head to head · open source
llama.cpp vs mcp-server-elasticsearch
llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. mcp-server-elasticsearch has 717 stars, 153 forks, 23 open issues and last shipped today. llama.cpp leads on adoption by 17,838% (128,619 vs 717 stars). llama.cpp is written in C++ under MIT; mcp-server-elasticsearch is written in Rust under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, mcp-server-elasticsearch 21%. mcp-server-elasticsearch 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.cpp | mcp-server-elasticsearch | |
|---|---|---|
| GitHub stars | ★ 129K | ★ 717 |
| License | MIT | Apache-2.0 |
| Written in | C++ | Rust |
| Last push | 2026-09-18 | 2026-09-18 |
| Forks | ⑂ 23K | ⑂ 153 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick llama.cpp if
- You weight community size — 129K stars and counting
- You want the MIT license terms
- Your stack matches C++
- You value the larger contributor base for long-term maintenance
pick mcp-server-elasticsearch if
- You want the mcp-server-elasticsearch 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 mcp-server-elasticsearch fits your workflow better
About llama.cpp
llama.cpp is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.
read the full llama.cpp overview →
About mcp-server-elasticsearch
Elasticsearch MCP Server [!CAUTION] This MCP server is deprecated and will only receive critical security updates going forward. It has been superseded by the Elastic Agent Builder MCP endpoint, which is available in Elastic 9.2.0+ and Elasticsearch Serverless projects. Use the Elasticsearch MCP Server for AI Agents The Elasticsearch MCP Server connects your AI agents to Elasticsearch data using the Model Context Protocol (MCP). It enables natural language interactions with your Elasticsearch indices, allowing agents to query, analyze, and retrieve data without custom APIs. Follow these steps to deploy and config…
read the full mcp-server-elasticsearch overview →
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Frequently asked questions
Is llama.cpp or mcp-server-elasticsearch more popular?
llama.cpp has 128,619 GitHub stars and mcp-server-elasticsearch has 717. llama.cpp has the larger community by that measure.
Are llama.cpp and mcp-server-elasticsearch free?
Both are open source. llama.cpp is licensed under MIT and mcp-server-elasticsearch under Apache-2.0. Neither carries a licence fee.
What is the difference between llama.cpp and mcp-server-elasticsearch?
llama.cpp is written in C++ and mcp-server-elasticsearch 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.cpp or mcp-server-elasticsearch?
Choose llama.cpp if you want the larger community (128,619 stars) or its MIT licence terms. Choose mcp-server-elasticsearch if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.