Cognee vs Skan AI

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Cognee compared with Skan AI
Detail	Cognee	Skan AI
What it does	Open-source memory platform for AI agents	Not given
Stage	Not given	Established
Founded	Not given	2020
Based in	DE	US
Pricing model	Not given	Contact for pricing
Pricing	Free: $0/month with 1M tokens and 1 workspace (no card required). Standard: $1.00 per 1M tokens plus $5 per additional workspace. Enterprise: contact for pricing with bring-your-own-cloud deployment. Open-source engine available free to self-host.	Not given
Key features	Unified vector search, knowledge graphs, and relational storage Automatic knowledge graph generation from data sources Multi-tenant support with isolated workspaces Data source integrations (Slack, Notion, Google Drive, GitHub, etc.) Self-improving memory that learns from use Multiple deployment options (cloud, self-hosted, air-gapped) Agent framework integrations (LangGraph, CrewAI, OpenAI SDK, MCP) Support for multiple vector and graph databases GDPR compliance and encryption at rest and in transit Built-in graph visualization of company brain	Context Graph of Work - continuously updated model of how work gets done across enterprises Blueprint for AI roadmap planning with defensible ROI recommendations Process Intelligence to identify bottlenecks and prioritize automation Engineering Intelligence for unified view of developer work Work-aware agents grounded in real operational context Zero-integration capture across legacy and modern systems In-environment processing with data staying in customer perimeter Agentic Operating Procedures for governed agent execution Audit logs and compliance tracking for every agent decision Built on 25B+ events from 500+ observed processes
What makes it different	Combines vectors with graph relationships and generated data models for connected context retrieval State of the art performance on BEAM benchmark (79% vs 73.4% at 100k tokens) Keeps memory costs flat as data grows compared to baseline LLM approaches Fully open-source with no database lock-in Updates memory as source data changes, not just static snapshots	Observes actual work at scale across enterprise systems rather than relying on assumptions or documentation Delivers AI agents that work in production at scale, not just pilots Provides measurable ROI and audit trails for AI spending and decisions Privacy-first architecture keeps sensitive data within customer environments
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	70 no change since last check	54 no change since last check
Trust Flow (Majestic)	14	14
Citation Flow (Majestic)	43	38
Referring domains (Majestic)	1089	463

3 details filled in for both companies.

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