Vector
Vector is an on-device semantic search launcher for macOS that finds apps, files, messages, and clipboard items locally.
If you're on an Apple Silicon Mac and your frustration with Spotlight is that it only matches strings, Vector is worth the download: the routing model and local embedding model are what make semantic recall over files and messages real rather than a demo. The pay-what-you-want one-time price sidesteps subscription fatigue entirely. Just don't buy it expecting a Raycast replacement — there's no plugin ecosystem, no cloud sync, and no team tier.
Verified 1d ago · liveness 61/100 · cite: rightaichoice.com/tools/vector
- Apple Silicon Mac users on macOS 26.0+ who want a faster Spotlight replacement
- Privacy-conscious users who want semantic search with no cloud dependency
- Keyboard-driven users who search files, messages, and clipboard history daily
- Individuals who prefer a one-time pay-what-you-want price over a subscription
- Windows or Linux users (macOS only)
- Intel Mac users or anyone below macOS 26.0 (Apple Silicon required)
- People who depend on Raycast-style plugin ecosystems or third-party integrations
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Skip Vector if you need a plugin ecosystem, cloud sync, an API, or Windows/Intel support — it is a single-developer, Apple Silicon-only macOS 26+ launcher, not an extensible automation platform.
Semantic indexing can temporarily consume up to around 800MB of memory, which is noticeable if you are running it on an 8GB Mac.
Vector is pay-what-you-want with a one-time payment and no recurring fee, which puts it well below subscription launchers and AI search tools that bill monthly per seat. It fits individual users and small Mac-based teams who want semantic search without an ongoing line item. If you need tiered team billing, admin controls, or a support contract, this pricing model does not offer them at any price.
In short
Vector — Vector is an on-device semantic search launcher for macOS that finds apps, files, messages, and clipboard items locally. Best for Apple Silicon Mac users on macOS 26.0+ who want a faster Spotlight replacement, Privacy-conscious users who want semantic search with no cloud dependency, Keyboard-driven users who search files, messages, and clipboard history daily. Free to use.
What people actually say about Vector — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
112 mentions across 8 sources (Hacker News, YouTube, Product Hunt, App Store, Stack Overflow, GitHub, Lemmy, Tech Press) · researched Aug 5, 2026.
Average across the 8 sources that answered — each source counts once, not each post.
- +On-device ML ensures total privacy with no cloud dependency.
- +Intent-aware routing (address→maps, name→contacts) feels magical when it works.
- +Pay-what-you-want, one-time pricing—no subscription creep.
- +Liquid Glass UI looks native and adapts to user habits.
- +Semantic search finds files Spotlight can't, especially by content.
- −Initial indexing of large message histories is slow.
- −Routing model requires manual tuning; not plug-and-play.
- −Semantic results sometimes miss the exact file you wanted.
- −Occasional crashes during heavy indexing; stability concerns.
- −Documentation and community support are thin.
- • No hidden costs per se, but early adopters may need to donate for future updates.
- • Potential paid plugins or integrations later (speculative).
Viability Score
How well maintained and how widely used is Vector? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- On-device routing model that selects which sources a query should hit
- Local embedding model for semantic search across files and messages
- Instant app launching with keyboard-first intent ranking
- Natural-language semantic file search using local embeddings
- Message recall across past conversations by meaning, not keywords
- Clipboard history search from the same launcher panel
- Contacts lookup by typing a person's name
- Maps and place results by typing an address
- Ambient calendar and weather surfaces that adapt to time of day
- Offline operation with no cloud dependency for search or routing
- Liquid Glass interface using native macOS materials and motion
- Customizable panel placement and per-source visibility toggles
- Apple Neural Engine optimization for low-latency local inference
- Opt-out anonymous TelemetryDeck analytics that collect no personal content
- Customizable shortcuts and control over how much Vector knows about your system
About Vector
Vector is a macOS launcher built around local intelligence, pulling apps, files, messages, clipboard history, maps, contacts, calendar, and weather into one keyboard-first panel. What separates it from a plain Spotlight-style search box is that matching runs on two bundled models: a routing model that decides which sources a query should hit, and an embedding model that powers semantic recall over your files and messages. Type a few letters and apps launch; type a person's name and contacts appear; type an address and you get map results; ask something like "Which Swift file manages networking?" and it searches by meaning rather than token matching. Privacy is architectural here rather than a marketing claim. Embeddings, ranking, and query understanding run on your Mac using the Neural Engine where possible, so searches work offline and your content does not need to leave the machine. Optional anonymous TelemetryDeck analytics can be turned off, and no personal content is collected. The panel also adapts to context: calendar in the morning, weather before you head out, place results when you need directions. The app is roughly 120MB and bundles both models. It requires an Apple Silicon Mac on macOS 26.0 or later, plus local storage for indexing. Semantic indexing can temporarily use around 800MB while it runs, though idle memory is typically far lower. Vector is positioned as a superior Spotlight replacement rather than a Raycast clone. There is no plugin ecosystem, no cloud sync, and no subscription; the vendor sells it pay-what-you-want as a one-time purchase you keep forever. That makes it a fit for individuals who want fast native search over their own data, and a poor fit for teams needing centralized management or extensibility.
