Tana's supertags are genuinely innovative. Tag a node as #meeting and it gains fields for date, attendees, and action items. Tag it as #person and it gets email, company, and role fields. The system is powerful, but it requires you to design the tag hierarchy, define the fields, and consistently apply the right tags to every piece of data.

Synap's entity profiles serve the same purpose — typed data objects with property schemas. But instead of you applying types manually, AI does it. Drop in a messy note about a meeting, and AI proposes: create an event entity with these attendees, extract three tasks with due dates, and link to the project entity. You review and approve. The structure is the same quality; the effort is zero.

Tana is one of the most powerful knowledge tools ever built. It's also one of the hardest to learn. The outliner paradigm, supertag configuration, live searches, and field inheritance create a system that rewards expertise but punishes beginners. Most users never unlock its full potential because the onboarding friction is too high.

Synap's approach is: you should never need to learn a system. Capture anything — text, link, file, voice message. AI handles typing, tagging, and relating. If you want to customize entity profiles or create new types, you can. But you don't have to. The default experience is zero-configuration.

Tana is a SaaS product with no self-hosting option and limited export capabilities. Your supertag system, your carefully structured data, your entire knowledge graph — it all lives on Tana's servers. If they change direction, increase prices, or shut down, your investment in building that system is at risk.

Synap runs on a dedicated PostgreSQL pod. Self-host it, export it with pg_dump, connect any SQL client, or build your own applications on the API. Your entity graph is stored in standard relational tables you can query directly. The structure you build — whether manually or through AI — belongs to you permanently.