head to head · open source
vllm vs Spanlens
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. Spanlens has 13 stars, 1 forks, 1 open issues and last shipped yesterday. vllm leads on adoption by 739,577% (96,158 vs 13 stars). vllm is written in Python under Apache-2.0; Spanlens is written in TypeScript under MIT. vllm has attracted 25% as many forks as stars, Spanlens 8%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (llm), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| vllm | Spanlens | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 13 |
| License | Apache-2.0 | MIT |
| Written in | Python | TypeScript |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 1 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 96K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick Spanlens if
- You want the Spanlens feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches TypeScript
- You evaluated both and Spanlens fits your workflow better
About vllm
Easy, fast, and cheap LLM serving for everyone 🔥 We have built a vLLM website to help you get started with vLLM. Please visit vllm.ai to learn more. For events, please visit vllm.ai/events to join us. About vLLM is a fast and easy to use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has grown into one of the most active open source AI projects built and maintained by a diverse community of many dozens of academic institutions and companies from over 2000 contributors. vLLM is fast with: State of the art serving throughput Efficient management of…
About Spanlens
Spanlens Open source LLM observability you can turn on in one line. Point your OpenAI, Anthropic, or Gemini client at Spanlens and every call is logged with cost, tokens, latency, and full agent traces. No SDK rewrite, no platform migration. Eleven providers supported, plus native Vercel AI SDK, LangChain, and LlamaIndex integrations, and you can query it all from Cursor or Claude Desktop through the bundled MCP server. Self hostable in one Docker command. MIT. Why it exists. I shipped an LLM app on OpenAI and Gemini and hit a wall. The provider dashboards showed total spend and nothing else. I could not tell…
read the full Spanlens overview →
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Frequently asked questions
Is vllm or Spanlens more popular?
vllm has 96,158 GitHub stars and Spanlens has 13. vllm has the larger community by that measure.
Are vllm and Spanlens free?
Both are open source. vllm is licensed under Apache-2.0 and Spanlens under MIT. Neither carries a licence fee.
What is the difference between vllm and Spanlens?
vllm is written in Python and Spanlens in TypeScript. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, vllm or Spanlens?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose Spanlens if its feature set, stack or MIT licence fits better. Both are self-hostable.