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Databases · head to head

Dgraph vs OpenAI API

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
Free plan
Yes
Visit DgraphFull review
OpenAI API logo

OpenAI API

Machine Learning

Hosted API for OpenAI's language, embedding, image and audio models, billed per token

From
$0.15/per-million-tokens
Free plan
No
Visit OpenAI APIFull review

The short version

  • Dgraph has a free plan, then $39/mo; OpenAI API starts at $0.15/mo.
  • Only Dgraph lists apache 2.0 licence; only OpenAI API lists speech and audio.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Dgraph and OpenAI API actually diverge.

Attributes where Dgraph and OpenAI API differ
AttributeDgraphOpenAI API
Starting priceFree$0.15/per-million-tokens
Pricing modelFreemiumUsage-based
Free tierYesNo
PlatformsLinux, macOS, Self-hosted, WebAPI
CategoryDatabasesMachine Learning
Founded20162015

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 OpenAI API

  • Text and reasoning models
  • Embeddings
  • Speech and audio
  • Image generation
  • Function calling
  • Structured outputs
  • Batch processing
  • Prompt caching

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 OpenAI API
  • A knowledge graph that outgrew a single machine and needs storage distributed across nodes without a per-core licence negotiationnot OpenAI API
  • Recommendation or fraud-detection features that traverse several hops at request time, where a relational join chain has become the bottlenecknot OpenAI API
  • Teams that want a graph database they can read, fork and self-host under a permissive licence rather than a source-available onenot OpenAI API

OpenAI API

  • Adding summarisation, drafting or classification to an existing product where building a model would take longer than the product's whole roadmapnot Dgraph
  • Retrieval-augmented question answering over internal documents, using the embedding and generation models togethernot Dgraph
  • Extracting structured records from unstructured text, where schema-constrained output removes most of the parsing problemnot Dgraph
  • Prototyping a language feature quickly to find out whether it is worth the cost of a self-hosted alternative laternot 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

OpenAI API

  • Cost scales with tokens rather than with seats, so a successful feature's bill grows with its adoption, and an interface that lets users paste long documents has no natural ceiling on spend unless you build one yourself
  • Models are deprecated on the vendor's timetable, and a fine-tuned model built on a retired base goes with it, so the tuning work and the data curation behind it must be redone rather than migrated
  • Behaviour shifts between model versions in ways no test catches unless you wrote one, so prompts tuned over months against a particular snapshot can regress quietly on migration, which makes an evaluation suite a prerequisite rather than an improvement
  • It cannot run inside your own network, so data residency requirements, air-gapped environments and contracts forbidding third-party processing rule it out regardless of the provider's own security posture
  • You inherit its availability and its rate limits, so a provider incident is an outage in your product and a traffic spike can be throttled at precisely the moment the feature is proving itself

Pricing, plan by plan

Dgraph

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

OpenAI API

$0.15/per-million-tokens
  • GPT-4o mini$0.15
    • Fast
    • Affordable
  • GPT-4o$5
    • Multimodal
    • 128K context

Which should you pick?

Choose Dgraph if

  • You need apache 2.0 licence.
  • You want to start without paying.
  • You work on Linux, macOS, Self-hosted, Web.
  • You also want generated graphql api.
Dgraph review

Choose OpenAI API if

  • You need text and reasoning models.
  • You work on API.
  • You also want embeddings.
OpenAI API review

Questions people ask

Is Dgraph or OpenAI API better?
Neither clearly leads. Dgraph starts at Free and OpenAI API at $0.15/per-million-tokens, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dgraph or OpenAI API?
Dgraph has a free tier; the other does not. Paid plans start at Free for Dgraph and $0.15/per-million-tokens for OpenAI API.
Does Dgraph or OpenAI API run on more platforms?
Dgraph runs on Linux, macOS, Self-hosted, Web. OpenAI API runs on API.
Can I use Dgraph for free?
Yes. Dgraph has a free tier, so you can try it without paying. OpenAI API starts at $0.15/per-million-tokens.
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 OpenAI API is typically brought in for.
What can Dgraph do that OpenAI API cannot?
Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding. OpenAI API covers Text and reasoning models, Embeddings, Speech and audio, Image generation.

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.

OpenAI API: Is my data used to train the models?

API inputs and outputs are not used for training by default, which differs from the consumer product. Retention periods and enterprise terms change, so read the current data usage policy rather than trusting a summary.

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.

OpenAI API: Can I run these models on my own hardware?

No. The weights are not distributed. If self-hosting is a requirement, you are looking at open-weight models instead, with the operational and quality trade-offs that implies.

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.

OpenAI API: How is it priced?

Per token, with input and output priced differently and each model priced differently. Batch processing and cached input prefixes reduce it. The practical consequence is that your bill is a function of prompt design, not just of request count.

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.

OpenAI API: What is the difference from Azure OpenAI Service?

The same model family delivered by Microsoft under an Azure contract, with Azure identity, networking and regional controls, and a different release cadence for new models. Enterprises with an Azure agreement often choose it for procurement and data residency reasons rather than technical ones.

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.

OpenAI API: How do I keep the cost under control?

Cap input length, cache repeated prefixes, route easy requests to smaller models, use the batch path where latency does not matter, and set per-user limits before launch rather than after the first surprising invoice.

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