head to head · open source
vllm vs great_expectations
vllm has 92,028 GitHub stars, 22,335 forks, 7,953 open issues and last shipped yesterday. great_expectations has 11,797 stars, 1,850 forks, 42 open issues and last shipped yesterday. vllm leads on adoption by 680% (92,028 vs 11,797 stars). vllm is written in Python under Apache-2.0; great_expectations is written in Python under Apache-2.0. vllm has attracted 24% 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 | ★ 92K | ★ 12K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-09-17 | 2026-09-17 |
| Forks | ⑂ 22K | ⑂ 1.9K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick vllm if
- You weight community size — 92K 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
vLLM is a high throughput, memory efficient Python library for LLM inference and serving, built for ML engineers and platform teams who need to run open weight models on their own hardware at production scale.
About great_expectations
Great Expectations, referred to in its own documentation as GX Core, is an open source Python library for validating and documenting the quality of data. It lives in the Python data ecosystem and is published under the Apache 2.0 license. The project has been in development for roughly nine years and carries about 11,794 stars with 1,848 forks, which places it among the longer running tools in the data quality space. Its central concept is the Expectation: an expressive, extensible unit test written against a dataset rather than against application code.
read the full great_expectations overview →
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Frequently asked questions
Is vllm or great_expectations more popular?
vllm has 92,028 GitHub stars and great_expectations has 11,797. 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 (92,028 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.