head to head · open source
vllm vs Laminar
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. Laminar has 3,471 stars, 257 forks, 116 open issues and last shipped yesterday. vllm leads on adoption by 2,670% (96,158 vs 3,471 stars). vllm is written in Python under Apache-2.0; Laminar is written in TypeScript under Apache-2.0. vllm has attracted 25% as many forks as stars, Laminar 7%. vllm was the more recently maintained of the two, and both are self-hostable with no licence fee.
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 | Laminar | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 3.5K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | TypeScript |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 257 |
| 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 Laminar if
- You want the Laminar feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches TypeScript
- You evaluated both and Laminar 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 Laminar
Laminar Laminar is an open source observability platform purpose built for AI agents. [x] Tracing. Docs [x] OpenTelemetry native powerful tracing SDK 1 line of code to automatically trace Vercel AI SDK, Browser Use, Stagehand, LangChain, OpenAI, Anthropic, Gemini, and more . [x] Signals. Docs [x] Describe any behavior of your agent that you want to track in plain English (e.g. "agent is stuck in a loop") [x] Laminar reads every agent run and pings you in Slack when it happens. [x] Evals. Docs [x] Unopinionated, extensible SDK and CLI for running evals locally or in CI/CD pipeline. [x] UI for visualizing evals…
read the full Laminar overview →
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Frequently asked questions
Is vllm or Laminar more popular?
vllm has 96,158 GitHub stars and Laminar has 3,471. vllm has the larger community by that measure.
Are vllm and Laminar free?
Both are open source. vllm is licensed under Apache-2.0 and Laminar under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and Laminar?
vllm is written in Python and Laminar 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 Laminar?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose Laminar if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.