head to head · open source
llama_index vs ServerlessLLM
llama_index has 52,779 GitHub stars, 8,500 forks, 770 open issues and last shipped 8 days ago. ServerlessLLM has 718 stars, 80 forks, 45 open issues and last shipped 2 months ago. llama_index leads on adoption by 7,251% (52,779 vs 718 stars). llama_index is written in Python under MIT; ServerlessLLM is written in Python under Apache-2.0. llama_index has attracted 16% as many forks as stars, ServerlessLLM 11%. llama_index 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
| llama_index | ServerlessLLM | |
|---|---|---|
| GitHub stars | ★ 53K | ★ 718 |
| License | MIT | Apache-2.0 |
| Written in | Python | Python |
| Last push | 2026-10-26 | 2026-09-04 |
| Forks | ⑂ 8.5K | ⑂ 80 |
| 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 ServerlessLLM if
- You want the ServerlessLLM 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 ServerlessLLM 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 ServerlessLLM
ServerlessLLM Load models 10x faster. Serve 10 models with 1 GPU. Docs • Quick Start • OSDI'24 Paper ⚡ Performance ServerlessLLM loads models 6 10x faster than SafeTensors , enabling true serverless deployment where multiple models efficiently share GPU resources. Model Scenario SafeTensors ServerlessLLM Speedup Qwen/Qwen3 32B Random 20.6s 3.2s 6.40x Cached 12.5s 1.3s 9.95x DeepSeek R1 Distill Qwen 32B Random 19.1s 3.2s 5.93x Cached 10.2s 1.2s 8.58x Llama 3.1 8B Instruct Random 4.4s 0.7s 6.54x Results obtained on NVIDIA H100 GPUs with NVMe SSD. "Random" simulates serverless multi model serving; "Cached" shows…
read the full ServerlessLLM overview →
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Frequently asked questions
Is llama_index or ServerlessLLM more popular?
llama_index has 52,779 GitHub stars and ServerlessLLM has 718. llama_index has the larger community by that measure.
Are llama_index and ServerlessLLM free?
Both are open source. llama_index is licensed under MIT and ServerlessLLM under Apache-2.0. Neither carries a licence fee.
What is the difference between llama_index and ServerlessLLM?
llama_index is written in Python and ServerlessLLM 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 ServerlessLLM?
Choose llama_index if you want the larger community (52,779 stars) or its MIT licence terms. Choose ServerlessLLM if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.