
Ephemeral GPU capacity for AI/ML training and inference workloads — fitting buyers whose AI/ML pipelines need elastic GPU access without long-term commitment to hyperscaler GPU SKUs. Useful for buyers running training bursts, inference scale-out, or fine-tuning workloads with variable GPU demand.
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More from Storj
Globally distributed S3-compatible cloud object storage — fitting buyers whose object-storage workload is large, geographically distributed, or where hyperscaler-region concentration and egress economics become the bottleneck. Drop-in S3 API compatibility means existing S3 tooling, SDKs, and lifecycle policies port over directly.
Globally distributed offsite backup target for backup/archive workloads — fitting buyers whose backup volumes are large enough that hyperscaler-region cold-storage economics become the bottleneck, and who want offsite backup geographically distributed by architecture rather than configured per-region.
Object storage purpose-tuned for AI inference data alongside GPUs on Demand — fitting AI/ML operating models that want both inference compute and inference data storage under one operator rather than aggregating GPU compute and object storage across separate hyperscalers and regions.
Global data sharing across geographies on the same distributed network — fitting buyers whose data has multi-region access patterns (collaborators, partners, edge consumers) and who want geographic distribution as the architectural default rather than configured via cross-region replication tooling.