head to head · open source
llama_index vs dstack
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. dstack has 2,299 stars, 272 forks, 68 open issues and last shipped yesterday. llama_index leads on adoption by 2,196% (52,779 vs 2,299 stars). llama_index is written in Python under MIT; dstack is written in Python under MPL-2.0. llama_index has attracted 16% as many forks as stars, dstack 12%. dstack was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (fine-tuning), 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
| llama_index | dstack | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 2.3K |
| License | MIT | MPL-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-11-02 |
| Forks | ⑂ 8.5K | ⑂ 272 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama_index if
- You weight community size — 53K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick dstack if
- You want the dstack feature set and don't need the biggest community
- You prefer the MPL-2.0 license terms
- Your stack matches Python
- You evaluated both and dstack fits your workflow better
About llama_index
🗂️ LlamaIndex (OSS Framework) 🦙 [!NOTE] The current focus of LlamaIndex is to build the best AI powered engine for document parsing and extraction. LlamaParse is our enterprise platform for agentic OCR, parsing, extraction, indexing and more. LiteParse represents our efforts to build the best free, fast, cheap text parser in the market. ParseBench and ExtractBench represent our commitment towards open benchmarking for parsing and extraction. The company itself has undergone an evolution since when this OSS framework first launched 3 years ago in 2023. Since the early days, the framework has consisted of a…
read the full llama_index overview →
About dstack
dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on prem clusters. It streamlines development, training, and inference, and is compatible with any hardware, open source tools, and frameworks. Accelerators dstack supports NVIDIA , AMD , Google TPU , and Tenstorrent accelerators out of the box. Latest news ✨ [2026/08] dstack 0.21.0: Pydantic v2, Gateway replicas [2026/07] dstack 0.20.29: Presets — agent driven inference optimization (experimental) [2026/07] dstack 0.20.27: Slurm backend [2026/05] dstack 0.20.21: Kubernetes multiple clusters…
read the full dstack overview →
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Frequently asked questions
Is llama_index or dstack more popular?
llama_index has 52,779 GitHub stars and dstack has 2,299. llama_index has the larger community by that measure.
Are llama_index and dstack free?
Both are open source. llama_index is licensed under MIT and dstack under MPL-2.0. Neither carries a licence fee.
What is the difference between llama_index and dstack?
llama_index is written in Python and dstack 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, llama_index or dstack?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose dstack if its feature set, stack or MPL-2.0 licence fits better. Both are self-hostable.