Némos vs Tana in 2026 — Capture Speed vs Supertag Power

Tana arrived in 2022 as an outliner with a radical idea: instead of folders and tags, structure your notes with supertags — schema-rich templates that turn any node into a structured object with typed fields. Want every meeting to have an attendee field that auto-links to people in your graph? Add #meeting supertag. Want every book to track a rating and review status? Add #book. The result is a PKM tool that operates somewhere between Notion's databases, Roam's outliner, and a personal Airtable. The Tana community on YouTube and Twitter is unusually intense — users film 90-minute videos walking through their supertag schemas the way photographers walk through camera setups.

Tana is genuinely powerful. It's also dense — the learning curve is real, and most users take weeks to set up a workflow that justifies the $14/mo.

Némos is the opposite end of the spectrum. We're optimized for the seconds between "I want to remember this" and "it's saved." No schema design, no supertag setup, no template configuration. Just capture, OCR, transcribe, and let on-device AI find it later. Here's how they actually compare.

What Tana does that nobody else does

Supertags are real, and they matter. A #meeting supertag in Tana isn't just a label — it's a schema that says "every meeting has these fields: attendees (typed: people), date (typed: date), action items (typed: list of tasks)." Apply the supertag and the node morphs into a structured object that participates in queries across the entire graph.

The closest analogy is Notion's databases, but Tana's are first-class in the outliner — you can scatter them across any depth of nesting and they aggregate via live queries. It's the most expressive PKM primitive shipped this decade.

If your job is modeling knowledge — a researcher tracking experiments, a founder tracking prospects, a writer tracking characters — Tana is a serious tool worth the learning curve.

What Tana doesn't do

Tana isn't built for friction-free capture. Opening the iOS app, navigating to the right inbox, choosing a supertag, and typing takes 5-10 seconds — fine for planned writing, terrible for in-the-moment thoughts that need to land in under a second before they evaporate.

Tana also doesn't natively do screenshots, voice memos with transcription, article parsing, or Apple Watch capture. You can attach images and audio files, but they're opaque — no OCR on screenshots, no transcript on voice memos, no metadata extraction on articles.

For everyday capture across the day, Tana is the wrong shape.

What Némos does instead

Némos is built for the capture half of PKM. The Action Button on iPhone or Apple Watch starts a recording in 0.5 seconds. Screenshots OCR automatically as you take them. Article URLs save with parsed text. Photos run through Vision for object recognition.

On-device AI then handles organization — auto-tagging, semantic categorization, duplicate detection — without requiring you to design a schema upfront. The trade-off is expressive power: Némos can't model "meetings have an attendees field" the way Tana can.

How they fit together (or don't)

A reasonable workflow for power users: capture in Némos, promote to Tana. Voice memos, screenshots, and quick ideas land in Némos via the Action Button or Watch complication. Once a week, the items that deserve structured modeling get re-entered into Tana with proper supertags. The rest stay in Némos as a searchable archive.

This works because Tana's strength is at the *organize* layer (structured models, queries, dashboards) and Némos's strength is at the *capture* layer (speed, breadth, privacy).

For most users, though, that two-tool workflow is overkill. Pick one.

The honest cost-benefit

Tana is $14-20/mo and demands weeks of setup time. The payoff is real if your knowledge work justifies it. If you're a solo researcher tracking 50+ papers, a writer tracking a novel's character graph, or a founder running a CRM-in-Tana — yes, it's worth it.

If you're a "I just want to remember the stuff I see and hear during the day" user, Tana is overengineered. Némos at $4.99/mo (or free) delivers more of what you actually need.

Privacy posture

Tana is cloud-only. Every node, every supertag, every query crosses Tana's servers. Tana AI uses OpenAI's API. The privacy posture is comparable to Mem and Notion — strong policies, real cloud surface.

Némos has zero cloud surface beyond Apple's E2E-encrypted CloudKit. For users where privacy is a constraint (healthcare, legal, journalism with confidential sources), Némos is the only credible option of the two.

Real-world workflow comparison

A 38-year-old engineering manager at a public tech company runs Tana as her primary PKM. Every meeting becomes a node with the #meeting supertag, which auto-creates fields for attendees, project, date, and outcome. Every direct report has a #person node with fields for current goals, recent 1:1 highlights, and growth areas. Quarterly reviews query the graph: "show me all 1:1 highlights for Priya tagged growth-area in the last 90 days" and Tana returns a structured table. This is the workflow that converts Tana skeptics: the schema lets you reason about your knowledge as data, not prose.

The same engineering manager has a parallel capture life that Tana ignores. She screenshots a chart from a competitor's earnings call on her iPhone, takes a voice memo about a hiring intuition while walking between buildings, saves an HBR article about distributed engineering teams at midnight, and jots a quick Apple Watch reminder during a workout. Tana's mobile app is fine for typed notes but slow for screenshots (no OCR), useless for voice memos (no transcription), and unavailable on Apple Watch. Her screenshot library has 6,000+ images, none searchable.

