Softwr

Machine Learning · head to head

Keras vs VerneMQ

Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite
VerneMQ logo

VerneMQ

Databases

Erlang MQTT broker whose source is Apache 2.0 but whose official binaries need a paid subscription

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; VerneMQ the official binaries and Docker images are not Apache 2.0 but sit under a EULA requiring a yearly commercial subscription, a distinction easy to miss and awkward to discover during a licence audit.
  • They diverge on capability: Keras covers Sequential and Functional API, VerneMQ covers Erlang/OTP clustering.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Keras and VerneMQ actually diverge.

Attributes where Keras and VerneMQ differ
AttributeKerasVerneMQ
Pricing modelopen-sourcequote
PlatformsPython, Google Colab, JupyterLinux, Docker, macOS, Kubernetes
CategoryMachine LearningDatabases
Founded2015Unknown

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

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 Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

Only in VerneMQ

  • Erlang/OTP clustering
  • MQTT 5.0 support
  • Plugin system
  • Backpressure handling
  • Bridge support
  • Metrics export
  • MQTT over WebSockets
  • Pluggable auth backends

What people use each for

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

Keras

  • Machine learningnot VerneMQ
  • Data analysisnot VerneMQ
  • Model trainingnot VerneMQ
  • Predictive analyticsnot VerneMQ

VerneMQ

  • An industrial operator that wants an MQTT broker with predictable memory behaviour and no data integration features it will not usenot Keras
  • A team building from source to stay strictly under Apache 2.0 terms with no vendor licence entanglementnot Keras
  • A deployment needing custom authentication logic implemented as a plugin in Lua or over a webhooknot Keras
  • An organisation that wants a broker maintained by a small European company rather than by a vendor that keeps changing licencesnot Keras

Where each one falls short

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

Keras

  • Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • Error messages can be vague and unhelpful, making debugging challenging
  • Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch

VerneMQ

  • The official binaries and Docker images are not Apache 2.0 but sit under a EULA requiring a yearly commercial subscription, a distinction easy to miss and awkward to discover during a licence audit.
  • Octavo Labs is a very small company, so support depth, response times and the bus factor on the codebase are materially thinner than at HiveMQ or EMQ.
  • There is no data integration or rule engine layer, so routing messages into a database means writing and operating your own consumer service.
  • Operating an Erlang cluster requires runtime knowledge that most teams do not have and will use for nothing else in their stack.
  • There is no vendor-managed cloud offering, so every deployment is self-operated with the infrastructure and on-call cost that implies.

Pricing, plan by plan

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

VerneMQ

Free
  • Source buildFree
    • Apache 2.0 licensed source from GitHub
    • Full clustering and plugin capability
    • You compile and package it yourself
  • Binary packages and Docker images$undefined/year
    • Covered by the VerneMQ EULA, not Apache 2.0
    • Yearly usage subscription expected for commercial use
    • Official builds and Docker images
  • Commercial support$undefined/year
    • Evaluation, customisation and operations assistance
    • Custom development
    • Long-term maintenance agreements

Which should you pick?

Choose Keras if

  • You need sequential and functional api.
  • You want to start without paying.
  • You work on Python, Google Colab, Jupyter.
  • You also want pre-built neural network layers.
Keras review

Choose VerneMQ if

  • You need erlang/otp clustering.
  • You want to start without paying.
  • You work on Linux, Docker, macOS, Kubernetes.
  • You also want mqtt 5.0 support.
VerneMQ review

Questions people ask

Is Keras or VerneMQ better?
Neither clearly leads. Keras starts at Free and VerneMQ at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Keras or VerneMQ?
Keras starts at Free and VerneMQ at Free.
Does Keras or VerneMQ run on more platforms?
Keras runs on Python, Google Colab, Jupyter. VerneMQ runs on Linux, Docker, macOS, Kubernetes.
Can I use Keras for free?
Both have a free tier, so you can try either at no cost before committing.
What is Keras best used for?
Keras is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what VerneMQ is typically brought in for.
What can Keras do that VerneMQ cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. VerneMQ covers Erlang/OTP clustering, MQTT 5.0 support, Plugin system, Backpressure handling.

Answered from the vendors’ own pages

Keras: What is Keras?

Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.

Source
VerneMQ: Is VerneMQ free?

The source is Apache 2.0 and free. The official binary packages and Docker images are covered by a separate EULA that expects a yearly fee for commercial use.

Keras: What model architectures does Keras support?

Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.

Source
VerneMQ: Is the project still maintained?

Yes. Octavo Labs AG in Zurich continues to publish 2.x releases, most recently in 2026.

Keras: Can Keras models run on TPUs and GPUs?

Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.

Source
VerneMQ: Does it have a managed cloud?

No. Every deployment is self-hosted, with commercial support available from Octavo Labs.

Keras: Does Keras offer pre-trained models?

Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.

Source
VerneMQ: How does it compare to EMQX?

Narrower in features and without a rule engine, but with a simpler licence story for source builds after EMQX moved to BSL.

Keras: Who should use Keras?

Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.

Source
Share

Related pages

More on Keras

More on VerneMQ

Keep looking

Other head to heads