head to head · open source
vllm vs txtai
vllm has 92,028 GitHub stars, 22,335 forks, 7,953 open issues and last shipped yesterday. txtai has 12,956 stars, 891 forks, 9 open issues and last shipped 3 days ago. vllm leads on adoption by 610% (92,028 vs 12,956 stars). vllm is written in Python under Apache-2.0; txtai is written in Python under Apache-2.0. vllm has attracted 24% as many forks as stars, txtai 7%. 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 | txtai | |
|---|---|---|
| GitHub stars | ★ 92K | ★ 13K |
| License | Apache-2.0 | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-09-17 | 2026-09-15 |
| Forks | ⑂ 22K | ⑂ 891 |
| 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 txtai if
- You want the txtai 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 txtai 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 txtai
txtai is an all in one AI framework for semantic search, LLM orchestration and language model workflows, written in Python and released under the Apache 2.0 license. It lives in the Python machine learning ecosystem and is built on Hugging Face Transformers, Sentence Transformers and FastAPI. The core component is an embeddings database, which is a union of vector indexes (both sparse and dense), graph networks and relational databases. That foundation enables vector search and also serves as a knowledge source for large language model applications.
read the full txtai overview →
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Frequently asked questions
Is vllm or txtai more popular?
vllm has 92,028 GitHub stars and txtai has 12,956. vllm has the larger community by that measure.
Are vllm and txtai free?
Both are open source. vllm is licensed under Apache-2.0 and txtai under Apache-2.0. Neither carries a licence fee.
What is the difference between vllm and txtai?
vllm is written in Python and txtai 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 txtai?
Choose vllm if you want the larger community (92,028 stars) or its Apache-2.0 licence terms. Choose txtai if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.