head to head · open source
Ollama vs dingo
Ollama has 181,184 GitHub stars, 17,921 forks, 3,957 open issues and last shipped yesterday. dingo has 1,704 stars, 265 forks, 8 open issues and last shipped 2 months ago. Ollama leads on adoption by 10,533% (181,184 vs 1,704 stars). Ollama is written in Go under MIT; dingo is written in Java under Apache-2.0. Ollama has attracted 10% as many forks as stars, dingo 16%. Ollama 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.
← all 8884 open source comparisons
Side by side
| Ollama | dingo | |
|---|---|---|
| GitHub stars | ★ 181K | ★ 1.7K |
| License | MIT | Apache-2.0 |
| Written in | Go | Java |
| Last push | 2026-09-17 | 2026-07-10 |
| Forks | ⑂ 18K | ⑂ 265 |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Ollama if
- You weight community size — 181K stars and counting
- You want the MIT license terms
- Your stack matches Go
- You value the larger contributor base for long-term maintenance
pick dingo if
- You want the dingo feature set and don't need the biggest community
- You prefer the Apache-2.0 license terms
- Your stack matches Java
- You evaluated both and dingo fits your workflow better
About Ollama
Ollama is a Go based, MIT licensed runtime that downloads and runs open source large language models such as DeepSeek, Qwen, Gemma, GLM, MiniMax and gpt oss locally on a user's own machine, and it is aimed at developers and teams that want model inference without routing prompts through a hosted API.
read the full Ollama overview →
About dingo
DingoDB DingoDB is an open source distributed multi modal vector database independently designed and developed by DataCanvas, which integrates real time strong consistency, relational semantics, and vector semantics into a unified platform, DingoDB positioning itself as a distinctive multi modal database solution. With exceptional horizontal scalability and elastic scaling capabilities, it effortlessly meets enterprise grade high availability requirements. Furthermore, DingoDB offers extensive multi language interfaces and seamless compatibility with the MySQL protocol, delivering unparalleled flexibility and con…
read the full dingo overview →
More in AI & Machine Learning
Related comparisons
More Machine Learning Infrastructure projects
Compare either of these against the rest of the Machine Learning Infrastructure field.
Frequently asked questions
Is Ollama or dingo more popular?
Ollama has 181,184 GitHub stars and dingo has 1,704. Ollama has the larger community by that measure.
Are Ollama and dingo free?
Both are open source. Ollama is licensed under MIT and dingo under Apache-2.0. Neither carries a licence fee.
What is the difference between Ollama and dingo?
Ollama is written in Go and dingo in Java. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Ollama or dingo?
Choose Ollama if you want the larger community (181,184 stars) or its MIT licence terms. Choose dingo if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.