Dgraph is an open-source distributed graph database with native GraphQL support. Explore its architecture, self-hosted licensing, and Dgraph Cloud pricing.
Category
Databases
Pricing
Free/Open Source with paid Cloud tiers, from Free
Verified
Not yet
Last updated
July 18, 2026
Founded
2015
Headquarters
San Francisco, California, USA
Free PlanAPIOpen SourceFreemiumSelf-Hosted
Overview
Dgraph is an open-source, natively distributed graph database built in Go, designed for low-latency queries over highly connected data using either GraphQL or its native DQL query language.
Founded in 2015 by ex-Google engineer Manish Rai Jain, Dgraph became one of the most-starred graph databases on GitHub, and is now maintained under Istari Digital following acquisitions by Hypermode in 2023 and Istari in 2025.
Key Features
Dgraph shards and replicates data automatically across a cluster, scaling storage and query throughput roughly linearly as nodes are added, unlike single-node graph databases.
It supports GraphQL as a first-class API alongside its own DQL query language, letting developers query a graph directly without a separate GraphQL translation layer.
Pricing
The core Dgraph database is free and open-source to self-host with no licensing fees.
Dgraph Cloud offers a free-forever tier, a Shared tier around $39.99/month per backend, and a Dedicated tier starting around $199/month for isolated multi-availability-zone clusters on AWS, GCP, or Azure.
Key Features
Native distribution and sharding — Automatically shards and replicates data across a cluster for horizontal scalability and fault tolerance.
GraphQL-native querying — Supports GraphQL as a first-class query interface directly against the database, without a translation layer.
DQL query language — Offers its own Dgraph Query Language for complex graph traversals beyond what GraphQL alone can express.
Open-source self-hosting — The core database can be downloaded and self-hosted for free under an open-source license.
Dgraph Cloud managed hosting — A fully managed cloud service with free, shared, and dedicated tiers removes operational overhead.
ACID transactions — Supports distributed ACID-compliant transactions across the cluster for data consistency.
Go-based architecture — Built in Go for performance and concurrency, with Docker images used over ten million times.
Enterprise security add-ons — Dgraph Enterprise adds access control, security, and dedicated support for self-managed deployments.
Pros & Cons
Pros
Free and open-source core with no licensing cost for self-hosting
Natively distributed architecture scales linearly with added nodes
First-class GraphQL support simplifies application development
Strong open-source community with an active GitHub project
Cons
Ownership changes (Hypermode acquisition in 2023, Istari Digital in 2025) create uncertainty about long-term commercial roadmap
Smaller ecosystem and talent pool compared to Neo4j
Managed Cloud pricing can add up for high-data-transfer workloads
Steeper learning curve for teams unfamiliar with graph query languages
Pricing
Open Source (self-hosted) Free N/A
Dgraph Cloud Free Free N/A
Dgraph Cloud Shared $39.99/month per backend Monthly
Dgraph Cloud Dedicated From $199/month Monthly
Frequently Asked Questions
Is Dgraph free to use?
Yes, the core Dgraph database is free and open-source to self-host; paid tiers only apply to the managed Dgraph Cloud service or enterprise add-ons.
What query languages does Dgraph support?
Dgraph supports GraphQL natively as well as its own query language called DQL (Dgraph Query Language).
Who created Dgraph?
Dgraph was created in 2015 by Manish Rai Jain, a former Google engineer, through the company Dgraph Labs.
How much does Dgraph Cloud cost?
Dgraph Cloud offers a free tier, a Shared tier around $39.99/month per backend, and a Dedicated tier starting around $199/month.
Who owns Dgraph now?
Dgraph Labs was acquired by Hypermode in 2023, and the combined company was acquired by Istari Digital in October 2025.
What is Dgraph used for?
Dgraph is used for applications with highly connected data such as social graphs, recommendation engines, knowledge graphs, and fraud detection systems.