A technical guide for teams evaluating graph databases built for scale, performance, and AI-driven workloads.
Introduction
Data today is deeply interconnected.
Understanding how systems, entities, and processes relate drives better accuracy, performance, and insight.
Graph databases model these relationships directly. They make it possible to traverse complex networks of data—customers, assets, transactions, events—and surface context that traditional relational or document stores can’t capture.
This guide explains how to evaluate modern graph databases, what architectural details matter, and which questions reveal real technical differences between legacy and next-generation systems.
Why Graph Databases Matter
Relationships sit at the center of nearly every data challenge.
- Fraud detection: Link accounts, transactions, and behaviors to identify risk.
- Cybersecurity: Connect devices, users, and network events to spot anomalies.
- Healthcare and life sciences: Trace patient journeys, treatments, and outcomes.
- Supply chain optimization: Map dependencies across suppliers, logistics, and inventory.
- Recommendation and search: Find relevant matches using context and similarity.
Traditional databases manage data in isolation. Graph databases are designed to understand how data connects, which makes them critical for advanced analytics, knowledge graphs, and AI applications that depend on context.
The Cost of Getting It Wrong
Choosing the wrong graph database can create long-term friction—fragmented integrations, high maintenance costs, and scaling limitations.
When data volumes grow or AI workloads are introduced, systems that rely on stitched components or separate query layers often fail to deliver consistent performance.
A modern platform should provide:
- A single native engine for all data models
- A unified query language
- Built-in search, analytics, and governance
- Horizontal scalability and high availability
Evaluating Modern Graph Databases
Five technical areas determine whether a graph database can meet enterprise and AI workloads.
Each section outlines what to look for and the key questions to ask vendors.
1. Data Model and Storage
A unified data foundation should combine graph, document, key-value, vector, and search models in one native engine—no external connectors or synchronization jobs.
This eliminates silos, improves performance, and simplifies consistency across workloads.
Questions to Ask:
- Are all data models supported natively, or layered through plugins and APIs?
- Can queries span multiple models (graph, document, vector) with a single command?
- How is data consistency maintained as scale and concurrency increase?
2. Developer Productivity
The best graph databases reduce operational complexity. Developers shouldn’t manage partitions, rewrite queries for each data type, or maintain brittle integrations.
Native JSON support, one query language (like AQL), and transparent scaling accelerate delivery and reduce learning curves.
Questions to Ask:
- Is there one query language across all data models?
- Is sharding handled automatically or manually configured?
- Can developers perform analytics and traversals in real time without extra setup?
3. Scalability, Performance, and Availability
As data and user demand grow, scaling must be seamless.
Modern systems scale horizontally, distribute workloads evenly, and maintain low latency with automatic recovery and fault tolerance.
Legacy architectures often require manual tuning or extra infrastructure to stay performant.
Questions to Ask:
- How does the database handle horizontal scaling and node failover?
- What performance guarantees exist for large or mixed workloads?
- Can data be restored to any point in time for recovery or audit purposes?
4. Search and Geospatial Integration
Search and spatial queries should be built into the core—not added through separate products.
Native text, semantic, and geospatial search capabilities allow unified queries across structured and unstructured data for faster, more relevant insights.
Questions to Ask:
- Are search and spatial features part of the core engine or external integrations?
- How is spatial indexing optimized for performance at scale?
- Can I combine graph traversals, search, and geospatial filters in a single query?
5. Analytics, ML, and AI Readiness
Modern applications require reasoning and retrieval, not just storage.
Graph-based machine learning (GraphML), vector search, and GraphRAG capabilities enable AI systems to work directly on the data foundation—without separate pipelines or re-indexing.
These capabilities improve explainability, context, and accuracy in production AI workloads.
Questions to Ask:
Key Takeaways
- A true multi-model design simplifies architecture and removes silos.
- One query language reduces development time and risk.
- Built-in search, vector, and analytics make the platform AI-ready.
- Native scalability and governance support enterprise reliability.
- Detailed vendor questions expose real architectural differences.
Conclusion
A graph database should be more than a data store—it should form a reliable, scalable foundation for building context-rich, high-performance applications.
ArangoDB delivers this foundation through a native multi-model engine that unifies graph, document, key-value, vector, and search data with one query language.
It provides horizontal scalability, governance, and built-in analytics to help teams simplify their data stack, reduce cost, and prepare for AI-driven workloads.
Choose a database designed for today’s complexity and tomorrow’s scale.
