head to head · open source

llama_index vs pegainfer

llama_index has 52,202 GitHub stars, 8,162 forks, 770 open issues and last shipped yesterday. pegainfer has 704 stars, 107 forks, 89 open issues and last shipped yesterday. llama_index leads on adoption by 7,315% (52,202 vs 704 stars). llama_index is written in Python under MIT; pegainfer is written in Rust under Apache-2.0. llama_index has attracted 16% as many forks as stars, pegainfer 15%. pegainfer 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.

llama_index ★ 52K pegainfer ★ 704 category AI & Machine Learning

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Side by side

llama_index pegainfer
GitHub stars ★ 52K ★ 704
License MIT Apache-2.0
Written in Python Rust
Last push 2026-09-17 2026-09-17
Forks ⑂ 8.2K ⑂ 107
Self-hosting Yes Yes
Data ownership Your server Your server

pick llama_index if

  • You weight community size — 52K stars and counting
  • You want the MIT license terms
  • Your stack matches Python
  • You value the larger contributor base for long-term maintenance

full llama_index profile →

pick pegainfer if

  • You want the pegainfer feature set and don't need the biggest community
  • You prefer the Apache-2.0 license terms
  • Your stack matches Rust
  • You evaluated both and pegainfer fits your workflow better

full pegainfer profile →

About llama_index

LlamaIndex is an MIT licensed, open source Python framework for building agentic applications — retrieval augmented generation systems, agents and multi agent workflows — on top of private documents and data, and it is aimed at AI engineers and teams who need to connect large language models to their own sources of context.

read the full llama_index overview →

About pegainfer

PegaInfer is a pure Rust and CUDA large language model inference engine that serves models ranging from Qwen3 to Kimi K2 behind an OpenAI compatible API, with no PyTorch or Python runtime in the default serving path.

read the full pegainfer overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 246K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

ollama vs llama-index ollama vs llama-cpp ollama vs vllm ollama vs gpt4all ollama vs localai llama-cpp vs vllm ollama vs pageindex ollama vs langfuse openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat dify vs langchain dify vs ponytail langchain vs ponytail dify vs graphify langchain vs graphify ponytail vs graphify dify vs claude-mem dify vs ragflow

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama_index vs Ollama llama_index vs llama.cpp llama_index vs vllm llama_index vs GPT4All llama_index vs LocalAI llama_index vs PageIndex llama_index vs Langfuse llama_index vs cognee llama_index vs taipy llama_index vs dagster llama_index vs zvec llama_index vs langchain4j

Frequently asked questions

Is llama_index or pegainfer more popular?

llama_index has 52,202 GitHub stars and pegainfer has 704. llama_index has the larger community by that measure.

Are llama_index and pegainfer free?

Both are open source. llama_index is licensed under MIT and pegainfer under Apache-2.0. Neither carries a licence fee.

What is the difference between llama_index and pegainfer?

llama_index is written in Python and pegainfer in Rust. 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 pegainfer?

Choose llama_index if you want the larger community (52,202 stars) or its MIT licence terms. Choose pegainfer if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.