Graph
The semantic property graph database with data model validation. Rich semantic context made simple - no RDF complexity, just powerful relationships with built-in validation for growing companies.
Open Source — All Konnektr products are open-source and available for self-hosting.
Rich Semantic Context Made Simple
Skip the RDF complexity. Property graphs with built-in validation for startups and growing companies who need meaningful data relationships.
Meaningful Relationships
Connect your data with rich semantic relationships. No RDF complexity, just intuitive property graphs that grow with your business.
Schema Validation
Prevent data corruption with built-in model validation. Ensure data integrity as your startup scales without breaking changes.
Your Data, Your Control
Built on PostgreSQL foundation you can audit and trust. Start small, scale smart, without vendor lock-in or enterprise complexity.
Visualize & Query with Graph Explorer
Graph Explorer provides an intuitive interface to interact with your digital twin graph. Query your data, visualize relationships, and explore your semantic model without writing code.
Full Model & Data Management
Load and inspect DTDL models, create twin and relationship instances, update properties, and run Cypher queries with an integrated code editor.
Relationship Visualization
See your digital twin relationships in an interactive graph view. Understand complex data structures at a glance.
Stop Fighting Your Database
Your growing company has complex data that traditional databases can't handle. You have:
- Complex relationships that SQL makes painful to query
- Data validation nightmares when your schema evolves
- Semantic context loss in rigid database schemas
- Vendor lock-in fears with proprietary graph databases
Graph Makes It Simple
Property graphs with built-in validation, powered by PostgreSQL you already trust.
- Natural relationships - model how you think
- Schema validation - prevent breaking changes
- PostgreSQL foundation - audit & trust
- Open source - no vendor lock-in
Real-World Use Cases
See how Graph enables digital twins that bring value to the physical world.
Water Distribution Systems
Utilities • Infrastructure • Smart Cities
The Challenge: Water networks have complex pipe connections, pressure dependencies, and sensor relationships. Leaks cascade through the system unpredictably.
With Graph: Model pipes, valves, sensors, and pressure zones as connected assets. Detect leak impact zones and optimize meter commissioning workflows.
Traffic Infrastructure
Transportation • Smart Cities • Asset Management
The Challenge: Traffic lights, bridges, signs, and sensors form an interconnected system. Asset failures create cascading traffic impacts.
With Graph: Model traffic infrastructure as connected assets. Predict failure impacts and assess traffic flow consequences before they happen.
Railway Operations
Rail Transport • Energy Management • Operations
The Challenge: Train schedules, track sections, power grids, and energy consumption are deeply interconnected. Optimization requires understanding complex dependencies.
With Graph: Connect trains, tracks, power systems, and schedules in one model. Optimize energy usage and detect conflicts before they impact service.
Infrastructure Monitoring
Civil Engineering • Asset Health • Environmental
The Challenge: Structural sensors, environmental conditions, and asset health create complex monitoring networks. Anomalies need contextual analysis.
With Graph: Model sensors, structures, and environmental factors as connected entities. Correlate deformation, temperature, and groundwater data intelligently.
Built for Developers & AI Agents
Integrate Graph with your existing tools or build semantic knowledge bases for AI applications. Compatible with Azure Digital Twins SDK and standard REST APIs.
Use Existing Azure Tools
Drop-in replacement for Azure Digital Twins. Use the official Azure SDK with your Konnektr Graph endpoint—no code changes required.
# Python example
from azure.digitaltwins.core import DigitalTwinsClient
from azure.identity import DefaultAzureCredential
# Point to your Konnektr Graph instance
endpoint = "https://<resource-id>.api.graph.konnektr.io"
credential = DefaultAzureCredential()
client = DigitalTwinsClient(endpoint, credential)
# Query your digital twins
query = "SELECT twin, rel, sensor FROM DIGITALTWINS MATCH (twin:Twin)-[rel:isObservedBy]->(sensor:Twin)
WHERE sensor.$dtId = 'sensor-001'
twins = client.query_twins(query)
for twin in twins:
print(twin)Semantic Knowledge for AI
Provide rich contextual knowledge to your AI agents. Query relationships and semantic data through simple REST APIs.
# REST API example for AI agent context
import requests
api_url = "https://<resource-id>.api.graph.konnektr.io"
headers = {"Authorization": "Bearer <token>"}
# Get related entities for RAG context
query = """
MATCH (twin:Twin)-[rel:isObservedBy]->(sensor:Twin {`$dtId`: 'sensor-001'})
RETURN twin, rel, sensor
"""
response = requests.post(
f"{api_url}/query",
headers=headers,
json={"query": query}
)
context = response.json()
# Feed context to your LLM or agentWhy Choose Graph Over Alternatives?
Compare Graph with other solutions and see why growing companies choose us.
vs SQL Databases
Traditional relational databases
Complex JOINs for relationships
Multiple table joins get expensive fast
Schema migrations are painful
Adding relationships breaks existing code
Graph: Natural relationships
Query relationships as you think about them
vs Proprietary Graphs
Neo4j, Amazon Neptune, etc.
Expensive licensing costs
Enterprise pricing that hurts growth
Vendor lock-in risks
Hard to migrate away when you grow
Graph: Open source & PostgreSQL
No lock-in, audit the code, trust the foundation
vs Document Databases
MongoDB, CouchDB, etc.
No relationship enforcement
References can break without validation
Complex queries for connected data
Multiple queries needed for relationship traversal
Graph: Relationships as first-class citizens
Validate connections, traverse efficiently
Get the best of both worlds: graph power with SQL reliability. Battle-tested foundation that your team can audit, trust, and extend.
Simple, Transparent Pricing
Start free for development and testing. Scale to production with our Standard plan. All plans include the full power of semantic property graphs.
Features
Twin Instances
Maximum digital twins per resource
Rate Limits
Query units per minute
Authentication
Access control methods
Events & History
Real-time notifications and audit logs
Support
Help and assistance level
Use Case
Best suited for
Developer
For development & testing
per month / resource
Up to 500
1,000 QU/min
User Authentication
Device Code Flow only
Not available
Community
GitHub Issues
Development & Testing
Need More?
Enterprise plans available with unlimited twins, custom API limits, high-availability SLA, and dedicated support.