Continue.dev vs Tabby (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.

Model Selection for Code Completion

Both Continue.dev and Tabby are model-agnostic — you bring the model. But the choice of model dramatically affects both the quality of completions and the hardware requirements for running them locally. Getting this right is the most impactful configuration decision you'll make.

Models by Use Case

Tab autocomplete (latency-sensitive): For inline ghost-text completions, latency matters more than raw quality. A 300ms suggestion feels instantaneous; a 1,500ms suggestion breaks typing flow. Use the smallest model that produces acceptable output:

• Qwen2.5-Coder 1.5B: The recommended default for autocomplete. Runs on CPU with 8GB RAM, 1GB VRAM is enough. Suggestion latency under 200ms on modern hardware. Surprisingly capable for single-line completions.
• Codestral Mamba 7B (Mistral): Better multi-line completions than Qwen 1.5B. Needs 6-8GB VRAM. Codestral is a code-specific model from Mistral trained on 80+ programming languages — strong on Python, TypeScript, Rust, and Go.
• DeepSeek-Coder V2 Lite 16B: High quality multi-line completions with strong fill-in-the-middle performance. Requires 12-16GB VRAM or significant CPU quantization overhead.

Chat and inline edit (quality-sensitive): For chat-based interactions (explaining code, refactoring, generating functions), latency tolerance is higher. Prioritize capability:

• Qwen2.5-Coder 7B-Instruct: Excellent balance of quality and speed for chat. 6GB VRAM. The recommended starting point for local chat.
• Qwen2.5-Coder 32B-Instruct: Near-frontier quality for a local model. 24GB VRAM needed, or use Q4 quantization to run on 16GB. Best local option for complex reasoning.
• DeepSeek-Coder V2 236B (via API or powerful server): The strongest open-weight code model available. Too large for typical local hardware; better served via a remote inference endpoint.

Hardware Requirements in Practice

No dedicated GPU (CPU-only): You can run Qwen2.5-Coder 1.5B or 3B for autocomplete. Chat quality will be limited, and latency will be higher. Workable for light use, frustrating as a primary workflow.

8GB VRAM (most consumer GPUs, Apple M2/M3 base): Run Qwen2.5-Coder 7B for both autocomplete and chat. This is the minimum setup that feels genuinely useful for daily development.

16GB VRAM (RTX 4080, Apple M2/M3 Pro, M4 base): Run Qwen2.5-Coder 14B or Q4-quantized 32B. Chat quality approaches cloud model performance for most code tasks.

24GB+ VRAM (RTX 3090/4090, Apple M2/M3 Max): Run Qwen2.5-Coder 32B full precision. This tier provides close to Claude 3.5 Sonnet quality for code tasks, entirely locally.

Cloud Models vs Local Models

Running local models keeps your code private and costs nothing in inference fees. But cloud models (Claude 3.5 Sonnet, GPT-4o, Gemini 1.5 Pro) are still ahead of local models for complex reasoning, large refactors, and multi-file changes.

The practical hybrid pattern for Continue.dev: use a fast local model (Qwen2.5-Coder 1.5B via Ollama) for autocomplete and a cloud model for chat. This gives you private, instant autocomplete while preserving access to frontier models for demanding tasks. Continue.dev's configuration supports this split natively.

For Tabby, the server centralizes this decision for your whole team. Run a local model on your Tabby server for completions (no per-request cost, full privacy), and optionally route chat requests to a cloud API for better quality.

For broader context on the AI coding assistant landscape, see best open source AI coding assistants, open source Cursor alternatives, and open source GitHub Copilot alternatives.

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.

See open source alternatives to Tabby on OSSAlt.