Weaviate vs Cognee

Detailed side-by-side comparison to help you choose the right tool

Weaviate

🔴Developer

AI Knowledge Tools

Open-source vector database enabling hybrid search, multi-tenancy, and built-in vectorization modules for AI applications requiring semantic similarity and structured filtering combined.

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Starting Price

Free

Cognee

🔴Developer

AI Knowledge Tools

Open-source framework that builds knowledge graphs from your data so AI systems can analyze and reason over connected information rather than isolated text chunks.

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Starting Price

Free

Feature Comparison

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FeatureWeaviateCognee
CategoryAI Knowledge ToolsAI Knowledge Tools
Pricing Plans4 tiers8 tiers
Starting PriceFreeFree
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling

Weaviate - Pros & Cons

Pros

  • Open-source vector database with rich hybrid search capabilities
  • Supports both vector and keyword search in one system
  • Built-in module system for vectorization and ML models
  • Self-hostable or managed cloud — flexible deployment options
  • GraphQL API provides powerful and flexible querying

Cons

  • Self-hosting requires significant operational expertise
  • Resource-intensive for large-scale deployments
  • Learning curve for the module and schema system
  • Cloud pricing can be significant for production workloads

Cognee - Pros & Cons

Pros

  • Dual knowledge representation enables both relational and semantic retrieval strategies
  • Pipeline-based architecture provides flexibility for domain-specific knowledge structures
  • Open-source approach eliminates vendor lock-in with standard graph database storage
  • Supports diverse input types with unified knowledge graph representation
  • Superior performance for complex queries requiring relationship understanding
  • Visual graph exploration capabilities aid in knowledge discovery and validation

Cons

  • Requires domain-specific configuration for optimal knowledge extraction quality
  • Relatively young project with documentation still catching up to capabilities
  • Knowledge graph quality heavily depends on input data quality and extraction models
  • Neo4j dependency adds infrastructure complexity compared to vector-only solutions
  • Steeper learning curve for teams unfamiliar with graph database concepts
  • Graph consistency management challenging with dynamic or frequently updated data

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🔒 Security & Compliance Comparison

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Security FeatureWeaviateCognee
SOC2✅ Yes
GDPR✅ Yes
HIPAA
SSO🏢 Enterprise
Self-Hosted🔀 Hybrid✅ Yes
On-Prem✅ Yes✅ Yes
RBAC✅ Yes
Audit Log
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyUS, EU
Data Retentionconfigurableconfigurable
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