Tabby
Self-hosted coding assistant designed so that no code or prompt ever leaves your infrastructure
Quick Overview
| Company | TabbyML (open source project) |
|---|---|
| Category | AI Coding Assistants |
| Headquarters | Distributed, Open Source |
| Founded | 2023 |
| EU Presence | Runs wherever you deploy it, including EU-only infrastructure |
| Data Location | Entirely on your own servers |
| Open Source | Yes (Apache 2.0) |
| GDPR Compliant | Yes - you remain the sole processor |
| Self-Hosting | Yes - the only deployment model |
| Main Features | Self-hosted completion, repository context indexing, answer engine, team management, OpenAPI interface, consumer-GPU support |
| Pricing | Free Community plan (open source, up to 5 users) / Team $19 per seat/month (up to 50 users) / Enterprise custom pricing |
| Best For | Organisations with a hard requirement that source code never leaves the network |
| Replaces | GitHub Copilot, Tabnine, Codeium |
Detailed Review
Tabby starts from the assumption that the answer to "where does our code go" has to be "nowhere". The coding assistant is a self-contained server you run yourself, with IDE plugins that talk only to it. The one thing to switch off is basic usage statistics (model, hardware and version), which Tabby collects by default and which an environment variable disables.
It is Apache-licensed and deliberately modest in its hardware requirements: Tabby runs usefully on consumer GPUs, which makes an on-premise deployment realistic for a small team rather than only for organisations with a GPU cluster.
Deployment and Hardware
A single Docker command brings up the server with a chosen model. Tabby supports CUDA, ROCm and Apple Metal, so an existing workstation GPU or even a well-specified Mac can serve a small team. The open-source Community plan covers up to five users; larger teams move to paid per-seat Team or Enterprise plans.
Because it runs on infrastructure you already own, the hardware side of the cost model is different from a pure cloud subscription, though beyond five users the Team plan at $19 per seat per month applies.
Repository Context and the Answer Engine
Tabby indexes your repositories and documentation so completions and answers reflect your own code rather than generic patterns. The answer engine lets developers ask questions about the codebase — where a function is used, how a service is configured — with responses grounded in the indexed source.
For onboarding new developers into a large internal codebase, that grounded search is often more valuable day to day than the completion itself.
Governance
The project is maintained by a US-incorporated company, but because the self-hosted server needs no cloud service and its usage statistics can be disabled, running it creates no data-processing relationship over your code. From a GDPR perspective you remain the sole controller and processor, which is the cleanest possible position.
Limitations to Consider
Self-hosted models are meaningfully weaker than frontier models on complex, multi-step reasoning, and Tabby's agentic capabilities are limited compared with Cursor or Claude Code. You take on the operational work — GPU capacity, container updates, model upgrades. And while the deployment is yours, the project maintainer is a US company, so this is a self-hosting choice rather than a European-vendor choice.
Who Should Use Tabby
Defence suppliers, financial institutions, healthcare software teams and anyone under contractual confidentiality obligations that rule out third-party AI services entirely. Where the requirement is absolute rather than preferential, Tabby is the most straightforward option in the category.
Pros and Cons
Pros
- No cloud dependency; usage stats can be switched off
- Runs on consumer-grade GPUs
- Repository-grounded answer engine
- Apache 2.0, free for up to 5 users
- You remain sole data controller
Cons
- Local models weaker on complex reasoning
- Limited agentic capability
- Maintainer is a US company
Alternatives to Tabby
Other European ai coding assistants worth comparing before you decide:
Mistral AI
France · European frontier models with Codestral for code generation
Hugging Face
France · Open model hub with code models you can run yourself
JetBrains AI Assistant
Netherlands · AI built into the IDEs your team already uses
See all European ai coding assistants or compare them directly against GitHub Copilot on our alternatives page.
Frequently Asked Questions
The Tabby coding assistant itself is self-hosted by design, so your code stays on your server. TabbyML does separately sell Tabby Cloud for its Pochi agent, a different hosted product you would have to choose deliberately.
It supports CUDA, ROCm and Apple Metal, and runs usefully on consumer GPUs. A single workstation-class GPU is enough to serve a small team.
Both are open source and self-hosted. Refact.ai adds fine-tuning on your own codebase and a more developed agent; Tabby is simpler to deploy and has a stronger repository-grounded answer engine. Either satisfies a no-code-leaves-the-network requirement.
Running it self-hosted, you remain the sole controller and processor of the data involved, which is the simplest possible GDPR position — there is no third-party processor to assess at all.