14 Most enterprises do not build on-prem AI infrastructure to make a statement. They build it because the training corpus is too sensitive to ship to a foreign tenancy, the egress on a single retraining pass is higher than a year of owned capacity, or the regulator wants the data plane operated under one jurisdiction. The question stops being whether to keep AI workloads in-house and becomes how to design the storage, network, and operational layers so a multi-petabyte AI program runs at GPU pace without breaking residency, recovery, or cost discipline. This guide is for infrastructure leaders, heads of AI infrastructure, and storage architects designing or expanding an on-prem AI program. It covers the layers a working design needs, the benefits that move the spend from optional to obvious, and the constraints that decide where the line between on-prem and everything else gets drawn. What is on-prem AI infrastructure? On-prem AI infrastructure is the set of compute, storage, network, identity, and operational layers that run the AI lifecycle — ingest, preparation, training, fine-tuning, inference, retrieval, checkpoints, and long-term retention — inside facilities the enterprise operates. The defining property is that placement, access, retention, and inspection of regulated data are enforced by infrastructure the enterprise controls, not by a contract with a public-cloud provider. The category is narrower than “private AI” — which can include sovereign-cloud and air-gapped variants — but broader than a single GPU cluster. A working on-prem AI design has to support the full lifecycle, not just the headline training run. Three properties separate a real design from a renamed GPU farm: Workload range. It has to serve GPU-direct training, high-concurrency inference, RAG and embeddings pipelines, model checkpoints, and decades of retention from one operational layer. Control at the data plane. Residency, identity-bound access, multi-tenant isolation, and immutability are properties of the storage platform, not assertions in policy slides. Independent scale. Compute and storage have to scale on their own clocks. AI workloads grow in both dimensions, rarely at the same rate. If any of these is missing, the design will hit a wall the first time the workload mix changes — which it always does. The design layers A working on-prem AI design has five layers. They are not independent — each one constrains the ones above and below it — but they are easier to reason about separately. 1. Compute layer The compute layer is the GPU and CPU envelope. For training, this is the dense GPU cluster — H-class or B-class accelerators interconnected with high-bandwidth fabric. For inference, it is a mix of GPU and CPU nodes sized for concurrency and latency rather than peak throughput. The split matters because training and inference rarely share the same hardware profile, and trying to force them onto one cluster wastes capacity on both sides. The compute layer also defines the power envelope. Modern training nodes draw enough power that facility planning — racks, cooling, PDU capacity — is part of the design from the first sketch, not a footnote at the end. 2. Storage layer The storage layer is where most of the operational pain actually lives. AI workloads do not have a single access pattern. Training reads large objects sequentially at extreme throughput. Inference reads small objects randomly under high concurrency. Embeddings, retrieval, and KV cache each behave differently again. Checkpoints write in bursts. Long-term retention sits cold for years. A working storage design uses tiered media under one operational model: GPU-direct flash for active training datasets, model checkpoints during a run, and KV cache for inference. Sub-millisecond latency at scale. Hot capacity (QLC, NL-SSD) for working datasets, recent inference logs, and embedding indexes — high throughput, lower cost per terabyte than premium flash. Warm capacity (NL-HDD, HDD) for the broader data lake, historical training corpora, and recent retention. Cold archival (tape, cloud-adjacent archival) for compliance retention, decommissioned models, and disaster recovery copies. The design point is that all four tiers sit under one namespace and one policy engine. Otherwise the team ends up operating four storage systems and writing migration scripts between them — which is exactly the operational drag the platform is supposed to remove. 3. Network layer The network layer in an on-prem AI design is two networks, not one. The east-west fabric — typically InfiniBand or high-speed Ethernet — carries GPU-to-GPU and GPU-to-storage traffic at the bandwidth modern training demands. The north-south network handles ingest, replication to retention sites, identity traffic, and operational telemetry. Mixing them on a single fabric saves cabling and loses throughput. For GPU-direct workloads, the storage-to-GPU path is the constraint. S3 over RDMA, GPUDirect Storage, and similar zero-copy paths exist to remove the host CPU and DRAM hop from the data path. See GPU-direct storage for how this layer is typically built. 