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Databases · head to head

Apache Kafka vs Dgraph

Apache Kafka logo

Apache Kafka

Databases

Open-source distributed event streaming platform

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 Kafka operationally heavy to self-host: brokers, storage, rebalancing and upgrades are a standing job, which is why managed Kafka is a large market; 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 Kafka covers Durable commit log, 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 Kafka and Dgraph actually diverge.

Attributes where Apache Kafka and Dgraph differ
AttributeApache KafkaDgraph
Pricing modelOpen source, no licence fee; managed services billed separatelyfreemium
PlatformsLinux, Windows, macOS, Self-hosted, DockerLinux, Mac, Docker, Web
FoundedUnknown2016

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 Kafka

  • Durable commit log
  • Horizontal scale
  • Kafka Connect
  • Kafka Streams
  • Replication
  • Low latency

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 Kafka

  • Moving events between services without point-to-point couplingnot Dgraph
  • Feeding analytics and warehouses from operational systems in near real timenot Dgraph
  • Replaying history to rebuild state after a consumer bugnot Dgraph
  • Buffering bursty producers ahead of slower downstream systemsnot 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 Kafka
  • A knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiationnot Apache Kafka
  • Recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottlenecknot Apache Kafka
  • Teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available onenot Apache Kafka

Where each one falls short

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

Apache Kafka

  • Operationally heavy to self-host: brokers, storage, rebalancing and upgrades are a standing job, which is why managed Kafka is a large market
  • Overkill for straightforward job queues, where a simpler broker is easier to run and reason about
  • Ordering guarantees hold per partition, not per topic, and getting partitioning wrong is a common and expensive design mistake
  • The ecosystem is fragmented across the Apache project and vendor distributions, so documentation and tooling advice often assume a particular distribution

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 Kafka

Free
  • Apache KafkaFree
    • Full platform
    • Kafka Connect
    • Kafka Streams

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 Kafka if

  • You need durable commit log.
  • You want to start without paying.
  • You work on Linux, Windows, macOS, Self-hosted, Docker.
  • You also want horizontal scale.
Apache Kafka 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 Kafka or Dgraph better?
Neither clearly leads. Apache Kafka 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 Kafka or Dgraph?
Apache Kafka starts at Free and Dgraph at Free.
Does Apache Kafka or Dgraph run on more platforms?
Apache Kafka runs on Linux, Windows, macOS, Self-hosted, Docker. Dgraph runs on Linux, Mac, Docker, Web.
Can I use Apache Kafka for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Kafka best used for?
Apache Kafka is most often used for moving events between services without point-to-point coupling, feeding analytics and warehouses from operational systems in near real time, replaying history to rebuild state after a consumer bug, buffering bursty producers ahead of slower downstream systems. Of those, moving events between services without point-to-point coupling and feeding analytics and warehouses from operational systems in near real time are not what Dgraph is typically brought in for.
What can Apache Kafka do that Dgraph cannot?
Apache Kafka covers Durable commit log, Horizontal scale, Kafka Connect, Kafka Streams. Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding.

Answered from the vendors’ own pages

Apache Kafka: Is Apache Kafka free?

Yes. Kafka is open source under the Apache License v2 with no licence fee. Costs come from the infrastructure you run it on, or from a managed service such as Confluent Cloud.

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 Kafka: How is Kafka different from a message queue?

A queue usually removes a message once it is consumed. Kafka keeps an ordered, durable log, so consumers track their own position and history can be replayed — which is what makes rebuilding state after a bug possible.

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 Kafka: Who uses Kafka?

The project reports use by more than 80% of the Fortune 100, with over 5 million lifetime downloads.

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.

Apache Kafka: Do I need to run Kafka myself?

No. Self-hosting is the operationally expensive option; managed services such as Confluent Cloud run the brokers for you and bill on throughput and storage instead.

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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