Némos captures all four into one indexed library with on-device OCR, real-time voice transcription, parsed article text, and watch capture. When she searches "competitor earnings distributed teams" three weeks later, all four artifacts surface. Tana returns only structured nodes because that's what Tana indexes.

The two products solve different jobs. Tana is a schema-first knowledge workspace for structured thinking. Némos is an ambient capture system for finding things later. Most heavy Tana users we talk to use Némos for capture and Tana for synthesis — they don't compete on the same surface.

The privacy deep-dive

Tana's data path: every node, every supertag definition, every query crosses Tana's backend on Google Cloud Platform. Tana AI features (auto-summarization, smart references) call OpenAI's GPT-4 API with your node contents serialized as context. Tana retains operational logs and stores backups. The privacy policy is reasonable — no training on user content, 30-day retention on logs — but the surface area includes Tana, GCP, and OpenAI. For confidential strategy work or M&A research, that surface matters.

Némos's data path: notes, screenshots, voice memos, and articles are stored locally in MMKV. iCloud sync (if enabled) uses CloudKit with Advanced Data Protection. AI runs on Apple's on-device Foundation Models via the Neural Engine. There is no third-party LLM and no remote database. The privacy model is structural rather than promotional.

For knowledge work at companies with strict data residency or confidentiality requirements, Tana is often blocked by legal review. Némos is structurally easier to approve.

What happens on a long flight

Tana is mostly unusable offline. The graph operations, supertag queries, and AI features all require network. The mobile app caches a small set of recently-viewed nodes but cannot index new captures. New nodes created offline often fail to sync cleanly on reconnect, occasionally requiring manual conflict resolution.

Némos runs identically online and offline. Voice memos transcribe on-device. Screenshots OCR locally. Semantic search hits a local embedding index. Apple Watch capture relays via Bluetooth. iCloud sync queues encrypted deltas for when WiFi returns.

The hidden costs nobody mentions

Tana's $10-14/month (depending on annual versus monthly billing) is the visible cost. The hidden costs are subtler. First, the learning curve is steep — most users invest 8-15 hours learning the supertag model before they reach productive workflows. Second, building a useful schema requires forethought; the wrong supertag structure compounds painfully over months as your library grows. Third, the AI features ship as add-ons billed separately, so heavy users pay closer to $20/month. Fourth, the lack of plain-text export means leaving Tana requires JSON parsing and custom migration scripts — the export does not preserve the schema structure that made your library useful.

Némos has hidden costs too. No supertag equivalent — we don't model your library as a typed graph. No web app for laptop-first workflows. No team workspaces.

Migration friction (a real timeline)

Week one: export your Tana library as JSON. The export preserves nodes, supertags, and field values but loses the visual hierarchy and query history. Install Némos on iPhone and iPad. Drop the JSON into Némos via the share extension — each node becomes a searchable note with the supertag metadata preserved as tags.

Week two: keep Tana running. Add new captures in Némos for screenshots, voice memos, and articles. Note which Tana queries you actually run regularly.

Week three: assess whether Némos's flat-but-searchable model covers your retrieval needs. For most knowledge workers it does. For users who genuinely need structured queries ("all books rated 4+ stars read in Q2"), Tana remains the better tool.

Week four: cancel Tana if your usage is mostly capture + retrieval. Keep Tana if you actively run schema-driven queries.

Total switching cost: roughly seven hours across the month.

What Apple users specifically gain

Némos was built iPhone-first. Tana was built web-first. The Action Button on iPhone 15 Pro and later starts a Némos voice capture in under one second. The Apple Watch app captures from the wrist. Live Activities pin recording state to the Dynamic Island. Spotlight indexes Némos system-wide. iCloud sync uses Advanced Data Protection. Foundation Models on iOS 26+ run on the Neural Engine. AppIntents expose your library to Siri Shortcuts and Apple Intelligence. None of this surface exists on Tana because Tana is a web product. Apple users who pick Tana end up paying $10-14/month for a product that ignores the platform they use most.

The team-of-one vs team-of-many decision

Tana is increasingly positioned for teams: shared workspaces, collaborative supertag schemas, multi-player editing. For a 5+ person knowledge team, that collaboration is valuable. For a solo knowledge worker maintaining a private library, the team features are weight you're paying for. Most heavy Tana adoption is actually solo; the team workspace usage is concentrated in a small set of consulting firms and research teams. For that minority, Tana is the better fit. For everyone else, the schema discipline is solo work and Némos's structurally simpler model with stronger capture often delivers more retrieval value per hour of effort.