Best Self-Hosted AI Coding Assistants for Private Code

For most teams, Continue is the best starting point for a self-hosted AI coding assistant: it works in familiar editors, can connect to models you run locally or serve privately, and is flexible enough to fit different stacks. If you want an integrated coding environment rather than an extension, choose a self-hosted Tabby deployment; the right pick ultimately depends on your hardware, privacy boundary, and appetite for operating infrastructure.

What to Look For

“Self-hosted” can mean different things. A model may run on a developer’s workstation, on a company server, or inside a private cloud account. An editor extension can still send prompts to an external model provider, so check the full request path—not just where the assistant’s interface runs.

• Data handling: Find out whether prompts, code, embeddings, and telemetry leave your environment. Check logging, retention, access controls, and whether any optional cloud services are enabled.
• Model choice: Support for local models gives you more control, but model quality varies. Check compatible runtimes and model formats, and test your languages and repository tasks rather than relying on a model’s general reputation.
• Editor and workflow fit: Inline completions, chat, code edits, terminal access, and repository context are distinct capabilities. Confirm the features you need work in your editor and with your chosen model.
• Deployment and upkeep: Consider GPU or memory requirements, setup effort, upgrades, backups, and who will maintain the service. A self-hosted product is not automatically low-maintenance.
• Security and governance: Look for authentication and permission controls appropriate to your deployment. Treat generated code as untrusted: review changes, run tests, and do not grant tools broader access than needed.
• Cost at your scale: Software may be open source while hardware, hosting, and staff time are not. Compare the cost of local inference with a private API deployment and your team’s usage.

Continue: Best Flexible Editor Extension

Continue is an open-source coding assistant available as extensions for popular editors, including VS Code and JetBrains IDEs. It supports configurable models and workflows, so you can connect a local model runtime or a privately managed model endpoint. That makes it a practical first trial if developers want to keep their existing editor and compare models without committing to one vendor.

Strengths: Flexible configuration, familiar editor workflows, and the ability to use different models for chat and autocomplete. You can tailor context sources and behavior to your codebase.

Trade-offs: Flexibility brings setup work. Performance and features depend on the selected model and provider; running the extension locally does not prove that every request stays local. Verify integrations, data flows, and current feature support before rollout.

Best for: Teams that want a customizable editor assistant and have someone comfortable configuring models and endpoints.

Tabby: Best Dedicated Self-Hosted Service

Tabby is an open-source, self-hosted coding assistant designed to provide code completion and related assistant features through a service your organization operates. It is a strong candidate when a team wants a centrally managed option rather than asking each developer to assemble a separate local setup.

Strengths: A service-oriented deployment can make administration and shared access easier. It is built specifically for coding assistance, with editor integrations and support for deploying models under your control.

Trade-offs: You need to provision and maintain the service, assess hardware requirements, and test whether its model and editor support meet your needs. “Self-hosted” still requires careful configuration of network access, authentication, and logs.

Best for: Organizations that prioritize centralized deployment and can dedicate infrastructure or engineering time to operate it.

Ollama with an Editor Assistant: Best for Local Experiments

Ollama runs supported language models locally on a developer machine or a server. It is not, by itself, a complete coding assistant experience; pair it with a compatible editor tool, such as Continue, to add chat or code-completion workflows.

Strengths: A straightforward way to try local inference and switch among supported models. Running on a workstation can keep inference inside that machine, provided the surrounding editor integration is also configured not to call an external service.

Trade-offs: Quality and speed are constrained by the machine and model. Sharing a local service across a team adds network, access-control, and capacity concerns. Check the current compatibility and performance of the exact model and integration.

Best for: Individual developers and small teams exploring private, local workflows before investing in a managed deployment.

OpenHands: Best for Repository-Level Agent Tasks

OpenHands is an open-source platform for AI agents that can work on software-development tasks, including interacting with repositories and development tools. It is a different category from a lightweight autocomplete extension: it is aimed at delegated, multi-step work and is often deployed in a controlled environment.

Strengths: A better fit for tasks that involve examining a repository, making coordinated changes, and using development tools than simple line completion. It can be run in an environment you manage.

Trade-offs: Agentic work requires stronger boundaries. Review what the agent can read, execute, and modify; use isolated environments and inspect changes before merging. Its usefulness depends heavily on the model, configuration, and task.

Best for: Teams experimenting with supervised coding agents, not developers seeking only fast inline suggestions.

Local Models Through vLLM or a Private Endpoint: Best for Platform Teams

For larger teams, serving an open model with infrastructure such as vLLM—or using a model endpoint in a private cloud environment—can provide centralized control over capacity, access, and deployment. Pair the endpoint with an editor assistant that supports the relevant API or model interface.

Strengths: A platform team can manage model serving, scale, monitoring, and network boundaries centrally. This approach can support multiple developer tools and workloads.

Trade-offs: It is an infrastructure project, not a plug-and-play assistant. Compatibility, throughput, GPU availability, authentication, and model quality need validation. A private cloud endpoint may still involve a third party, so read its data-handling terms and configure it accordingly.

Best for: Organizations with platform engineering capacity and a clear need to manage inference centrally.

How to Choose

Start with the privacy requirement. If code must not leave a developer’s computer, test a fully local model and confirm the editor, embedding, and telemetry settings. If company-controlled servers are acceptable, a central Tabby or model-serving deployment may be easier to govern. If a private cloud is permitted, verify the provider’s processing and retention terms rather than assuming “private” means on-premises.

Match the tool to the task. For inline completion and everyday editor chat, try Continue with a local runtime or approved endpoint. For a shared service, evaluate Tabby. For repository-wide delegated tasks, test OpenHands in an isolated environment. Do not expect a compact local model to match every hosted model on complex reasoning or large codebases.

Set a realistic budget. A workstation may be the cheapest way to pilot local inference, but performance varies with hardware and model size. Team deployments add server capacity, operations, monitoring, and upgrades. Compare those costs with a privately managed endpoint and include engineering time—not just software fees.

Run a representative pilot. Use a few real tasks from your languages and repositories. Measure suggestion usefulness, latency, context accuracy, and how often developers need to correct output. Also check behavior on proprietary code, access permissions, and failure recovery. Roll out only after establishing review and testing practices.

Frequently Asked Questions

Does self-hosted mean code never leaves the company?
Not necessarily. An editor plugin may call an external model, telemetry service, or embedding provider. Trace and configure every component.

Can I run a coding assistant without a GPU?
Some smaller models can run on CPU, but speed and capability depend on the model and hardware. Test your workload before choosing a deployment.

Is Continue a model?
No. Continue is an assistant and editor integration; it connects to models, which may run locally or remotely.

Which option is easiest to start with?
For a personal trial, pair Continue with a supported local model runtime such as Ollama. A shared deployment takes additional setup and administration.

Are locally hosted models as capable as hosted models?
It depends on the model, hardware, and task. Local options can offer strong privacy and control, while some demanding coding tasks may benefit from larger or more capable models.

Can an assistant safely make changes automatically?
It can produce useful edits, but output can be wrong or unsafe. Limit tool permissions, inspect diffs, and run appropriate tests before accepting changes.

Bottom Line

Choose Continue as the best general starting point for private-code workflows: it fits existing editors and lets you select a local or privately managed model. Choose Tabby when centralized self-hosting is the priority, and consider OpenHands for supervised repository-level agent work. In every case, verify where data travels and pilot the exact model, integration, and deployment you intend to use.

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