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Databases · head to head

Dgraph vs OpenSearch

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
OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • They diverge on capability: Dgraph covers Apache 2.0 licence, OpenSearch covers Full-text search.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Dgraph and OpenSearch actually diverge.

Attributes where Dgraph and OpenSearch differ
AttributeDgraphOpenSearch
Pricing modelfreemiumOpen source, no licence fee; managed services billed separately
PlatformsLinux, Mac, Docker, WebLinux, Docker, Kubernetes, Self-hosted
Founded2016Unknown

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 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

Only in OpenSearch

  • Full-text search
  • OpenSearch Dashboards
  • Log analytics
  • Vector search

What people use each for

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

Dgraph

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

OpenSearch

  • Log and observability storage where an Apache-2.0 licence is a requirementnot Dgraph
  • Replacing Elasticsearch after the licence change without changing architecturenot Dgraph
  • Search plus analytics on one cluster rather than two systemsnot Dgraph

Where each one falls short

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

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.

OpenSearch

  • Diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • Operationally heavy in the way Elasticsearch is: cluster sizing, shard strategy and JVM tuning are ongoing work
  • Smaller ecosystem of third-party tooling than Elasticsearch, which most integrations still target first
  • Overkill for plain application search, where a dedicated search engine is far simpler

Pricing, plan by plan

Dgraph

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

OpenSearch

Free
  • OpenSearchFree
    • Full functionality
    • Self-hosted
    • No usage limits

Which should you pick?

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

Choose OpenSearch if

  • You need full-text search.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want opensearch dashboards.
OpenSearch review

Questions people ask

Is Dgraph or OpenSearch better?
Neither clearly leads. Dgraph starts at Free and OpenSearch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dgraph or OpenSearch?
Dgraph starts at Free and OpenSearch at Free.
Does Dgraph or OpenSearch run on more platforms?
Dgraph runs on Linux, Mac, Docker, Web. OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted.
Can I use Dgraph for free?
Both have a free tier, so you can try either at no cost before committing.
What is Dgraph best used for?
Dgraph is most often used for an application whose core data is a graph, such as permissions, social connections or product relationships, where the frontend already consumes graphql, a knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiation, recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottleneck, teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available one. Of those, an application whose core data is a graph, such as permissions, social connections or product relationships, where the frontend already consumes graphql and a knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiation are not what OpenSearch is typically brought in for.
What can Dgraph do that OpenSearch cannot?
Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding. OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics, Vector search.

Answered from the vendors’ own pages

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.

OpenSearch: Is OpenSearch free?

Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.

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.

OpenSearch: Why does OpenSearch exist?

Elastic moved Elasticsearch off the Apache 2.0 licence in 2021. AWS forked the last Apache-licensed version, and the project now sits under the Linux Foundation.

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.

OpenSearch: Is OpenSearch compatible with Elasticsearch?

It was at the 7.10 fork point. Both have developed independently since, so compatibility weakens with every release and should be verified for the features you use.

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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