Digger Solo
Summary
You remember the document — the invoice from that Berlin supplier, the research paper about mitochondrial drift — but the filename is some timestamp and the folder is buried three levels deep. Spotter is a local-first file search tool built for exactly that moment.
The vendor describes Spotter as a semantic search layer that sits on top of your local file collection, letting you query by concept rather than keyword. It handles PDFs, images, documents, and music files, and the docs describe a relationship visualization feature that maps how files connect semantically. Because processing stays on your machine, nothing is uploaded to a cloud service. The free tier caps at 500 files with no index updates, which means any new files you add after the initial scan fall outside the search until you upgrade. Teams managing thousands of research papers or archive folders hit that ceiling fast.
Bottom line: Pick this if you have a dense, static PDF library and want concept-level search without touching a cloud service — but plan for a different solution when your collection grows past a few hundred files and changes week to week.
Pricing Plans
Subscription- Price
- €49 one-time or €4.90/month
- Free Tier
- Limited to 500 files, no updates included
Free
Try before you buy, limited to 500 files, no updates included
- Up to 500 files
- Semantic search
- No account required
Ownership
One-time purchase. Unlimited files, works completely offline, own it forever with 12 months of updates.
- Unlimited files
- Offline operation
- Lifetime access
- 12 months of updates included
Personal Monthly
Monthly subscription with unlimited files, cancel anytime, monthly updates
- Unlimited files
- Cancel anytime
- Monthly updates
- Support development
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Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- All processing runs locally with no cloud upload, so sensitive documents — client contracts, medical records, draft research — never leave the machine.
- Concept-based search across PDFs with cited passage retrieval, so you locate the right paragraph without remembering the filename or reading the whole document.
- Duplicate detection across scattered folders, so years of disorganized downloads and backup copies stop inflating search results and eating disk space.
- Semantic music queuing from an unorganized library, so you get a coherent listening experience without manually curating playlists or relying on a streaming service's taste graph.
- Self-hosted via a Linux .deb package, so teams with air-gapped environments or strict data residency requirements can deploy it without a cloud dependency.
Cons
Sign in to edit- The free tier caps at 500 files and does not update the index after the initial scan — any file added after setup is invisible to search until you upgrade, which makes it unusable as a living workspace for anyone adding documents regularly.
- There is no API, so Spotter cannot be wired into a broader workflow: no programmatic queries, no integration with a note-taking app, no automated tagging pipeline. Teams that need file intelligence inside an existing tool stack switch to an alternative with an API surface.
- Linux is the confirmed self-hosted platform via the .deb package; the scraped content does not confirm native packages for other operating systems, so Windows or macOS users relying on local processing may face a gap the vendor has not publicly addressed.
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About
- Platforms
- macOS, Windows, Linux
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- 2026-06-05T18:25:17.404Z
Best For
Who it's for
- Users with large, disorganized local file collections
- Privacy-conscious professionals who want AI search without cloud uploads
- Knowledge workers managing large numbers of PDFs and research documents
- Teams or individuals handling diverse file types (images, documents, video)
What it does well
- Find documents by concept without remembering exact filenames (e.g., 'invoice from Berlin supplier')
- Discover and remove duplicate images and documents across scattered folders
- Ask questions about PDF content and retrieve specific passages with citations
- Visualize and explore how files in a collection relate to each other semantically
- Automatically queue similar music from a large, unorganized music library
Integrations
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Frequently Asked Questions
- Is Digger Solo free?
- Digger Solo has a permanent free tier alongside paid upgrades (paid plans from €49 one-time or €4.90/month). You can keep using a baseline version indefinitely without paying.
- Is Digger Solo open source?
- No — Digger Solo is a closed-source tool. Source code is not publicly available.
- Can I self-host Digger Solo?
- Yes. Digger Solo supports self-hosting on your own infrastructure.
- What platforms does Digger Solo support?
- Digger Solo is available on: macOS, Windows, Linux.
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Most file search tools find what you can name. Spotter is built for what you can describe. The vendor describes a workflow where you point the tool at a local folder, it builds a semantic index of the contents, and you query against concepts — ‘invoice from Berlin supplier’ or ‘study about sleep and memory’ — rather than exact filenames or keyword fragments. It covers PDFs, images, documents, and music files, and the vendor states duplicate detection runs across scattered folders to surface redundant files.
The defining feature is local processing. No file leaves your machine. For privacy-conscious professionals — lawyers, researchers, journalists, anyone handling sensitive documents — this removes the core objection to AI-powered search. The Linux .deb package the vendor offers confirms it runs on-device, not as a cloud relay. That architecture trades convenience for control, and for a specific user profile, that trade is exactly right.
Where it fits: a researcher with a static archive of PDFs who needs to surface cited passages without manually scanning every file. Where it breaks: any team whose collection is actively growing, because the free tier does not update the index after the initial scan, and the 500-file cap is a hard wall. A music librarian with 10,000 tracks or a legal team processing new documents weekly will need a paid tier or a different tool entirely. The relationship visualization feature — the vendor describes it as mapping semantic connections across a collection — is interesting for exploration but the scraped content does not detail how it performs on large or heterogeneous collections.