Continue vs Tabby.
Compare capability, performance, ease of use, value, pricing and practical fit in one view.
The comparison in one view.
Start with Lorezi's current pick, then inspect the score, pricing and fit behind it before deciding.
Continue
An open-source, highly configurable AI coding assistant that integrates directly into your IDE, allowing you to bring your own LLMs and maintain full control over your development environment.
Tabby
An open-source, self-hosted AI coding assistant that provides real-time code completion and chat capabilities while ensuring data privacy.
Where each tool wins.
| Dimension | Continue | Tabby |
|---|---|---|
| Overall | 4.22/5 | 4.55/5 |
| Features | 4.5/5 | 5.0/5 |
| Performance | 4.0/5 | 4.5/5 |
| Ease of use | 3.5/5 | 4.1/5 |
| Value | 5.0/5 | 4.5/5 |
| Starting price | Free | Free |
See what sits behind the overall score.
The same comparison dimensions are shown consistently across the page. Treat small decimal differences as directional context, then check pricing, fit and limitations before deciding.
Choose by the job, not the logo.
Best-fit guidance is paired with the practical workflows already attached to each Lorezi software record.
Software developers, Open source contributors, Enterprise engineering teams, AI researchers, Full-stack developers
- Connect tools and data across workflows
- Apply Custom LLM configuration in a real workflow
- Write, review, debug or improve software
- Apply Natural language refactoring in a real workflow
- Apply Slash command support in a real workflow
Developers, Enterprise Teams, Security-conscious Organizations, Open Source Contributors
- Write, review, debug or improve software
- Apply Self-hosted deployment options in a real workflow
- Connect tools and data across workflows
- Apply Local model inference in a real workflow
- Apply Role-based access control in a real workflow
What each product brings to the workflow.
Feature inventories and platform coverage come directly from the connected software profiles.
- IDE integration
- Custom LLM configuration
- Context-aware code completion
- Natural language refactoring
- Multi-file codebase indexing
- Chat with codebase
- Slash command support
- Real-time code completion
- Self-hosted deployment options
- Natural language to code generation
- Context-aware code suggestions
- Support for multiple programming languages
- Integration with major IDEs
- Local model inference
Strengths and limitations, side by side.
A useful comparison should expose the reasons to choose a tool and the reasons to hesitate in the same view.
Strengths
- Open source and transparent
- Supports local LLM integration
- Highly customizable configuration
- Works with VS Code and JetBrains
Limitations
- Requires manual setup for local models
- Steeper learning curve than proprietary tools
Strengths
- Complete data privacy through self-hosting
- No telemetry or data collection
- High performance with local model inference
- Seamless integration with popular IDEs
- Transparent open-source codebase
Limitations
- Requires technical expertise for self-hosting
- Hardware requirements for local GPU inference
- Smaller community compared to proprietary alternatives
What it takes to adopt each tool.
Pricing status, free-plan availability and developer ownership are surfaced without hiding unknown vendor data.
Continue joined Cursor. The commercial Continue organization has been acquired, while the open-source Continue project remains freely available for self-hosted use.
Community is free/open source for up to 5 users. Team is $19/user/month for up to 50 users; Enterprise is custom. Tabby Cloud/Pochi also uses usage-based model billing with $20 in free monthly credits.
Final Lorezi decision
Tabby takes this comparison.
Compare nearby alternatives without rebuilding your shortlist.
Open related head-to-head comparisons for the same software market.