dtSearch Desktop vs Precision File Search
dtSearch Desktop
dtsearch.comPrecision File Search
pfs-ai.github.ioSide by side
| Figure | dtSearch Desktop | Precision File Search |
|---|---|---|
| Price from | $102.08/mo | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Plans published | 2 | 1 |
| Spec score · Desktop Search Software | 8.2 (#6) | 7.6 (#23) |
| Intended users | The company describes use across enterprise document and email search, legal research, e-discovery, forensics, government, finance, recruiting, and medical records. | The project README names researchers, students, developers, legal and business professionals, writers, and people organizing large local document collections as intended users. |
Plans
dtSearch Desktop
- dtSearch Desktop — single user license$249, paid once
- dtSearch Desktop — annual single individual user “investigative” license$1,225 a year
In their own words
dtSearch Desktop
dtSearch Desktop has no write-up on Specifiction yet; its figures are above.
Precision File Search
Precision File Search is a free desktop application for finding, classifying, and understanding local files on Windows, macOS, and Linux. It combines classic search with semantic search using a retrieval-augmented generation pipeline, and can answer natural-language questions with summarized responses. A trainable machine-learning model sorts scattered documents into categories users define. The maker says the application and AI models can operate locally, without cloud dependency, and that files, queries, and personal data are not sent to external servers. The project lists support for 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish. An internet connection is needed on first run to download multilingual AI models; later runs can be offline. PFS supports OpenAI-compatible inference URLs, including online providers and local engines such as Ollama and LM Studio. The README lists 8 GB of RAM for the core platform and a dedicated GPU, with at least 4 GB of VRAM for smaller models, for local LLM use. The project is licensed under MPL-2.0.