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Databases · head to head

Apache Druid vs Dgraph

Apache Druid logo

Apache Druid

Databases

Real-time analytics database for sub-second OLAP queries

From
Free
Rated
-
Full reviewWebsite
Dgraph logo

Dgraph

Databases

Apache 2.0 distributed graph database written in Go, maintained by Hypermode, queried through GraphQL or its own DQL language.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Druid open-source offering lacks high-availability, distributed architecture, and enterprise security features; Dgraph sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
  • They diverge on capability: Apache Druid covers Real-time Ingestion, Dgraph covers Apache 2.0 licence.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Druid and Dgraph actually diverge.

Attributes where Apache Druid and Dgraph differ
AttributeApache DruidDgraph
Pricing modelopen-sourcefreemium
PlatformsDocker, Kubernetes, Native deployment (Java-based)Linux, Mac, Docker, Web
Founded19992016

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Druid

  • Real-time Ingestion
  • Sub-second Queries
  • Column-oriented Storage
  • Streaming Integration
  • Approximate Algorithms
  • Flexible Schemas
  • Time-based Partitioning
  • Kafka

Only in Dgraph

  • Apache 2.0 licence
  • Generated GraphQL API
  • DQL query language
  • Predicate sharding
  • Raft replication
  • Distributed ACID transactions
  • Written in Go
  • Full-text and geo indexing

What people use each for

The jobs each tool is most often brought in to do.

Apache Druid

  • Real-time analytics platforms ingesting millions of events per second from streaming sourcesnot Dgraph
  • Applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows)not Dgraph
  • Time-series and event analysis at massive scale with columnar storage efficiencynot Dgraph

Dgraph

  • An application whose core data is a graph, such as permissions, social connections or product relationships, where the frontend already consumes GraphQLnot Apache Druid
  • A knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiationnot Apache Druid
  • Recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottlenecknot Apache Druid
  • Teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available onenot Apache Druid

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Druid

  • Open-source offering lacks high-availability, distributed architecture, and enterprise security features
  • Requires native integration with Apache Kafka or Amazon Kinesis for real-time ingestion; custom integrations need development
  • High-concurrency query support (hundreds of thousands QPS) requires significant cluster infrastructure investment

Dgraph

  • Sharding is by predicate, so a single very hot predicate lives entirely in one Raft group and cannot be split further; adding nodes does not relieve it and the fix is a data model change.
  • Stewardship passed from Dgraph Labs to Hypermode after the original company stopped operating independently, so anyone building on it is betting on a second custodian rather than on the original team's roadmap.
  • The GraphQL layer is generated and opinionated, so anything it does not express drops you into DQL, which is a second language your team must learn and which no other database speaks.
  • The community is a fraction of Neo4j's, so operational answers, tuning experience, hiring and third-party tooling are all thinner, and unusual failure modes in a Zero and Alpha cluster leave you reading source rather than a forum.
  • There is no portable graph standard to migrate to; Cypher, Gremlin and SPARQL are all different query models, so the schema and every query is a rewrite if you later leave, and that cost grows with the application.

Pricing, plan by plan

Apache Druid

Free

No published plan breakdown. See the Apache Druid review.

Dgraph

Free
  • CommunityFree
    • Native GraphQL
    • Graph queries
    • Full-text search
  • Cloud$39/month
    • Managed service
    • Auto-scaling
    • Enterprise support

Which should you pick?

Choose Apache Druid if

  • You need real-time ingestion.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Native deployment (Java-based).
  • You also want sub-second queries.
Apache Druid review

Choose Dgraph if

  • You need apache 2.0 licence.
  • You want to start without paying.
  • You work on Linux, Mac, Docker, Web.
  • You also want generated graphql api.
Dgraph review

Questions people ask

Is Apache Druid or Dgraph better?
Neither clearly leads. Apache Druid starts at Free and Dgraph at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Druid or Dgraph?
Apache Druid starts at Free and Dgraph at Free.
Does Apache Druid or Dgraph run on more platforms?
Apache Druid runs on Docker, Kubernetes, Native deployment (Java-based). Dgraph runs on Linux, Mac, Docker, Web.
Can I use Apache Druid for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Druid best used for?
Apache Druid is most often used for real-time analytics platforms ingesting millions of events per second from streaming sources, applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows), time-series and event analysis at massive scale with columnar storage efficiency. Of those, real-time analytics platforms ingesting millions of events per second from streaming sources and applications requiring sub-second queries over high-cardinality datasets (billions to trillions of rows) are not what Dgraph is typically brought in for.
What can Apache Druid do that Dgraph cannot?
Apache Druid covers Real-time Ingestion, Sub-second Queries, Column-oriented Storage, Streaming Integration. Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding.

Answered from the vendors’ own pages

Apache Druid: Is Apache Druid free to use?

Apache Druid is an open-source project with no licensing fees. It is licensed under CC BY-SA 4.0, and the Druid name and logo are trademarks of The Apache Software Foundation.

Source
Dgraph: Is Dgraph open source?

Yes. The current repository is Apache 2.0, which is a permissive OSI licence, and the project is at v25 under Hypermode's maintenance.

Apache Druid: Can I use Apache Druid for commercial purposes?

Yes, Apache Druid is open-source software available for commercial use at no cost. The CC BY-SA 4.0 license permits commercial deployment.

Source
Dgraph: Is it really GraphQL?

It serves a generated GraphQL API, which is real GraphQL for clients. Its native language, DQL, resembles GraphQL syntactically but is Dgraph's own language and is not the GraphQL specification.

Apache Druid: Where do I find pricing for commercial support or services?

No pricing or support tiers are published on the Apache Druid homepage. For commercial support options, contact the Apache Druid community or consult additional resources beyond the project website.

Source
Dgraph: How does it compare to Neo4j?

Neo4j has the larger ecosystem, Cypher, and far more operational precedent. Dgraph distributes storage across nodes by default and gives you a GraphQL endpoint without writing resolvers. The choice usually turns on whether you need horizontal scale and a GraphQL surface more than you need ecosystem depth.

Dgraph: What does a production cluster look like?

At minimum a set of Zero nodes for coordination and a replicated set of Alpha nodes for data, typically three of each for fault tolerance, which is a meaningfully larger operational footprint than a single graph server.

Dgraph: Who maintains it now?

Hypermode. Dgraph Labs, the original company, no longer operates it, and that change of custodian is the main non-technical risk to weigh.

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