Continue.dev vs Tabby: Open Source AI Code Completion in 2026
The Case for Self-Hosted Code AI
GitHub Copilot costs $10/month per developer ($100/year). Copilot Business is $19/user/month. For a 10-person engineering team, that's $2,280/year — and every line of code you type with suggestions enabled is transmitted to GitHub/Microsoft.
For companies with proprietary code, regulated industries, or security requirements, sending code to Microsoft's infrastructure may not be acceptable regardless of their privacy policies.
Open source alternatives Continue.dev and Tabby both deliver AI code completion and chat assistance that runs on your own infrastructure. Your code never leaves your network.
TL;DR
Continue.dev (25K+ stars) is the better choice for individual developers and small teams. It's a VS Code/JetBrains extension that connects to any LLM — local Ollama, cloud APIs, or anything OpenAI-compatible. No server to manage.
Tabby (28K+ stars) is the better choice for engineering teams. A centralized server deployment with admin controls, team usage analytics, repository-level code context, and consistent AI assistance across your entire team.
Quick Comparison
| Feature | Continue.dev | Tabby |
|---|---|---|
| GitHub Stars | 25K+ | 28K+ |
| Deployment type | Extension only | Server + extension |
| Server required | No | Yes |
| Tab autocomplete | Yes | Yes |
| Chat in editor | Yes | Limited |
| Team management | No | Yes |
| Usage analytics | No | Yes |
| Repository context | File/directory | Full codebase index |
| IDE support | VS Code, JetBrains | VS Code, JetBrains, Vim |
| Model flexibility | Any (OpenAI-compatible) | Any (via config) |
| License | Apache 2.0 | Apache 2.0 |
Continue.dev — Best for Individual Developers
Continue.dev is an IDE extension, not a server. Install it in VS Code or JetBrains, point it at an LLM backend, and immediately get AI code assistance — no infrastructure to manage.
Core Features
Inline edit (Cmd+I): Highlight code, press a shortcut, describe the change. Continue.dev rewrites the selected code based on your description. This is the "cursor-style" edit flow that developers love — AI surgical edits without leaving the editor.
Chat sidebar (Cmd+L): Open a chat panel with access to your code context. Add files, symbols, or terminal output as context, then discuss your code or ask for implementations.
Tab autocomplete: Inline ghost text suggestions as you type. Configurable to use different (faster) models for autocomplete than for chat, optimizing for latency vs. quality.
Context providers: Reference specific things as context in your AI conversations:
@file— include a specific file@code— include a specific function or class@terminal— include recent terminal output@diff— include current git diff@problems— include editor problems/warnings@codebase— semantic search across your project
Configuration
Configure Continue.dev to use any LLM backend. Full local privacy with Ollama:
{
"models": [
{
"title": "Qwen2.5-Coder 7B (local)",
"provider": "ollama",
"model": "qwen2.5-coder:7b",
"contextLength": 8192
},
{
"title": "Claude 3.5 Sonnet",
"provider": "anthropic",
"model": "claude-3-5-sonnet-20241022",
"apiKey": "sk-ant-..."
}
],
"tabAutocompleteModel": {
"title": "Qwen2.5-Coder 1.5B (fast autocomplete)",
"provider": "ollama",
"model": "qwen2.5-coder:1.5b"
}
}
Use a fast small model for autocomplete (low latency) and a more capable model for chat (better reasoning). Mix and match cloud and local.
Self-Hosting Setup
Continue.dev has nothing to self-host — install the extension:
VS Code: ext install Continue.continue
JetBrains: Install "Continue" from the plugin marketplace
Pair with Ollama for fully local inference:
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen2.5-coder:7b
ollama pull qwen2.5-coder:1.5b # for autocomplete
Setup time: 5-10 minutes.
Recommended Local Models
| Use Case | Model | Size | Notes |
|---|---|---|---|
| Tab autocomplete | Qwen2.5-Coder 1.5B | 1GB | Very fast, CPU-capable |
| Chat (general) | Qwen2.5-Coder 7B | 4.5GB | Good quality, 6GB VRAM |
| Complex reasoning | DeepSeek-Coder V2 Lite | 9GB | 8GB VRAM recommended |
| Large codebase | Qwen2.5-Coder 32B | 20GB | 24GB VRAM or quantized |
Limitations
- No built-in team management or usage analytics
- Autocomplete quality depends heavily on model quality and latency
- No centralized codebase indexing (each user indexes their own local files)
- Configuration requires some technical knowledge
Best for: Individual developers and small teams who want AI code assistance without managing server infrastructure.
Tabby — Best for Engineering Teams
Tabby is a self-hosted AI coding assistant designed specifically for teams. A central server provides AI assistance, maintains codebase indexes, enforces policies, and gives admin visibility into AI usage.
Core Features
Centralized server: One Tabby server for your entire engineering team. All developers connect to it — consistent models, consistent quality, centralized management.
