The Problem: Your Code Is Leaving Your Network

You type a comment, and GitHub Copilot suggests the implementation. You write a function signature, and your IDE fills in the body. It’s magic—until you realize what’s happening: every keystroke, every variable name, every business logic fragment is being sent to someone else’s servers.

For personal projects, maybe that’s fine. But if you’re working on proprietary codebases, client projects, or anything with compliance requirements (PIPEDA, HIPAA, SOC 2), that’s a problem. Your AI coding assistant has seen your code. Do you know where it goes? How long it’s retained? Who can access it?

This is why self-hosted AI coding assistants exist. They give you Copilot-like code completion without the cloud dependency. Two of the most popular open-source options are Continue and Tabby—but they take very different approaches. Let’s figure out which one fits your workflow.

Quick Answer: Which Should You Choose?

If you want a drop-in Copilot replacement that works with any LLM: Choose Continue. It’s an IDE extension (VS Code, JetBrains) that connects to Ollama, OpenAI, Anthropic, or local models. Flexible, battle-tested, and actively developed.

If you want a self-contained code completion server for your team: Choose Tabby. It bundles everything into a single Docker container—including a built-in vector database for code-aware context. Deploy once, everyone connects.

If you’re a solo developer experimenting: Start with Continue + Ollama on your laptop. You’ll learn how local LLMs work before committing to infrastructure.

Candidates Overview

Continue (continue.dev)

What it is: An open-source IDE extension that brings AI code completion and chat to VS Code and JetBrains IDEs. It doesn’t ship with a model—it connects to whatever LLM you provide.

Key strengths:

  • Works with VS Code, IntelliJ, PyCharm, GoLand, and other JetBrains IDEs
  • Connects to Ollama, LM Studio, OpenAI, Anthropic, Azure, or any OpenAI-compatible endpoint
  • Active community and frequent updates
  • Chat interface, code explanation, test generation, and refactoring tools
  • No server required for local use—runs entirely in your IDE

Key limitations:

  • Each developer configures their own model connection
  • No built-in code indexing (relies on the model’s context window)
  • Requires an LLM backend to be useful—no model included

Best for: Individual developers or small teams who already run local LLMs and want maximum flexibility.

Tabby (tabby.com)

What it is: A self-hosted AI coding assistant that runs as a server. It includes a code indexer, vector database, and LLM runner in a single Docker container. Team members connect via IDE extensions.

Key strengths:

  • All-in-one Docker deployment—no external dependencies
  • Built-in code indexing with vector search for context-aware suggestions
  • Team-friendly: deploy once, everyone connects
  • OpenAI-compatible API (can use as a backend for other tools)
  • Supports Git repository indexing for project-wide context

Key limitations:

  • Heavier resource requirements (runs a server + vector DB)
  • Fewer IDE integrations than Continue
  • Younger project with smaller community

Best for: Teams that want a shared, centrally-managed coding assistant with code-aware context.

Feature Comparison

Feature Continue Tabby
Deployment model IDE extension (local) Server + IDE extension
LLM support Ollama, OpenAI, Anthropic, custom Built-in, or external OpenAI-compatible
IDE support VS Code, JetBrains (all) VS Code, JetBrains (limited)
Code indexing No (relies on context window) Yes (built-in vector DB)
Team deployment Per-developer config Central server, shared config
Chat interface Yes Yes
Code completion Yes (inline) Yes (inline)
Git integration No Yes (repository indexing)
License Apache 2.0 Apache 2.0

Decision Guide

Your Scenario Choose Because
Solo developer, already running Ollama Continue Zero additional infrastructure—just point it at your existing LLM
Team of 5+ developers Tabby Central deployment, consistent config, shared code index
Need project-wide context Tabby Vector search indexes your entire repo for smarter suggestions
Using JetBrains IDEs Continue Broader JetBrains support (IntelliJ, PyCharm, GoLand, etc.)
Want to try multiple LLMs Continue Switch between Ollama, OpenAI, Claude without redeploying
Minimal server management Continue No server required for local use
Compliance-heavy environment Tabby Single controlled endpoint for auditing and access management

Hosting Requirements

Tool CPU RAM Storage CWH Product
Continue (local) Depends on LLM 8-32 GB 10-50 GB Your workstation
Tabby (server) 4+ cores 16-32 GB 50+ GB SSD Cloud VPS
Tabby + GPU 4+ cores + GPU 32+ GB 100+ GB SSD GPU Server

Note: For Tabby, CPU-only inference works but is slow for code completion. If your team expects sub-second suggestions, consider a GPU server. For smaller models (StarCoder 3B, CodeQwen 7B), CPU can be acceptable.

Getting Started with Continue

If you already have Ollama running locally, setting up Continue takes about two minutes:

  1. Install the Continue extension from the VS Code marketplace or JetBrains plugin repository
  2. Open the Continue sidebar and click “Add model”
  3. Select “Ollama” and enter your model name (e.g., codellama:7b or deepseek-coder:6.7b)
  4. Start coding—inline completions and chat are now available

For more on setting up Ollama, see our complete production setup guide.

Getting Started with Tabby

Deploy Tabby on a VPS with Docker:

# Create a directory for persistent data
mkdir -p ~/tabby-data

# Run Tabby with the default code model
docker run -d \
  --name tabby \
  --gpus all \  # Remove if no GPU
  -v ~/tabby-data:/data \
  -p 8080:8080 \
  tabbyml/tabby

# Check logs
docker logs -f tabby

Once running, access the web UI at http://your-server:8080 to generate API tokens for your team. Install the Tabby extension in VS Code and configure the endpoint:

# In VS Code settings.json
{
  "tabby.endpoint": "http://your-server:8080",
  "tabby.token": "your-api-token"
}

Index your repositories for context-aware suggestions:

# Via API
curl -X POST http://your-server:8080/v1/index \
  -H "Authorization: Bearer your-token" \
  -d '{"git": "https://github.com/your-org/your-repo"}'

Our Recommendation

For most Canadian development teams, we recommend starting with Continue if you’re experimenting, and moving to Tabby once you’re ready for team-wide deployment.

Here’s why: Continue lets each developer experiment with minimal friction. Someone on your team probably already runs Ollama—they can try Continue today. But once you’ve validated that self-hosted code completion works for your workflow, Tabby’s centralized model makes more sense for teams. You get consistent configuration, shared code indexes, and a single audit point for compliance.

If you’re deploying Tabby for a team, consider Cloud VPS with 16-32 GB RAM for CPU inference, or a GPU Server if sub-second latency matters. All our servers run in Canadian data centres—your code never leaves the country.

Conclusion

Self-hosted AI coding assistants are no longer experimental. Both Continue and Tabby give you Copilot-like features without sending your code to third-party servers. The choice comes down to your deployment model: Continue for flexibility and experimentation, Tabby for team-wide consistency and code-aware context.

Either way, you’re keeping your intellectual property on your own infrastructure—and for many organizations, that’s worth the extra setup effort.

Next steps: