Open source GitHub Copilot alternatives: Tabby, Continue and local models

What part of Copilot are you replacing?

GitHub Copilot started as inline autocomplete and grew into chat, code review and agents. This guide focuses on the original job: fast, ghost-text completions as you type. Chat and agent features are covered better by the Cursor-alternative tools.

Autocomplete has unusual requirements. It must answer in a fraction of a second, fire on nearly every keystroke pause and use the code around the cursor, including text after it. That favours small, specialised models over big general ones.

Those constraints make autocomplete one of the best candidates for self-hosting. A modest model close to the developer can feel faster than a large one far away.

It also means the model can be swapped as better small code models appear, without changing the editor plugin your developers use every day.

Which open source Copilot alternatives exist?

Whichever option you shortlist, check recent release activity and open issues for your editor’s plugin. Plugin quality, not model quality, is often what developers notice first.

How does Tabby work for a team?

Tabby runs as a server, typically in Docker on a machine with a GPU, and developers install a plugin for VS Code, JetBrains or Vim-family editors. The server hosts the completion model and handles requests from everyone.

Its advantage over per-laptop setups is administration: one place to pick the model, manage users and connect repositories so completions can draw on your own code as context. Developers only install a plugin and paste a token.

The cost is operating a GPU service. Latency depends on the model size and the GPU, so test with your team’s real editing habits before rolling it out broadly.

Tabby also offers a chat panel and answer features in its editor plugins, so a team can start with completion and add chat on the same server later. Check which features are in the open edition you deploy.

How do you set up Continue with a local model?

Use a model designed for fill-in-the-middle. General chat models can complete code, but they handle the text after the cursor poorly and tend to ramble.

• Install Ollama on your machine and pull a small code model built for fill-in-the-middle completion.
• Install the Continue extension in VS Code or JetBrains.
• In Continue’s configuration, set the autocomplete model to the Ollama model you pulled.
• Optionally add a larger model for chat, local or hosted, as a separate entry.
• Tune the debounce delay and maximum completion length until suggestions feel responsive.
• Disable telemetry in the extension settings if your policy requires it.

What hardware do you need, and how do you measure success?

For one developer, small quantized code models run on recent Apple silicon laptops and on desktop GPUs with a reasonable amount of video memory. CPU-only completion works but usually feels sluggish.

For a team, a single GPU server shared through Tabby or another inference server is usually more efficient than upgrading every laptop. Completion requests are short, so one card can serve several developers if the model is small.

Measure latency, not just quality. A suggestion that arrives after you have typed the line is worth nothing.

Quantization helps a lot here. A quantized version of a code model uses much less memory with a modest quality loss, which often makes the difference between a responsive setup and a sluggish one.

• Ask developers after two weeks whether they would keep it; honest feedback beats dashboards.
• Track acceptance of suggestions if your tool reports it, and watch the trend rather than the absolute value.
• Measure median latency from a normal developer machine, not from the server itself.
• Note which languages get poor suggestions; a different model may suit them better.
• Review a sample of accepted completions for security issues such as hardcoded secrets.

How to choose between them

Whichever you pick, run a pilot with a few developers who use different languages. Completion quality varies more by language than most people expect.

Also agree on a fallback. If the completion server is down, developers should simply see no suggestions, not a stream of errors in the editor.

• One developer, strong laptop: Continue with Ollama is the quickest path.
• Team with a spare GPU server: Tabby gives central control and repository context.
• Strict data policy: any of these, as long as the model runs on your infrastructure.
• Mixed editors across the team: check plugin support for every editor before deciding.
• Need chat and agents too: pair a completion setup with a separate agent tool rather than forcing one tool to do everything.

Common mistakes with self-hosted autocomplete

The last point is easy to miss. The tool may be permissively licensed while the model you load has its own conditions.

• Choosing the biggest model that fits in memory and ending up with slow completions.
• Using a chat model without fill-in-the-middle support for inline completion.
• Exposing the completion server on the internet without authentication.
• Indexing every repository for context, including secrets that should never be suggested.
• Judging quality on day one before tuning context length and debounce settings.
• Forgetting that code model licences differ; read the model card before company-wide use.

When is Copilot still the better choice?

If your code already lives on GitHub, your policy allows cloud processing and nobody wants to maintain a GPU server, the hosted product is the lower-effort option. Self-hosting pays off when data rules, cost control or model choice outweigh convenience.

Many teams end up hybrid: self-hosted completion for private repositories, hosted tools for open source work. RepoLoot’s catalog tags developer tools by difficulty, which helps you estimate how much operational work a self-hosted option really implies.

Regulated industries are the clearest case for self-hosting, because the question of where code is processed has a simple answer: on your own machines.

A team completion server is a reusable asset. The same inference endpoint can power code review bots, commit message suggestions or documentation drafts inside your CI system, all without sending code outside your network.

Tabby’s repository indexing is also useful beyond autocomplete. When the server knows your internal libraries, suggestions follow your house patterns rather than generic public code, which is often the biggest quality gain over hosted tools.

Start with completion only, then add these extras once developers trust the basic suggestions.

How do these options compare on the details?

The table below compares the practical questions that decide adoption once basic completion works. Answers depend on configuration, so verify against current documentation for your version.

Frequently asked questions

Is Tabby a free alternative to GitHub Copilot?

Tabby’s core is open source and free to self-host, and it offers additional enterprise features under separate terms, so check the licence file. You still pay for the hardware that runs the model, usually a GPU server, plus the time to operate it.

Can I get Copilot-style autocomplete fully offline?

Yes. Run a code model locally through Ollama or a similar runtime and connect it with the Continue extension, or run Tabby on a machine inside your network. No code leaves your environment, provided the extension’s telemetry is disabled as well.

Which model type works best for code completion?

Use small code models trained for fill-in-the-middle, which see the code both before and after the cursor. They respond quickly and produce focused completions. Large general chat models are better kept for chat and agent tasks, where latency matters less.

Do these alternatives support JetBrains IDEs?

Tabby and Continue both provide JetBrains plugins alongside VS Code support, and Tabby also supports Vim-family editors. Plugin maturity varies by editor, so test your team’s main IDE before rolling out, and check the project documentation for the current list.