Standalone graph database

A graph database built for queries others can't answer.

Deep, complex graph queries that take other databases hours or days, answered in seconds. That's not a benchmark curiosity. It means real-time systems instead of batch pipelines, AI agents that can reason across your entire graph, and architectural simplicity where others need caching, pre-computation, and workarounds.

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What this means in practice

Performance that changes what you can build

Real-time instead of batch

Queries across 5, 10, 20+ relationship hops execute in milliseconds. Build real-time applications where others require overnight batch processing.

No architectural workarounds

No need for pre-computation, caching layers, or denormalization. The database is fast enough that your architecture can be simple.

AI agents that reason at depth

AI and MCP agents can traverse your full graph in real time. No incomplete results, no hallucination from truncated traversals.

Queries that were impossible, now operational

Multi-hop relationship queries that other databases literally cannot answer in production become routine operations.

The evidence
500x
faster on typical complex queries
>10,000x
faster on deep graph traversals
O(n)
linear time complexity
2.6s
vs 31 hours (the market leader, 4-hop)
1-hop
2-hop
3-hop
4-hop
Data Graphs Market leader Other graph databases

Log scale. Query performance measured on identical hardware, same benchmark dataset.

Real-world proof

When a global manufacturer needed real-time supply chain traceability

The use case: traverse deeply connected supply chain data across components, sub-assemblies, raw materials, suppliers, certifications, and regulatory requirements. Queries spanning multi-tier supplier networks, from finished product back to source material across 10 to 15 relationship layers. Results needed in real time to support compliance checks, risk assessment, and regulatory reporting.

Traditional graph database approaches could not deliver. Queries became too slow beyond a few hops. Manual workarounds and pre-processing pipelines were required. The system broke under the depth and complexity of the supply chain relationships. It could not support real-time traceability at production scale.

Why Data Graphs succeeded
✓ Consistent high-speed execution at depth, across 5 to 20+ hops
✓ No pre-computation, no caching, no denormalization needed
✓ Predictable performance regardless of graph complexity
✓ Real-time multi-entity traversal supporting live workflows
✓ Scaled across interconnected datasets without degradation

This is not a marginal improvement. It is a capability shift: from graph as a storage layer to graph as an execution layer.

Under the hood
⚡
Proprietary query algorithm
Graph indexes encoded in hyper-dense bitmaps. Query results generated using bitmap intersection, not graph-walking. Fundamentally different computer science.
📐
O(n) linear performance
Time complexity scales linearly regardless of depth. Other databases scale exponentially, meaning every additional hop multiplies query time.
🦀
Built in Rust
Ultra-low memory footprint. Compiles natively for any target: cloud, on-prem, mobile, edge devices.
🔥
GPU-optimized parallelization
Embarrassingly parallel query execution. Low-level parallelization with virtually no synchronization overhead.
🔄
OpenCypher/ISO GQL
Full compatibility with the most widely used graph query language. Existing queries work without rewriting.
🌐
Hybrid graph model
Best of property-label and RDF/OWL ontology semantics. JSON-LD payloads for W3C RDF compatibility.
Why this matters for AI

Graph performance directly impacts AI reliability

When an AI agent needs to traverse your knowledge graph to answer a question, slow graph queries mean incomplete traversals, which mean incomplete context, which mean hallucinated or wrong answers.

Data Graphs enables real-time multi-hop reasoning for AI agents. It replaces brittle RAG pipelines with structured, deterministic queries. AI outputs become traceable, explainable, and reliable because the underlying graph can actually be traversed in full, in real time, at production scale.

✓ Real-time multi-hop reasoning for AI agents
✓ Prevents hallucination from incomplete traversal
✓ Replaces brittle RAG with structured, deterministic queries
✓ Supports Agentic AI and MCP architectures
✓ Makes AI outputs traceable and explainable
Deployment options

Deploy anywhere. Your data stays where you put it.

Cloud-managed

Fully managed within the Data Graphs SaaS platform. Zero infrastructure to maintain.

Included in platform

On-prem / Sovereign Cloud

Deploy standalone on your own infrastructure. Full data sovereignty and control.

Enterprise license

On-device

Compiled natively for Android and other targets. Sub-millisecond retrieval. Tiny footprint.

Edge deployment

See the performance difference for yourself

Request benchmark data or talk to our team about how the Data Graphs engine can power your most demanding workloads.

Request benchmark data Technical whitepaper