Dgraph
Dgraph is a native GraphQL database built for high-performance applications that require a scalable, distributed backend to handle complex data relationships and real-time queries efficiently.
TigerGraph
TigerGraph is a native parallel graph database platform designed to help you analyze massive datasets in real-time to uncover complex relationships and hidden patterns across your business data.
Quick Comparison
| Feature | Dgraph | TigerGraph |
|---|---|---|
| Website | dgraph.io | tigergraph.com |
| Pricing Model | Freemium | Freemium |
| Starting Price | Free | Free |
| FREE Trial | ✘ No free trial | ✓ 0 days free trial |
| Free Plan | ✓ Has free plan | ✓ Has free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2016 | 2012 |
| Headquarters | Palo Alto, USA | Redwood City, USA |
Overview
Dgraph
Dgraph is a native GraphQL database designed to help you build applications with complex data patterns without the overhead of traditional relational mapping. You can store your data as a graph and query it using standard GraphQL or Dgraph's own query language, DQL. This approach eliminates the need for complex joins and allows you to fetch deeply nested data in a single network request, significantly reducing latency for your end users.
You can deploy Dgraph as a managed cloud service or run it on your own infrastructure using Docker or Kubernetes. It is built to scale horizontally, meaning you can handle growing traffic and data volumes by simply adding more nodes to your cluster. Whether you are building a social network, a recommendation engine, or a real-time fraud detection system, Dgraph provides the ACID-compliant reliability and speed you need to manage interconnected data at scale.
TigerGraph
TigerGraph is a high-performance graph database that lets you explore and analyze interconnected data at massive scale. Unlike traditional databases that struggle with complex relationships, you can use TigerGraph to link billions of entities and run deep-link queries in seconds. It combines the power of a native graph engine with the scalability of a distributed system, making it ideal for fraud detection, supply chain optimization, and customer 360 initiatives.
You can build your data models visually and write queries using GSQL, a powerful language that feels familiar if you already know SQL. The platform handles both transactional and analytical workloads simultaneously, so you don't have to move data between different systems. Whether you are a data scientist looking for better features for machine learning or a developer building real-time recommendation engines, you get the speed and scale needed for enterprise-grade applications.
Overview
Dgraph Features
- Native GraphQL Build your backend instantly by providing a GraphQL schema—Dgraph automatically generates the database and API for you.
- Distributed Architecture Scale your database horizontally across multiple nodes to handle massive datasets and high-traffic applications with ease.
- ACID Transactions Ensure your data remains consistent and reliable with fully distributed ACID transactions across all your database shards.
- Full-Text Search Implement powerful search capabilities directly in your queries, including term matching, regular expressions, and multi-language support.
- Geo-Location Queries Store geographical data and perform complex spatial queries like finding points within a specific radius or polygon.
- Automated Sharding Let the system handle data distribution automatically, rebalancing your data across the cluster to prevent performance bottlenecks.
TigerGraph Features
- Native Parallel Graph. Execute complex queries across billions of vertices and edges simultaneously to get real-time results from your largest datasets.
- GSQL Query Language. Write powerful, high-level queries with a language that combines the familiarity of SQL with the flexibility of graph traversals.
- Distributed Architecture. Scale your database horizontally across multiple nodes to handle massive data growth without sacrificing performance or speed.
- GraphStudio UI. Design your graph schema, map data, and explore results visually through an intuitive web-based interface for faster development.
- Deep Link Analytics. Traverse 10 or more hops across your data to uncover hidden relationships that traditional databases simply cannot find.
- Multi-Graph Security. Create multiple logical graphs on a single cluster to securely share data across different teams and departments.
Pricing Comparison
Dgraph Pricing
- Shared cluster deployment
- 1MB/sec data transfer
- 1 million credits per month
- Community support
- Automatic backups
- Everything in Free, plus:
- Dedicated hardware resources
- High availability replication
- VPC Peering capabilities
- Advanced security features
- Priority technical support
TigerGraph Pricing
- 1 graph solution
- Up to 50GB storage
- Shared CPU resources
- Community support
- Access to 20+ starter kits
- Everything in Free, plus:
- Dedicated instances
- Scalable storage options
- Backup and restore
- Standard support
- Pay-as-you-go pricing
Pros & Cons
Dgraph
Pros
- Simplifies backend development with native GraphQL support
- Handles deeply nested data relationships extremely fast
- Scales horizontally to support massive data growth
- Open-source core allows for flexible deployment options
Cons
- Learning curve for DQL advanced query features
- Documentation can be sparse for complex edge cases
- Managed cloud pricing can scale quickly with usage
TigerGraph
Pros
- Exceptional performance on deep-link queries
- Scales horizontally to handle massive datasets
- GSQL language is powerful and expressive
- Visual design tools simplify graph modeling
- Excellent for real-time fraud detection use cases
Cons
- Steep learning curve for GSQL language
- Documentation can be difficult to navigate
- Requires significant memory for large graphs