File Storage · head to head
Ceph vs MLflow
Ceph
File Storage
Open source distributed storage providing object, block and file from one cluster
- From
- Free
- Rated
- -
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ceph ceph assumes an operator who understands placement groups, CRUSH rules and recovery tuning, so organisations without dedicated storage staff routinely end up with a cluster that works until the first failure and then does not.; MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- They diverge on capability: Ceph covers RADOS object store, MLflow covers Experiment tracking.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Ceph 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 Ceph
- RADOS object store
- RADOS Gateway
- RBD block devices
- CephFS
- CRUSH placement
- Erasure coded pools
Only in MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
What people use each for
The jobs each tool is most often brought in to do.
Ceph
- Backing a private cloud where virtual machine disks, shared filesystems and an S3 endpoint all need the same hardwarenot MLflow
- Growing past the point where a proprietary array upgrade costs more than a rack of commodity serversnot MLflow
- Research and media environments with petabytes of data and staff who can operate storagenot MLflow
- Providing an S3 endpoint on premises with multi site replication under your own controlnot MLflow
MLflow
- Machine learningnot Ceph
- Data analysisnot Ceph
- Model trainingnot Ceph
- Predictive analyticsnot Ceph
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ceph
- Ceph assumes an operator who understands placement groups, CRUSH rules and recovery tuning, so organisations without dedicated storage staff routinely end up with a cluster that works until the first failure and then does not.
- Small clusters are inefficient: three way replication means a third of raw capacity is usable, and erasure coding needs enough failure domains to be safe, so the economics only work above a certain size.
- Recovery and rebalancing generate heavy internal traffic, so a failed disk can degrade client latency across the cluster unless backfill is throttled correctly beforehand.
- Upgrades must follow a strict daemon order across monitors, managers, OSDs and gateways, and a mistake in that order on a live cluster is difficult to reverse.
- Because it is self hosted, every byte served to the internet is transit you pay for on your own links, so the free licence does not mean free egress and bandwidth planning becomes your problem rather than the providers.
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
Ceph
Free- CephFree
- Full functionality, no capacity limit
- Object, block and file interfaces
- Community support via mailing list and Slack
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Which should you pick?
Choose Ceph if
- You need rados object store.
- You want to start without paying.
- You work on Linux.
- You also want rados gateway.
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 Ceph or MLflow better?
- Neither clearly leads. Ceph 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, Ceph or MLflow?
- Ceph starts at Free and MLflow at Free.
- Does Ceph or MLflow run on more platforms?
- Ceph runs on Linux. MLflow runs on Web, Python API, REST API.
- Can I use Ceph for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ceph best used for?
- Ceph is most often used for backing a private cloud where virtual machine disks, shared filesystems and an s3 endpoint all need the same hardware, growing past the point where a proprietary array upgrade costs more than a rack of commodity servers, research and media environments with petabytes of data and staff who can operate storage, providing an s3 endpoint on premises with multi site replication under your own control. Of those, backing a private cloud where virtual machine disks, shared filesystems and an s3 endpoint all need the same hardware and growing past the point where a proprietary array upgrade costs more than a rack of commodity servers are not what MLflow is typically brought in for.
- What can Ceph do that MLflow cannot?
- Ceph covers RADOS object store, RADOS Gateway, RBD block devices, CephFS. MLflow covers Experiment tracking, Model registry, Model packaging, Deployment.
Answered from the vendors’ own pages
Ceph: How many nodes do I need to start?
Three is the practical minimum for a replicated cluster with real fault tolerance, and most production advice starts at five once you account for maintenance windows.
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.
SourceCeph: Is it faster than a SAN?
Not on single stream latency. It wins on aggregate throughput and on growing without a forklift upgrade, which is a different property from raw speed.
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.
SourceCeph: Can I buy support?
Yes. IBM sells IBM Storage Ceph and SUSE and others have offered supported builds; the upstream project itself is free.
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.
SourceCeph: Should I use it just for S3?
If object is all you need, a dedicated object store is simpler to run. Ceph earns its complexity when you need block and file as well.
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.
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 Cloudian
- MLflow vs IDrive e2
- MLflow vs TrueNAS
- MLflow vs LucidLink
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- MLflow vs Sync.com
- 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