployment caveats
Cody is available in VS Code, JetBrains, Visual Studio and the Sourcegraph web application. Sourcegraph says Cody combines local and remote code context through its Search API and supports chat, completions, edits and customizable prompts. That repository-wide context can be more valuable than a standalone autocomplete server when developers need to understand symbols, dependencies and patterns spread across many repositories.
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Sourcegraph Enterprise documentation notes that bring-your-own-key (BYOK) with managed services is supported for self-hosted Sourcegraph instances. It also notes that some guardrails are limited to Sourcegraph Model Provider deployments. Therefore, “self-hosted Sourcegraph” and “fully air-gapped Cody” are not automatically equivalent. Verify the exact Sourcegraph edition, model provider, network egress and feature support for every Cody capability you plan to enable.
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When Cody is the right architectural choice
- Your organisation already operates Sourcegraph and wants coding assistance to use its indexed, cross-repository context.
- Code search, ownership metadata and repository navigation are strategic developer-platform capabilities.
- You can accept a procurement and architecture review that distinguishes Sourcegraph-hosted services, BYOK endpoints and completely disconnected operation.
If the requirement is an absolute air gap with no external model or service dependency, make that a pass/fail acceptance criterion and test it with firewall rules and packet inspection.
Infrastructure and GPU planning
Model serving is the hidden project in any self-hosted design. An AMD Enterprise AI Kubernetes/Helm blueprint for a Continue coding assistant on AMD Instinct hardware lists a default reference configuration of three GPUs, 12 CPU cores and 256 GiB of RAM. That is a reference deployment, not a universal requirement. Actual capacity depends on model size, quantization, context window, concurrent users, batching and the latency target.
Measure the workload instead of copying a reference
- Count expected active developers during the busiest hour, not only total licensed users.
- Separate autocomplete requests from chat and edit requests; autocomplete usually has a tighter latency target and shorter prompts.
- Choose candidate models and record memory use, first-token latency, tokens per second and error rate at realistic concurrency.
- Test repository-context retrieval separately from generation so an index or search bottleneck is not misdiagnosed as a GPU problem.
- Reserve headroom for model reloads, rolling upgrades and a node failure.
Consumer-grade GPU support can reduce entry cost for a Tabby deployment, while enterprise accelerators and a managed Kubernetes stack may simplify support and scaling. Compare the total operating burden, not just the purchase price of a card.
How to choose: a decision process for security and platform teams
- Classify the data boundary. Decide whether private VPC, on-premises or no-egress air-gapped operation is mandatory. Include source code, prompts, completions, repository indexes, logs and telemetry.
- Assign ownership. Choose between a vendor-operated private product (Tabnine), a self-operated server (Tabby), a customer-hosted model behind a flexible client (Continue), or a Sourcegraph-centered platform (Cody).
- Define context requirements. A single-repository assistant has different needs from one that must search dozens of repositories, respect code ownership and answer questions about shared services.
- List developer surfaces. Record required support for VS Code, JetBrains, Visual Studio, web access, CLI workflows and CI jobs. Confirm each surface for the edition and deployment mode under consideration.
- Set operational targets. Specify acceptable autocomplete latency, chat response time, concurrent users, availability, recovery time and upgrade windows.
- Make privacy contractual. Capture retention, training use, deletion timing, support access, telemetry and subprocessors in the agreement. A product statement is not a substitute for enforceable terms.
- Run a controlled pilot. Use representative repositories, synthetic secrets and a blocked-egress test. Compare usefulness and operational effort under the same tasks rather than relying on a vendor demo.
Air-gapped deployment checklist
An air gap changes software distribution and support as much as it changes networking. Before production approval, document the following:
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- Container images, model weights, tokenizer files, licenses and vulnerability reports are transferred through an approved removable-media or staging process.
- Image signatures and hashes are verified inside the disconnected environment.
- Model and extension updates have a tested rollback version and a documented release cadence.
- Identity, role mapping and administrator access work against internal directory services without cloud callbacks.
- Logs are retained in an approved internal system and exclude source snippets unless explicitly required.
- Help-desk and vendor-support procedures provide diagnostics without exporting confidential code.
- Network policies deny unexpected DNS, HTTPS and telemetry destinations; test the denial during the pilot.
