TuringDB Logo

Engineered for real-time
graph analytics

The fastest in-memory graph database for healthcare, finance, supply chains, and operational intelligence.

Trusted & supported by:

NHS
University of Oxford
Leon Berard
Roche
WHO
Sanofi
NHS
University of Oxford
Leon Berard
Roche
WHO
Sanofi

The new standard in graph performance

SPEED
1x
Faster than traditional DBs
COMPLEXITY
500.000.000+
Nodes supported for real world scale
MEMORY
10
A simple machine is enough

Low-latency queries

Get complex graph query responses in milliseconds, for a smooth, fast User Experience (UX). Build faster analytics, simulations, and AI systems

No reads & writes competition

Zero-lock concurrency ensures your reads and writes never compete, so your analytics are never slowed down by your data inputs

Full snapshot isolation

The immutable snapshots ensure that specific versions of your graph remain the same and cannot be changed, critical for reproducibility & auditability

Git-like versioning

Reduce maintenance complexities with auditable graphs: create and commit versions, keep different versions in branches, merge in main, and time travel

Rich metadata for AI

Nodes & edges can store rich metadata with unlimited properties, perfect for digital twins or for LLMs and agentic AI systems that need context

Compatible with Cypher

No need to learn a new language, rewrite your current queries, or use query writing LLMs: simply use Cypher to query TuringDB graphs at scale

Sub-millisecond query execution

TuringDB's columnar in-memory architecture delivers lightning-fast graph traversals that outperform traditional databases by orders of magnitude. Run complex multi-hop queries on billion-node graphs in milliseconds, not seconds.

111,532,358 nodes · 1,615,685,872 edges

TuringDB

Other DBs

The first graph database with native Git-style version control

Store every change as an immutable commit. Query any version at full speed. Branch and merge for risk-free exploration with powerful time-travel analytics and complete audit trails.

Rich metadata for high-context retrieval

TuringDB can store unlimited properties on nodes and edges along with large chunks of text and numerical values. Perfect for feeding LLMs and AI agents with context, building multilayer graphs, and creating graph-based digital twins for data and knowledge representation.

Manufacturing Plant Digital Twin

Click on any unit to view its metadata

Turbine-A1

EquipmentTurbinePowerGenerationCriticalAsset
Core Properties
id:turb-a1-2024
type:Steam Turbine
location:Building 3, Floor 2
manufacturer:Siemens AG
operating_hours:38900 hrs
Operational Metrics
temperature:547°C
pressure:165.2 bar
rpm:3,600
power_output:125.8 MW
vibration:2.1 mm/s
Context

High-pressure steam turbine installed in 2018. Critical component for power generation. Requires specialized maintenance team. Connected to boiler B-12 and generator G-5 with a 99.2% uptime...

Relationships
INCOMING:
separator-s2FEEDS_INTO
OUTGOING:
generator-g5POWERED_BY
condenser-cd1CONNECTED_TO
Talk to Our Team

Your shortest path to
high performance

Our engineers are experienced in helping companies integrate and get the most out of TuringDB. Schedule a call to discuss data modeling, query optimization, infrastructure requirements, or database migration.

30-minute technical discussion tailored to your use case
Learn from engineers with real-world deployment experience
Get practical insights on integrating TuringDB into your stack
YouThe graph performance you need