Behind the Verdict
Most launchers compete on how many integrations they can bolt on. Vector competes on a narrower question: what happens before you finish typing. The routing model is the interesting part — type a name and it surfaces contacts, type an address and it goes to maps, without you picking a source first. That intent-aware behavior is the thing we'd actually miss if we switched back to Spotlight. Pick Vector if you live in the keyboard and want semantic search over your own files and messages without sending anything to a server. The natural-language queries the vendor shows — "When was Joseph planning on visiting?" or "That note about the airport pickup" — are the right test. If your data is messy, unindexed, or scattered across cloud drives that never touch local disk, expectation should be lower. Where it bites: the hardware floor is real. Apple Silicon plus macOS 26.0 means anyone on an Intel Mac or an older OS is out. Indexing can spike to around 800MB temporarily, which you'll notice on an 8GB machine. And uninstalling is a two-step job — trash the app, then clear ~/Library/Application Support/Vector, which can hold a few hundred megabytes. The closest alternative is Raycast, and the comparison is not close in either direction. Raycast wins on extensions, team features, and breadth of integrations. Vector wins on native feel, offline semantic recall, and a one-time price instead of a subscription. Alfred sits in the same slot as Raycast. If you need a plugin platform or centralized management, none of Vector's advantages matter to you. We'd reach for this when the daily friction is searching your own machine rather than orchestrating external services. It is deliberately not a platform, and the vendor says so directly — it's built to replace Spotlight's frustration,
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Real-world workflow fit
Concrete scenarios for the personas Vector actually fits — and what changes day-one when you adopt it.
Mid-debug, you need the Swift file that handles networking but you only remember what it does, not what it is called. You open Vector and type 'the Swift file managing networking'.
Outcome: The embedding model matches on meaning rather than filename, surfaces the file, and you are back in your editor in seconds without leaving the keyboard or touching Finder.
You half-remember a message about an airport pickup from weeks ago but do not want to hand your message history to a cloud search product. You type the gist of it into Vector while offline on a flight.
Outcome: Local embeddings return the message from your own machine with no network request, so the search works in airplane mode and nothing leaves the laptop.
You start the morning by typing into one panel instead of opening Calendar, Weather, Contacts, and Spotlight separately, then launch your first app from the same box.
Outcome: Vector's ambient surfaces put your agenda and conditions in the panel, the routing model sends a name to Contacts and an address to Maps, and app launch stays the top-ranked result type.
Use Cases
- Launch any app by typing a few letters, with app results ranked above everything else.
- Find a file by describing what it does, e.g. 'the Swift file managing networking'.
- Recall a message or clipboard item from hours ago using a natural-language query.
- Type a person's name to get their contact details without opening Contacts.
- Type an address and see map and place results without switching to the Maps app.
- See your morning agenda and travel context without opening Calendar.
- Check weather before leaving the house from the same panel you launch apps from.
- Search personal files and messages by meaning while fully offline.
Models Under the Hood
as of 2026-09-23
Limitations
- Vector requires an Apple Silicon Mac running macOS 26.0 or later, so it is unavailable on Intel Macs and on older macOS releases.
- It has no API and no plugin system, and integration with third-party services is limited to what macOS itself provides — you cannot script it the way you can a plugin-based launcher.