- Repository indexing, cache storage and backups are encrypted and covered by the same data-classification rules as source control.
Integration, governance and security questions
Ask how the assistant connects to GitHub or GitLab, CI systems, code search and identity. A completion extension may need only an internal API, while repository-aware chat may need read access to many projects. Use least-privilege service accounts, separate indexer credentials from developer credentials, and record which repositories each user can retrieve.
For pull requests and CI, decide whether generated suggestions are advisory or can block a pipeline. Preserve attribution and review history without treating generated code as automatically safe. Scan outputs with the same secret, license and vulnerability controls used for human-authored code.
Administration should include user and group provisioning, audit events, model and prompt configuration, rate limits, usage visibility and emergency disablement. Ask whether these controls are native, supplied by Sourcegraph or Tabnine, available through an API, or something your team must build around Tabby or Continue.
Licensing, support and upgrade diligence
The supplied product information does not establish comparable current enterprise prices, SLA commitments or a complete air-gapped model-compatibility matrix. Treat pricing pages and feature lists as starting points for procurement, not as equivalent offers. Request written answers for the exact deployment pattern, number of developers, support hours, response targets, offline licensing and upgrade assistance.
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Open-source availability does not eliminate cost: engineering time, GPU capacity, security review, monitoring and incident response become internal expenses. A commercial private product may cost more in license fees while reducing the work required to maintain model-serving infrastructure. Score both categories in the business case.
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What to test in a two-week pilot
- Install each candidate in the intended network segment and verify that normal operation succeeds with outbound access denied where required.
- Use a representative set of repositories containing the languages, build systems and architectural patterns your developers use.
- Measure autocomplete latency, chat usefulness, edit accuracy, retrieval precision, failure rate and resource consumption at realistic concurrency.
- Attempt to retrieve a repository the test user is not authorised to read; the assistant must respect source-control permissions.
- Inspect application, proxy, indexer and model-server logs for source-code retention and unexpected destinations.
- Perform a model or container upgrade, then roll back to the previous version and record downtime and operator steps.
- Have developers complete the same maintenance tasks with each tool and record where context, navigation or integration saves time.
- Finish with a security and procurement review that turns every unresolved assumption into an acceptance criterion or contract term.
Practical recommendations by team profile
| Your situation | Starting choice | Reason |
|---|---|---|
| Classified or highly regulated environment with a strict air gap and limited appetite for building an AI platform | Tabnine Enterprise | It has the clearest documented private and completely air-gapped deployment patterns and a supported enterprise operating model. |
| Infrastructure team wants an internally operated, self-contained service and can own model and GPU decisions | Tabby | Its open-source, on-premises server avoids a required cloud control plane and exposes an OpenAPI interface. |
| Existing model-serving platform with several endpoints and a need to assign models by task | Continue.dev | The client can target OpenAI-compatible servers, Ollama and named private serving options while keeping rules and prompts configurable. |
| Sourcegraph Enterprise is already the system of record for code search and cross-repository navigation | Sourcegraph Cody | Cody can use Search API context across repositories, provided the chosen edition and provider meet your network and guardrail requirements. |
No independent cross-vendor performance benchmark or named laboratory statistic establishes a universal winner. The right decision is the one that meets your data boundary, context needs and operating capacity under a controlled pilot.
Frequently Asked Questions
Can a private deployment still send data to a cloud service?
Yes. A locally installed extension or application can still call a hosted model, telemetry endpoint, license service or support channel. Require a component-by-component data-flow diagram and verify it with firewall and proxy logs.
Is an air-gapped assistant possible without GPUs?
You can operate the control plane without a GPU, but useful local inference normally requires suitable accelerator or CPU capacity. Select a model and latency target first, then benchmark the hardware; the AMD reference configuration is not a universal minimum.
How should an offline team receive model and security updates?
Use a controlled staging network or approved removable-media process to import signed images, model files and vulnerability information. Verify hashes inside the isolated environment, test upgrades, and retain a rollback version.
Can one organisation use more than one of these tools?
Yes, but define ownership and data boundaries per workflow. For example, Continue can provide a common IDE layer over internal endpoints while Sourcegraph Cody serves repository-wide search users; avoid sending the same restricted code to multiple independently governed systems.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
