Continue vs Tabby
| Feature | Continue | Tabby |
|---|---|---|
| Category | LLMs & AI Infra | LLMs & AI Infra |
| Sub-category | AI Coding | AI Coding |
| Maturity | stable | stable |
| Complexity | beginner | intermediate |
| Performance tier | lightweight | medium |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 22.0K | 25.0K |
| Contributors | 300 | 100 |
| Commit frequency | daily | weekly |
| Plugin ecosystem | none | none |
| Docs quality | good | good |
| Backing org | Continue Dev | TabbyML |
| Funding model | vc_backed | vc_backed |
| Min RAM | 256 MB | 4 GB |
| Min CPU cores | 1 | 2 |
| Scaling pattern | single_node | single_node |
| Self-hostable | Yes | Yes |
| K8s native | No | No |
| Offline capable | Yes | Yes |
| Vendor lock-in | none | none |
| Languages | TypeScript | Rust |
| API type | SDK | REST |
| Protocols | HTTP | HTTP |
| Deployment | npm | docker, binary |
| SDK languages | — | — |
| Team size fit | solo, small, medium, enterprise | solo, small, medium |
| First release | 2023 | 2023 |
| Latest version | — | — |
When to use Continue
- ✓ Private AI coding assistant with local models
- ✓ Custom context providers for company codebase
- ✓ Multi-model code assistance in VS Code/JetBrains
When to use Tabby
- ✓ Private self-hosted code completion for enterprises
- ✓ GPU-accelerated code suggestions
- ✓ Air-gapped development environments
Continue anti-patterns
- ✕ Quality depends on chosen LLM
- ✕ Needs separate LLM server
- ✕ Less polished than GitHub Copilot UX
Tabby anti-patterns
- ✕ Needs GPU for good performance
- ✕ Smaller model ecosystem than Continue
- ✕ Setup more complex than cloud alternatives