Dgraph Vs Tigergraph

Misc
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Hi Community,

TigerGraph is already in use in my Organization ( other team ) and I want to explore Dgraph.
So, I would like to know your opinions on why we should go with Dgraph.

Thanks
Shyam

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Key Advantages of Dgraph over TigerGraph

1. Open Source & Licensing

  • Dgraph is open-source under the Apache 2.0 license, meaning you can use, modify, and deploy it without restrictions.
  • TigerGraph follows a proprietary model with a “limited free-tier version” (Community Edition), requiring licensing for full-scale deployments.

If you need a truly open-source graph database with no licensing constraints, Dgraph is preferable.

2. Query Language & Ease of Use

  • Dgraph uses Dgraph Query Language (DQL), which is JSON-based, easy to learn, and supports GraphQL natively.
  • TigerGraph uses GSQL, a SQL-like query language that is powerful but complex, requiring a steeper learning curve.

If your team is comfortable with GraphQL, JSON, or needs a simpler query language, Dgraph is more user-friendly.

3. Native Graph + Vector Search

  • Dgraph supports native vector storage and semantic/similarity search capabilities OOTB,. The ability to run graph + vector hybrid queries open up greater possibilities for building real-world but complex AI/ML applications using connected data.
  • TigerGraph lacks native vector storage/search and requires additional integrations.

For several real-world popular use-cases requiring vector storage and semantic search, like fraud detection, recommendation systems, or any AI-driven queries, Dgraph has a clear advantage with its native vector capabilities.

4. Horizontal Scalability

  • Dgraph is built for massive horizontal scalability, is natively distributed with a simple yet unique sharding architecture, and works well for HTAP workloads.
  • TigerGraph uses a “shard-and-replicate model”, but its scalability is more manual and license-restricted.

The advantages of relationship-based sharding over traditional entity-based sharding are pretty well known wrt performance so if you need an easy-to-scale, distributed graph database, Dgraph offers high-performance sharding and replication features.

5. Simple Deployment & Cloud-Native

  • Dgraph is easy to deploy on Kubernetes, AWS, GCP, or even locally. It does not require a separate graph processing engine.
  • TigerGraph has a more complex deployment model and needs specific compute and storage configurations.

If you want a lightweight, Kubernetes-friendly, and easy-to-deploy graph database, Dgraph is a better fit.

6. Performance & Ingestion Speed

  • Dgraph supports high-speed batch ingestion (JSON, RDF, GraphQL [still JSON UTC ] mutations) without additional tools.
  • TigerGraph requires custom ETL processes and GSQL loading jobs.

If you need fast streaming or batch ingestion, Dgraph provides a simpler and more scalable solution.

7. HTTP & GraphQL APIs Out-of-the-Box

  • Dgraph provides a GraphQL API natively - you can query the database with GraphQL without additional setup.
  • TigerGraph requires manual REST API development using GSQL queries.

If you want GraphQL support for front-end or external applications, Dgraph is the superior choice.

Conclusion:

If you need a fully open-source, cloud-native, GraphQL-first, vector + graph hybrid, and highly scalable database, Dgraph is the better choice.

If you need advanced graph analytics with SQL-like querying** and are fine with proprietary licensing, TigerGraph may be suitable.

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Thanks !!

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