head to head · open source
vllm vs great_expectations
vllm has 96,158 GitHub stars, 24,317 forks, 7,953 open issues and last shipped yesterday. great_expectations has 11,914 stars, 1,883 forks, 42 open issues and last shipped yesterday. vllm leads on adoption by 707% (96,158 vs 11,914 stars). vllm is written in Python under Apache-2.0; great_expectations is written in Python under Apache-2.0. vllm has attracted 25% as many forks as stars, great_expectations 16%. 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 | great_expectations | |
|---|---|---|
| GitHub stars | ★ 96K | ★ 12K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 24K | ⑂ 1.9K |
| 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 great_expectations if
- You want the great_expectations feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Python
- You evaluated both and great_expectations 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 great_expectations
About GX Core GX Core combines the collective wisdom of thousands of community members with a proven track record in data quality deployments worldwide, wrapped into a super simple package for data teams. Its powerful technical tools start with Expectations: expressive and extensible unit tests for your data. Expectations foster collaboration by giving teams a common language to express data quality tests in an intuitive way. You can automatically generate documentation for each set of validation results, making it easy for everyone to stay on the same page. This not only simplifies your data quality processes…
read the full great_expectations overview →
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Frequently asked questions
Is vllm or great_expectations more popular?
vllm has 96,158 GitHub stars and great_expectations has 11,914. vllm has the larger community by that measure.
Are vllm and great_expectations free?
Both are open source. vllm is licensed under Apache-2.0 and great_expectations under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and great_expectations?
vllm is written in Python and great_expectations in Python. 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 great_expectations?
Choose vllm if you want the larger community (96,158 stars) or its Apache-2.0 licence terms. Choose great_expectations if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.