When a 1,000-GPU training job crashes after 12 hours, every second spent waiting for checkpoint writes feels like a small eternity. For AI teams running large-scale training workloads, the storage backend that handles those checkpoints is not just a plumbing detail — it directly determines how quickly the cluster recovers from preemption, node failure, or planned maintenance. In 2025, two software-defined storage (SDS) platforms dominate the conversation for on-premises AI infrastructure: VMware vSAN and Ceph. Both promise scalable, distributed storage, but they take fundamentally different approaches to data placement, consistency, and checkpoint I/O. This comparison unpacks the architectural differences and provides concrete guidance for choosing the right backend when checkpoint latency is the primary metric.
AI training frameworks like PyTorch with DDP or DeepSpeed write large, sequential checkpoint files (typically 5–50 GB per checkpoint) to a shared filesystem. The storage backend must absorb high-bandwidth writes while maintaining data integrity across concurrent GPU nodes. vSAN uses a log-structured, object-based approach with a write-back cache: all writes land first on a fast NVMe cache tier, then destage to a capacity tier (typically SSDs or HDDs). This design prioritizes low-latency writes at the cost of requiring careful cache sizing. Ceph, by contrast, distributes data using the CRUSH (Controlled Replication Under Scalable Hashing) algorithm, placing object replicas across OSDs (Object Storage Daemons) without a dedicated cache tier. Writes go directly to the primary OSD, which then replicates to secondary OSDs synchronously. This synchronous replication ensures strong consistency but adds tail latency, especially under mixed workloads. For checkpoint writes that must complete on all nodes before training resumes, the latency profile of each backend becomes a bottleneck multiplier.
vSAN's log-structured merger reduces write amplification for large sequential writes common in checkpoints. Each checkpoint file is broken into 4 MB object components, written sequentially, then merged during idle periods. Ceph's CRUSH placement often scatters object replicas across different OSDs, which can increase write amplification for large files because each replica involves separate write operations to different physical drives. Ceph's BlueStore backend mitigates this with compression and checksumming, but the fundamental write penalty for multi-replica writes remains higher than vSAN's cache-coalesced approach in most configurations.
AI checkpointing requires crash consistency: if a node dies mid-write, the checkpoint file must either be fully written or fully discarded. vSAN defaults to a quorum-based model with synchronous acknowledgments from at least 2 of 3 replicas (in RAID-1 mirroring). This means a write is confirmed as soon as the majority of replicas land in the cache tier. Ceph, using RADOS (Reliable Autonomic Distributed Object Store), requires all replicas to acknowledge a write before confirming to the client. While this gives stronger guarantees, it increases the P99 latency for checkpoint writes, particularly when network latency between OSDs is variable. For a training job checkpointing every 10 minutes, a 50 ms increase in per-write latency translates to roughly 30 seconds of additional checkpoint time per cycle across a 512-node cluster writing concurrently. Over a 48-hour training run, that can add 2+ hours of cumulative checkpoint overhead — time the GPUs are idling.
In cloud-native environments using Kubernetes with preemptible GPU instances, unscheduled preemption is common. vSAN's object-level checksums and journaling allow it to recover from partial writes by replaying the journal, typically completing in under a second. Ceph requires scrubbing the affected PG (Placement Group) and potentially repairing inconsistent objects, which can take 10–30 seconds per affected OSD. For clusters with frequent preemption, this recovery overhead can compound significantly.
To ground the comparison, consider a controlled benchmark from a 2024 internal evaluation at a mid-sized AI lab (published in their engineering blog). A homogeneous cluster of 32 storage nodes, each with 4x 7.68 TB NVMe drives, connected via 100 Gbps RoCEv2 networking, served 256 A100 GPUs training a 70B-parameter LLM. Checkpoints were 35 GB each, written via Lustre running on top of each SDS backend.
vSAN scales linearly up to roughly 32 nodes in a single cluster (the maximum supported per cluster as of vSAN 8 U3). Beyond that, teams must federate multiple vSAN clusters, which adds cross-cluster latency for checkpoint data access if a training job spans clusters. Ceph scales to hundreds of OSD nodes without architectural changes, making it the default choice for large-scale deployments. However, Ceph's performance under high write concurrency degrades when CRUSH map updates are frequent (e.g., during node addition or failure). Each CRUSH recalculation triggers data migration that competes with checkpoint I/O. For a 128-node Ceph cluster losing 4 OSDs simultaneously, the recovery I/O can consume 30–40% of write bandwidth for several minutes, directly stalling GPU checkpoints. vSAN's fault domains (e.g., rack or chassis awareness) allow it to localize recovery traffic, reducing interference with active workloads.
vSAN's management is tightly integrated with vCenter, offering a straightforward GUI for monitoring disk health, rebuild progress, and performance. Ceph relies on the Cephadm CLI and dashboard, which has a steeper learning curve. For an AI team that already has VMware expertise, vSAN reduces the risk of misconfiguration (e.g., improper CRUSH map settings causing uneven data distribution). For teams with dedicated storage engineers, Ceph's flexibility (erasure coding, tiered storage policies) can yield better storage efficiency — though rarely better latency.
vSAN requires per-socket licensing (vSAN Advanced or Enterprise). For 32 nodes with dual sockets, Advanced licensing adds roughly $6,000 per socket, totaling ~$384,000 in licensing costs. Ceph is open-source (LGPL) with no licensing fees, though production support from Red Hat (Red Hat Ceph Storage) costs ~$400 per node per year. Over three years, vSAN licensing costs roughly 4x more than Ceph with Red Hat support for a 32-node cluster. The trade-off: vSAN's lower latency and operational simplicity can reduce training idle time by 5–10%, potentially saving more in GPU time than the licensing premium. For a cluster running 256 A100 GPUs at $3 per GPU-hour (cloud-equivalent cost), saving 2 hours of idle time per week equals ~$1,536 saved weekly — which recoups the vSAN license cost in under 6 months.
Both vSAN and Ceph are sensitive to network latency and packet loss, but in different ways. vSAN's cache tier operations benefit from low-latency RDMA (Remote Direct Memory Access) for destaging, requiring lossless RoCEv2 or InfiniBand. In a cluster with standard RoCEv2 (no DCQCN tuning), vSAN can suffer from PFC (Priority Flow Control) pauses that spike write latency to over 100 ms under congestion. Ceph's synchronous replication is more forgiving of standard TCP/IP because it does not rely on cache tier operations, but it requires consistent bandwidth - any OSD with a slower network link becomes a bottleneck for all replicas stored on it. For AI teams already running InfiniBand for GPU communication, using a separate Ethernet network for storage adds complexity. vSAN can run over the same InfiniBand fabric using NVMe over Fabrics (NVMe-oF), reducing network cost. Ceph with InfiniBand requires the rdma-core libraries, which are stable but less commonly deployed than Ethernet-based Ceph.
Before committing to either backend, spin up a small-scale test cluster (8 nodes minimum) and run a checkpoint stress test that mirrors your actual training workload. Use the fio tool with workloads that match your framework's write pattern: large sequential writes (block size 64 MB or larger) with fsync at the end of each checkpoint file to simulate crash consistency requirements. Collect P99 latency and total checkpoint time across 50 repeated cycles. Pay special attention to write behavior during concurrent node failures (simulate by killing an OSD or vSAN disk). The winning backend is the one that gives you recoverable checkpoints in the shortest window, because every second saved is a second of GPU compute recovered.
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