Continue vs vLLM
A side-by-side comparison of two self-hosted local llm runners options — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
Continue
Open-source AI code assistant for VS Code and JetBrains
VS
vLLM
High-throughput LLM serving engine with PagedAttention
| Feature | Continue | vLLM |
|---|---|---|
| Category | Local LLM Runners | Local LLM Runners |
| License | Apache-2.0 | Apache-2.0 |
| Language | TypeScript | Python |
| Setup difficulty | Easy | Hard |
| Min. RAM | 1,024 MB | 16,384 MB |
| Deployment | bare-metal, source | docker, kubernetes, bare-metal |
| GitHub stars | ★ 35,374 | ★ 88,482 |
| First released | 2023 | 2023 |
| Replaces | GitHub Copilot, Cursor | OpenAI API |
Why pick each one
Choose Continue if…
- Released under the Apache-2.0 license
- Easy to set up — beginner-friendly
- Mature project with 35.4k GitHub stars
- Written in TypeScript
Choose vLLM if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 88.5k GitHub stars
Frequently asked questions
Is Continue or vLLM better?
Neither is universally better. vLLM has the larger community, while Continue is simpler to set up (easy difficulty). Choose based on the comparison table above and your own setup.
Are Continue and vLLM free and open-source?
Yes. Continue is licensed under Apache-2.0 and vLLM under Apache-2.0. Both can be self-hosted at no software cost.
Can I run Continue and vLLM with Docker?
Continue: check the project docs for container support. vLLM: yes.