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Pricing sourced from Cognee · Last verified March 2026
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View Full Features →Traditional RAG treats document chunks as isolated text and uses vector similarity for retrieval. Cognee builds knowledge graphs that understand entity relationships, enabling questions like 'which companies did John Smith work for before founding his startup?' that require multi-hop reasoning across connected entities.
Neo4j requires database management, backup strategies, and scaling configuration that vector-only solutions avoid. However, Cognee's managed cloud service handles infrastructure automatically. Self-hosted deployments need Neo4j expertise or dedicated devops support.
Accuracy depends on document type and domain. Business documents with clear entity names (companies, people, locations) work well. Technical documents with domain-specific entities require custom extraction models. Expect 80-90% accuracy on standard business content, lower for specialized fields.
Yes, through API endpoints and the hybrid retrieval system. You can query Cognee's knowledge graphs alongside existing vector databases. Many teams use it as an additional reasoning layer on top of existing RAG infrastructure.
AI builders and operators use Cognee to streamline their workflow.
Try Cognee Now →The industry-standard framework for building production-ready LLM applications with comprehensive tool integration, agent orchestration, and enterprise observability through LangSmith.
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