Machine Learning · head to head
MLflow vs Rowy
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
Rowy
Spreadsheets
Spreadsheet interface for Google Cloud Firestore with cloud functions written in the browser
- From
- Free
- Rated
- -
The short version
- Each has a real cost: MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves; Rowy every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
- They diverge on capability: MLflow covers Experiment tracking, Rowy covers Firestore backed grid.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which MLflow and Rowy 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 MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
Only in Rowy
- Firestore backed grid
- Browser authored cloud functions
- Typed columns
- Runs in your Google Cloud project
- Role based access
- Form view
- Open source
What people use each for
The jobs each tool is most often brought in to do.
MLflow
- Machine learningnot Rowy
- Data analysisnot Rowy
- Model trainingnot Rowy
- Predictive analyticsnot Rowy
Rowy
- A Firebase application that needs an internal admin interface without building onenot MLflow
- Operations staff correcting production records in Firestore without developer involvementnot MLflow
- Attaching a small transformation or notification function to a field change without a full deployment pipelinenot MLflow
- A content team populating documents that a mobile application reads directly from Firestorenot MLflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
Rowy
- Every Firestore limit is inherited, including the one megabyte per document ceiling and roughly one sustained write per second per document, which surprises users who expect spreadsheet behaviour.
- Firestore has no joins, so linking tables in the grid is a convenience layer rather than a relational feature and reports across collections still need a separate query.
- It is useful only to teams already on Firebase, so choosing it effectively locks the operational tooling to one cloud vendor.
- Query costs are billed by document read, and an unfiltered grid on a large collection can generate a bill that a spreadsheet user has no intuition for.
- Development activity has slowed relative to the broader category, so evaluate current maintenance before making it load bearing for internal operations.
Pricing, plan by plan
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Rowy
Free- Open source self hostedFree
- No licence fee
- Deploys into your own Google Cloud project
- You pay Google Cloud for Firestore reads, writes and storage
Which should you pick?
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
Choose Rowy if
- You need firestore backed grid.
- You want to start without paying.
- You also want browser authored cloud functions.
Questions people ask
- Is MLflow or Rowy better?
- Neither clearly leads. MLflow starts at Free and Rowy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, MLflow or Rowy?
- MLflow starts at Free and Rowy at Free.
- Does MLflow or Rowy run on more platforms?
- MLflow runs on Web, Python API, REST API. Rowy runs on Web.
- Can I use MLflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is MLflow best used for?
- MLflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Rowy is typically brought in for.
- What can MLflow do that Rowy cannot?
- MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Rowy covers Firestore backed grid, Browser authored cloud functions, Typed columns, Runs in your Google Cloud project.
Answered from the vendors’ own pages
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
SourceRowy: Where is my data stored?
In your own Firestore instance inside your own Google Cloud project. Rowy does not hold a copy.
MLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
SourceRowy: What does it cost to run?
No licence fee. You pay Google Cloud for Firestore reads, writes, storage and function invocations, and an open grid on a big collection reads a lot.
MLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
SourceRowy: Can it do joins across collections?
Not really. Firestore does not support joins, and no interface layer can add them.
MLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
SourceRowy: Is there a row limit?
Not from Rowy. The limits that bite are Firestore document size and per document write throughput.
MLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceRowy: Is it suitable if I am not on Firebase?
No. It is specifically a Firestore interface.
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