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Databases · head to head

Dgraph vs TensorBoard

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
T

TensorBoard

Machine Learning

Local visualisation for training runs, reading event files written to a directory

From
Free
Free plan
Yes
Visit TensorBoardFull review

The short version

  • TensorBoard is free; Dgraph starts at $39/mo.
  • Only Dgraph lists apache 2.0 licence; only TensorBoard lists scalar dashboards.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Dgraph and TensorBoard actually diverge.

Attributes where Dgraph and TensorBoard differ
AttributeDgraphTensorBoard
Pricing modelFreemiumOpen-source
PlatformsLinux, macOS, Self-hosted, WebWeb
CategoryDatabasesMachine Learning

Identical on both: starting price (Free), free tier (Yes).

Not yet recorded: founded for TensorBoard.

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 TensorBoard

  • Scalar dashboards
  • Run comparison
  • Graph visualisation
  • Histograms and distributions
  • Embedding projector
  • Image, audio and text panels
  • Hyperparameter view
  • Profiler

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

TensorBoard

  • Watching a training run in progress on a workstation or a remote box, to decide whether to stop it earlynot Dgraph
  • Diagnosing why a model is not learning, by looking at gradient and weight histograms rather than only the loss curvenot Dgraph
  • Profiling a slow training loop to find out whether the bottleneck is the data pipeline or the acceleratornot Dgraph
  • Working in an environment with no outbound network access, where a hosted tracking service is not an optionnot 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

TensorBoard

  • There is no authentication of any kind, so putting it on a shared host exposes every run, every metric and every logged sample image to anyone who can reach the port, and adding access control means building and maintaining a reverse proxy
  • Event files grow without limit and the interface loads runs into memory, so a directory holding hundreds of runs or a script logging scalars every step becomes slow to start and unpleasant to navigate well before the disk fills
  • It shows only what the training loop chose to write, so nothing connects a curve back to the code commit, the data set version or the environment unless the engineer logged those explicitly, which means the reproducibility problem is left entirely to you
  • TensorBoard.dev, the hosted service for sharing a run by link, was shut down at the end of 2023, so results now travel between colleagues as screenshots or through a deployment somebody on the team has to operate
  • Comparing runs is done by ticking boxes in a run list, which is fine for ten runs and useless for a thousand, and that threshold is precisely where teams start paying for MLflow, Weights and Biases or Neptune instead

Pricing, plan by plan

Dgraph

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

TensorBoard

Free

No published plan breakdown. See the TensorBoard review.

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 TensorBoard if

  • You need scalar dashboards.
  • You want to start without paying.
  • You also want run comparison.
TensorBoard review

Questions people ask

Is Dgraph or TensorBoard better?
Neither clearly leads. Dgraph starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Dgraph or TensorBoard?
Dgraph starts at Free and TensorBoard at Free.
Does Dgraph or TensorBoard run on more platforms?
Dgraph runs on Linux, macOS, Self-hosted, Web. TensorBoard runs on Web.
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 TensorBoard is typically brought in for.
What can Dgraph do that TensorBoard cannot?
Dgraph covers Apache 2.0 licence, Generated GraphQL API, DQL query language, Predicate sharding. TensorBoard covers Scalar dashboards, Run comparison, Graph visualisation, Histograms and distributions.

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.

TensorBoard: Does it work with PyTorch?

Yes. PyTorch includes a SummaryWriter that emits the same event file format, and Lightning wires it up by default. Nothing about the tool requires TensorFlow at run time.

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.

TensorBoard: Do I have to install TensorFlow to use it?

No. The tensorboard package installs on its own. Some plugins expect TensorFlow to be present, but the scalar, histogram and image dashboards do not.

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.

TensorBoard: Is it an experiment tracker?

No, and treating it as one is the common mistake. It visualises whatever a run wrote to disk. It does not store hyperparameters, code versions, artefacts or results in a way that survives someone deleting the log directory.

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.

TensorBoard: How do I share a dashboard with a colleague?

Host it yourself behind your own authentication, or send screenshots. The hosted sharing service was retired at the end of 2023.

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.

TensorBoard: What does it cost?

Nothing. It is Apache 2.0 licensed and runs on your own machine.

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