Repository indexing: Tabby indexes your entire codebase (or configured repositories). When suggesting completions, it retrieves semantically similar code from your actual codebase as context. This produces suggestions that match your codebase's patterns, naming conventions, and architecture — not just generic code patterns from training data.
# ~/.tabby/config.toml
[model.completion.http]
kind = "llama.cpp/completion"
model_id = "qwen2.5-coder-7b-instruct"
api_endpoint = "http://localhost:8080"
[[repositories]]
git_url = "https://github.com/your-org/your-repo"
Usage analytics: Admin dashboard showing completions accepted/rejected per developer, model usage, latency metrics. Understand how your team is using AI assistance.
Activity feed: See aggregated AI assistance activity across your team.
Authentication: Multiple auth options including GitHub OAuth, GitLab OAuth, and LDAP.
Multi-IDE support: VS Code, JetBrains IDEs, and Vim/Neovim via extensions.
Self-Hosting Setup
# Docker (recommended)
docker run \
-v /var/lib/tabby:/data \
-p 8080:8080 \
--gpus all \
tabbyml/tabby serve \
--model Qwen2.5-Coder-7B \
--chat-model Qwen2.5-Coder-7B-Instruct
# Or download the binary
curl -fsSL https://tabby.tabbyml.com/api/releases/latest.sh | sh
tabby serve --model Qwen2.5-Coder-7B
Hardware: 4GB VRAM minimum, 8GB recommended. Apple Silicon supported via Metal.
Repository Context in Practice
The repository indexing is Tabby's most distinctive feature. When you're writing code in a file that references patterns from elsewhere in your codebase, Tabby retrieves those patterns as completion context.
Example: You're writing a new API endpoint. Tabby retrieves similar endpoint implementations from your codebase, so its suggestions match your team's specific patterns for error handling, logging, and response formatting — not generic patterns.
This is significantly better than Continue.dev's file-level context for teams with large codebases.
Tabby Enterprise
Tabby has an enterprise tier (Tabby Cloud or self-hosted Enterprise) that adds:
- SSO (SAML, OIDC)
- Advanced analytics
- Priority support
- Team management
Pricing not publicly listed; contact for enterprise quotes. The open source self-hosted version covers most team needs.
Limitations
- More complex deployment than Continue.dev
- Requires ongoing server maintenance
- Chat capabilities are more limited vs. Continue.dev's sidebar chat
- Repository indexing requires accessible git repositories
Best for: Engineering teams of 5+ who want centralized AI coding assistance, codebase-aware suggestions, and management visibility.
Side-by-Side: Autocomplete Quality
Both tools can use the same underlying models. Autocomplete quality difference comes from context:
Continue.dev: Sends the current file and some surrounding context as the completion prefix. Quality depends on the model and local context.
Tabby: Sends current file context + retrieved similar code from your entire indexed codebase. The additional context improves suggestion relevance for established patterns.
In practice, for large codebases with consistent patterns (enterprise Java, internal frameworks, custom DSLs), Tabby's repository indexing produces noticeably better completions.
For small projects or individual use, the quality difference is minimal.
Cost Analysis: Copilot vs Self-Hosted
GitHub Copilot (10-Person Team)
| Plan | Monthly | Annual |
|---|---|---|
| Copilot Individual | $10/dev | $1,200 |
| Copilot Business | $19/dev | $2,280 |
| Copilot Enterprise | $39/dev | $4,680 |
Self-Hosted Alternative
| Setup | Monthly | Annual |
|---|---|---|
| Continue.dev + Ollama (local) | $0 | $0 |
| Continue.dev + Ollama (Hetzner server) | $10-15 | $120-180 |
| Tabby server (Hetzner GPU-capable) | $30-40 | $360-480 |
| Tabby + Claude API (hybrid) | $15-40 | $180-480 |
A 10-person team saves $1,800-4,200/year vs Copilot Business/Enterprise, depending on setup. The Tabby server investment for a 10-person team breaks even vs Copilot in 2-4 months.
Privacy Comparison
| Scenario | Code Privacy |
|---|---|
| GitHub Copilot | Code sent to GitHub/Azure. Enterprise gets data processing agreements. |
| Continue.dev + Ollama | Fully local. No code leaves your machine. |
| Continue.dev + Claude API | Code sent to Anthropic per conversation. |
| Tabby (local models) | Fully local. Code stays on your server. |
| Tabby + OpenAI | Completion requests sent to OpenAI. |
For maximum privacy: Continue.dev or Tabby with local Ollama models.
Decision Guide
Use Continue.dev if:
- You're an individual developer
- You want the simplest setup (just an extension)
- You need the best chat capabilities (full sidebar with rich context)
- You want flexibility to mix local and cloud models per task
Use Tabby if:
- You're managing AI assistance for a team of 5+
- You want codebase-aware suggestions (repository indexing)
- You need usage analytics and admin controls
- You have a company mandate for centralized AI governance
Find Your Code AI
Browse all GitHub Copilot alternatives on OSSAlt — compare Continue.dev, Tabby, Cody, Fauxpilot, and every other open source AI code completion tool with deployment guides and performance data.