- Semantic indexing can temporarily use up to around 800MB of memory, and the index data stored in ~/Library/Application Support/Vector can run to a few hundred megabytes, which you must delete manually on uninstall.
- The app is built by a single developer, and the vendor describes updates as landing whenever they are ready rather than on a fixed schedule, so the feature set evolves at that pace.
- There is no cloud sync and no team or enterprise management layer.
as of 2026-09-14
Verification history
We have re-verified Vector 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Vector tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Pay What You Want
$0+ one-time
Ideal for
Individual Apple Silicon Mac users who want full local semantic search without a recurring bill
What this tier adds
Starting tier — one-time pay-what-you-want purchase that unlocks all features, with no recurring fee and no higher paid plan
Where the pricing makes sense
The company stage and team size where Vector's pricing actually pencils out — and where peers do it cheaper.
Vector is pay-what-you-want with a one-time payment and no recurring fee, which puts it well below subscription launchers and AI search tools that bill monthly per seat. It fits individual users and small Mac-based teams who want semantic search without an ongoing line item. If you need tiered team billing, admin controls, or a support contract, this pricing model does not offer them at any price.
Setup time & first value
How long it actually takes to get something useful out of Vector — broken out by persona, not the marketing-page minute.
Install is a single ~120MB download and drag to Applications — most people are launching apps minutes after install. First meaningful value comes once semantic indexing completes in the background; indexing is the slow part and can temporarily use up to around 800MB of memory. Expect the launcher to feel useful immediately for apps and contacts, and the semantic file and message recall to sharpen
Switching to or from Vector
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Spotlight: Disable or ignore Spotlight, install Vector, and let it index locally — app launching and semantic file search then run from the Vector panel instead.
- →From Alfred: Rebuild your most-used app and file searches as plain typed queries in Vector, since there is no plugin system to port workflows into.
- →From Raycast: Keep Raycast if you rely on extensions, or replace only the app-launching and file-search habits with Vector and accept that third-party commands stay behind.
- ↗To Raycast: Rebuild your Vector habits as Raycast commands and extensions, accepting that local semantic recall over files and messages is not the same there.
- ↗To Spotlight: Fall back to the built-in launcher for app launching, but you lose intent-aware routing and semantic search over messages and files.
- ↗To Alfred: Move app and file launching over, and plan to replace semantic recall with keyword-based workflows.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Vector”, and we withheld 6: 6 could not be judged, because “Vector” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Vector.
Official links
Tools that pair well with Vector
Common stack mates teams adopt alongside Vector, with the specific reason each pairing earns its keep.
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Featured Head-to-Head Comparisons
Vector vs Guesty
Guesty and Vector serve entirely different needs: Guesty is a comprehensive property management platform for vacation rental businesses, while Vector is a macOS productivity launcher. Choose Guesty if you manage multiple rental listings and need AI-driven automation; choose Vector if you're a Mac power user seeking fast, private on-device search. They are not direct competitors.
Vector vs Gem
If you're a hiring team overwhelmed with repetitive recruiting tasks—sourcing, screening, scheduling—Gem's AI agents can automate most of the process and integrate with your existing ATS. Vector is an entirely different tool: a macOS-native, privacy-first semantic search replacement for Spotlight for individuals. Choose based on your domain: recruiting operations vs personal productivity on Mac.
Vector vs Poke Interaction Co
If you're a macOS power user craving a privacy-first, lightning-fast semantic search for local files, messages, and clipboard, Vector is your pick. But if you want an AI assistant that handles email, calendar, health tracking, and automations right inside Apple Messages or WhatsApp—with verified integration and proactive workflows—Poke wins. For most people doing daily productivity tasks across multiple services, Poke's broader integration set and proactive automations have more practical utility.
Mypeas Ai vs Vector
If you need to find files and messages faster on your Mac without sending data to the cloud, Vector is your pick—it's free and fully offline. If you need to document every AI interaction to justify your productivity at review time, MYPEAS.ai is the only tool that generates evidence-backed reports. They solve completely different problems; choose based on whether you want to search or to prove.
Alternatives to Vector
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Read.ai
Read.ai records your meetings across Zoom, Meet, and Teams, then lets you search everything it captured.
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