File Storage · head to head
Duplicati vs MLflow
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Duplicati no managed service or commercial support; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Duplicati covers AES-256 encryption, MLflow covers Experiment tracking.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Duplicati and MLflow 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 Duplicati
- AES-256 encryption
- Incremental backup
- Deduplication
- Multiple cloud backends
- Compression
- Web interface
- AWS S3
- Azure
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
Both cover
- Windows support
- Mac support
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
Duplicati
- Data protectionnot MLflow
- Disaster recoverynot MLflow
- Business continuitynot MLflow
- Ransomware protectionnot MLflow
- Compliancenot MLflow
MLflow
- Machine learningnot Duplicati
- Data analysisnot Duplicati
- Model trainingnot Duplicati
- Predictive analyticsnot Duplicati
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Duplicati
- No managed service or commercial support
- Relies on community support
- No enterprise features
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
Pricing, plan by plan
Duplicati
FreeNo published plan breakdown. See the Duplicati review.
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Duplicati if
- You need aes-256 encryption.
- You want to start without paying.
- You work on Windows, macOS, Linux.
- You also want incremental backup.
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.
Questions people ask
- Is Duplicati or MLflow better?
- Neither clearly leads. Duplicati starts at Free and MLflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Duplicati or MLflow?
- Duplicati starts at Free and MLflow at Free.
- Does Duplicati or MLflow run on more platforms?
- Duplicati runs on Windows, macOS, Linux. MLflow runs on Web, Python API, REST API.
- Can I use Duplicati for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Duplicati best used for?
- Duplicati is most often used for data protection, disaster recovery, business continuity, ransomware protection. Of those, data protection and disaster recovery are not what MLflow is typically brought in for.
- What can Duplicati do that MLflow cannot?
- Duplicati covers AES-256 encryption, Incremental backup, Deduplication, Multiple cloud backends. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Both handle Windows support, Mac support, Linux support.
Answered from the vendors’ own pages
Duplicati: Is Duplicati free?
Yes. Duplicati is completely free and open-source under the LGPL license. There are no premium tiers, trials, or limitations.
SourceMLflow: 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.
SourceDuplicati: What does Duplicati support?
Duplicati supports zero-trust, fully encrypted backups to local storage, network drives, and cloud services. It includes deduplication and incremental backups.
SourceMLflow: 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.
SourceDuplicati: What are the platforms?
Duplicati runs on Windows, macOS, and Linux. It can back up data to local storage, network drives, or cloud providers.
SourceMLflow: 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.
SourceMLflow: 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.
SourceMLflow: 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.
SourceRelated pages
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- MLflow vs Nextcloud
- MLflow vs pCloud
- MLflow vs Sync.com
- MLflow vs Tresorit
- MLflow vs Backblaze
- MLflow vs Cloudian
- MLflow vs LucidLink
- MLflow vs Storj
- MLflow vs Microsoft OneDrive
- MLflow vs Zerto
- MLflow vs IDrive e2
- MLflow vs Panzura
- MLflow vs Comet ML
- MLflow vs Weights & Biases
- MLflow vs Neptune.ai
- MLflow vs ClearML
- MLflow vs DVC
- MLflow vs Kubeflow
- MLflow vs BentoML
- MLflow vs AWS SageMaker
- MLflow vs DataRobot
- MLflow vs Seldon
- MLflow vs Azure Machine Learning
- MLflow vs Dataiku
- MLflow vs Palantir Foundry
- MLflow vs Pinecone
- MLflow vs Python
- MLflow vs PyTorch
- MLflow vs scikit-learn
- MLflow vs Apache Spark MLlib