Machine Learning · head to head
Ray vs VerneMQ
VerneMQ
Databases
Erlang MQTT broker whose source is Apache 2.0 but whose official binaries need a paid subscription
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; 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: Ray covers Distributed computing, VerneMQ covers Erlang/OTP clustering.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Ray and VerneMQ actually diverge.
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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- TensorFlow
- Hugging Face
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.
Ray
- Distributed AI model training and servingnot VerneMQ
- Large-scale data processingnot VerneMQ
- Reinforcement learning workloadsnot VerneMQ
- ML inference servingnot VerneMQ
VerneMQ
- An industrial operator that wants an MQTT broker with predictable memory behaviour and no data integration features it will not usenot Ray
- A team building from source to stay strictly under Apache 2.0 terms with no vendor licence entanglementnot Ray
- A deployment needing custom authentication logic implemented as a plugin in Lua or over a webhooknot Ray
- An organisation that wants a broker maintained by a small European company rather than by a vendor that keeps changing licencesnot Ray
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
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
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
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 Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
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.
Questions people ask
- Is Ray or VerneMQ better?
- Neither clearly leads. Ray 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, Ray or VerneMQ?
- Ray starts at Free and VerneMQ at Free.
- Does Ray or VerneMQ run on more platforms?
- Ray runs on Linux, Mac, Windows. VerneMQ runs on Linux, Docker, macOS, Kubernetes.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributed ai model training and serving, large-scale data processing, reinforcement learning workloads, ml inference serving. Of those, distributed ai model training and serving and large-scale data processing are not what VerneMQ is typically brought in for.
- What can Ray do that VerneMQ cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. VerneMQ covers Erlang/OTP clustering, MQTT 5.0 support, Plugin system, Backpressure handling.
Answered from the vendors’ own pages
Ray: Is Ray free?
Yes. Ray is free and open source software with over 34,800 GitHub stars and 1,000+ contributors. Users can download and use the Ray framework at no cost.
SourceVerneMQ: 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.
Ray: Is there a paid option for Ray?
Yes. Anyscale, the managed platform built by Ray's creators, offers paid tiers with enterprise features like governance and advanced tooling. Specific Anyscale pricing details are not listed on the Ray website.
SourceVerneMQ: Is the project still maintained?
Yes. Octavo Labs AG in Zurich continues to publish 2.x releases, most recently in 2026.
Ray: Can I try Ray with credits?
Yes. New users can try Ray with $100 credit on Anyscale's managed platform to explore the service.
SourceVerneMQ: Does it have a managed cloud?
No. Every deployment is self-hosted, with commercial support available from Octavo Labs.
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.
Related pages
Other head to heads
- Ray vs Google Vertex AI
- Ray vs AWS SageMaker
- Ray vs Azure Machine Learning
- Ray vs DataRobot
- Ray vs Milvus
- Ray vs Pinecone
- Ray vs H2O.ai
- Ray vs Dask
- Ray vs Apache Spark MLlib
- Ray vs Weaviate
- Ray vs TensorFlow
- Ray vs LangChain
- Ray vs Dataiku
- Ray vs KNIME
- Ray vs Palantir Foundry
- Ray vs Python
- Ray vs EMQX
- Ray vs RabbitMQ
- Ray vs NATS
- Ray vs TIBCO Enterprise Message Service
- Ray vs Canary Labs
- Ray vs Solace PubSub+
- Ray vs TimescaleDB
- Ray vs Apache Pulsar
- Ray vs Presto
- Ray vs StarRocks
- Ray vs Timeplus
- Ray vs PostgreSQL
- Ray vs Chroma
- Ray vs Cloudinary
- Ray vs Convex
- Ray vs Dgraph
- Ray vs Dragonfly
- Ray vs Dremio
- VerneMQ vs Google Vertex AI
- VerneMQ vs AWS SageMaker
- VerneMQ vs Azure Machine Learning
- VerneMQ vs DataRobot
- VerneMQ vs Milvus
- VerneMQ vs Pinecone
- VerneMQ vs H2O.ai
- VerneMQ vs Dask
- VerneMQ vs Apache Spark MLlib
- VerneMQ vs Weaviate
- VerneMQ vs TensorFlow
- VerneMQ vs LangChain
- VerneMQ vs Dataiku
- VerneMQ vs KNIME
- VerneMQ vs Palantir Foundry
- VerneMQ vs Python
- VerneMQ vs EMQX
- VerneMQ vs RabbitMQ
- VerneMQ vs NATS
- VerneMQ vs TIBCO Enterprise Message Service
- VerneMQ vs Canary Labs
- VerneMQ vs Solace PubSub+
- VerneMQ vs TimescaleDB
- VerneMQ vs Apache Pulsar
- VerneMQ vs Presto
- VerneMQ vs StarRocks
- VerneMQ vs Timeplus
- VerneMQ vs PostgreSQL
- VerneMQ vs Chroma
- VerneMQ vs Cloudinary
- VerneMQ vs Convex
- VerneMQ vs Dgraph
- VerneMQ vs Dragonfly
- VerneMQ vs Dremio