MooseFS is a general-purpose POSIX file system, which means the same cluster can serve very different jobs at the same time — hot virtual-machine disks on one mount, cold archives on another, an analytics data lake on a third. The grid below is a tour of the workloads our customers run today; pick the one that looks like yours and the rest of the page expands the detail.
Want the deeper technical view first? See Advantages & Features for everything MooseFS does, or Industries to see who’s running these workloads in production.
Whatever your workload, MooseFS has been running it in production since before the cloud was a thing.
The interactive grid above rotates through a representative slice. Below is the same set written out as a static reference — one card per workload, with two sentences of context for each.
Back VM disk images on a single, fault-tolerant POSIX namespace shared across every hypervisor in the cluster, so live migration and failover don’t depend on any one box. The same store can hold templates, snapshots and running images side by side, with hot-swappable hardware underneath.
Centralize backup storage with built-in redundancy, atomic snapshots and rapid recovery for business continuity. MooseFS is described on the old marketing site as “ideal for online backup solutions” precisely because snapshots are instantaneous and the underlying data is already spread redundantly across nodes.
Store petabytes of infrequently accessed data on commodity hardware with erasure coding (up to nine parity sums in Pro) to keep the cost per usable terabyte down. The built-in trash bin gives a soft-delete safety net, and storage classes let you keep cold copies on slower disks without changing the application.
Edit high-resolution video directly from the network, combining local NVMe speeds with datacenter capacity by spreading every file across many storage nodes. Post-production teams and VFX studios get parallel reads of the same project without dragging assets to local workstations.
Feed machine-learning jobs from a POSIX-compliant store that efficiently handles billions of small files — the regime that breaks naive object storage. Training, validation and evaluation sets sit in one namespace, so experiments can be reproduced from a single mount point.
Mount a single filesystem across an HPC cluster and eliminate the per-node copies and metadata bottlenecks that traditional shared storage runs into at scale. Parallel I/O across every chunkserver means more nodes equals more aggregate throughput, not just more contention.
Build private, multi-tenant file services with quotas, Unix ACLs and flexible replication rules per directory. Tenants get standard POSIX semantics from a single platform you operate yourself — no per-bucket pricing, no proprietary SDK.
Ingest continuous video streams from thousands of cameras with self-healing redundancy and predictable retention. Storage classes and quotas keep each retention window honest; failed drives are rebuilt automatically without operator action.
Store sequencing pipelines and reference genomes in a namespace that scales to exabytes, with snapshots that make reproducible analyses cheap. Statistical-genomics groups have been on MooseFS long enough to absorb the exponential growth of modern DNA work — see customers.
Host source art, builds and patch deltas for distributed teams with snapshot-based versioning at the filesystem level. Artists and engineers see the same files from any office; releases and rollbacks become a directory operation.
Provide persistent volumes for stateful Kubernetes workloads via the standard POSIX mount, no vendor-specific plug-in needed in the data path. Volumes survive pod rescheduling; the underlying cluster keeps replicating chunks across nodes regardless of where containers land.
Training jobs stream datasets in parallel from every storage node, with no manual sharding required upstream. Because MooseFS exposes a normal filesystem, the same dataloader works in a notebook on a laptop and on a multi-GPU box without code changes.
Store append-mostly metrics, traces and logs, keeping recent data hot for queries and older data warm for years. Storage classes let you push older partitions onto slower drives without touching the application, and snapshots make point-in-time investigations trivial.
Enable parallel reads across hundreds of compute nodes for big-data analytics, with the same data accessible to Spark, batch jobs and ad-hoc command-line tools through one POSIX mount. The cluster at Gemius has been running an analytics workload of this shape since 2005 — 6 PB of historical traffic data and 300,000 events per second, 24/7.
Version contracts, PDFs and ECM payloads at the filesystem level with snapshots and trash-bin recovery for secure document management. Unix permissions and ACLs give per-team isolation without an extra access-control layer.
Deduplicate layer blobs at the storage tier and serve every region from a unified tree with the same content guarantees. Pulls hit a parallel filesystem instead of competing for a single object-store node.
Store DICOM volumes and medical images next to the GPUs that process them, so radiology and ML pipelines work from the same files. Erasure coding keeps long-term retention affordable, and storage classes can pin recent studies on faster media.
Store build artifacts, logs and deployment packages for continuous-integration pipelines on a filesystem that scales horizontally as the pipeline graph grows. Snapshots give cheap rollback points for releases and bisection.
Aggregate and retain call records, signalling logs and subscriber data for telecom infrastructure and downstream analytics. Long retention windows are absorbed by erasure coding; ingest is parallel across all storage nodes.
The use cases above aren’t hypothetical — the customer stories page collects the public deployments behind them, organised by industry. If your workload doesn’t fit neatly into any of these labels, it’s almost certainly still something we’ve seen before; get in touch and we’ll come back with a written reply.