4. Identity, access, and audit layer AI workloads in regulated environments demand identity-bound access at the data plane: every read, every write, every lifecycle event tied to an authenticated identity and recorded in a tamper-evident audit chain. This is where the difference between a generic on-prem cluster and a regulated-grade design shows up. The audit chain has to survive operational stress — training jobs that saturate the fabric, inference spikes that ten-times concurrency, multi-site replication catching up after a network event. Audit gaps under load are how accreditation reviews fail. 5. Operational layer The operational layer is what the team actually uses every day: provisioning, monitoring, lifecycle management, capacity planning, healing, upgrades, expansion. The design goal is that the team running the environment does not grow linearly with the footprint. That requires policy-driven automation, workload-aware insights, and a runtime that handles refresh cycles without forklift disruption. Key benefits The economic and operational case for keeping AI workloads in-house rests on four benefits. Each one is concrete enough to model — which is what separates them from generic “cloud is expensive” framing. Sovereignty and demonstrable control Sovereignty is the benefit that makes in-house operation non-optional for a growing list of workloads. When the dataset is classified, regulated, or under contractual residency rules, the only way to prove control is to operate the data plane inside the enterprise’s own legal and operational boundary. Public-cloud assertions of residency do not survive the disclosure-order test for organizations subject to extraterritorial regimes. The benefit is not just “data stays inside the country.” It is that placement, access, retention, and inspection are enforced in code rather than asserted in contracts. See cloud data sovereignty and sovereign cloud storage for the deeper legal and architectural picture. Predictable cost at sustained scale Public-cloud per-hour pricing is attractive for short experiments and breaks down for sustained AI workloads. Three cost lines compound: premium storage, egress, and reserved-instance commitments that look cheaper than on-demand but lock in capacity the workload may not stay on. Once active training data crosses two to three petabytes — or the workload runs continuously enough that reserved capacity beats per-hour pricing — the unit economics turn. The cloud repatriation strategy discussion covers the inflection point in more detail. Owned infrastructure trades the variable-cost model for a predictable capital plan. The benefit is not always lower total cost in year one. It is a cost curve that does not bend the wrong way as the program grows. Data gravity advantage Data at petabyte scale does not move easily. If the training corpus already lives inside the enterprise — telemetry from industrial systems, claims data, imaging archives, transactional history — the cost-effective AI deployment runs where the data is, not where the GPUs are easiest to rent. Continuously shipping multi-petabyte datasets to a public cloud and back is the design that turns a healthy AI program into a recurring egress bill. The data gravity problem write-up covers the dynamics in detail. The benefit is positional. Compute placed next to the data the program already produces eliminates a class of cost and a class of risk that no amount of cloud architecture can fully neutralize. Operational control without vendor lock-in The fourth benefit is harder to model but shows up every time the program needs to change. A platform built on open standards — S3-compatible object storage, open compute hardware, standard identity protocols — gives the enterprise the ability to refresh hardware, swap operational tooling, and adopt new accelerator generations without re-platforming the data layer. Public-cloud AI services tie the data plane to a provider’s APIs, formats, and contractual terms. Switching costs grow with every terabyte of accumulated training data. The benefit is freedom of motion through what is going to be a long, fast-moving hardware cycle. Constraints that shape the design Five constraints decide what the design actually looks like in practice. Workload mix. A program that is 90% training and 10% inference designs differently from one that is 30% training, 50% inference, and 20% RAG. The storage tier mix and the network fabric follow the workload shape. Data residency. Where the data must legally reside, who can access it, and which jurisdiction’s laws apply are first-class design inputs. Multi-region enterprises often run a multi-site deployment with regional autonomy under a unified operational view. Power envelope. Modern GPU density is a facility design problem before it is a procurement problem. Total power, cooling capacity, and PDU layout decide how much accelerator capacity can be deployed in a given room. Refresh cycle realism. GPU generations turn over every 12 to 24 months. Storage generations turn over more slowly. The design should let the team refresh compute without refreshing storage, and vice versa. Disaggregated architectures — see distributed object storage — are how this is typically achieved. Operational headcount. Most enterprises building this kind of platform cannot scale headcount linearly with footprint. Automation, policy-driven lifecycle, and a runtime that surfaces operational insights rather than dumping raw telemetry are the difference between a team that scales and one that drowns. Why this points toward autonomous data infrastructure An on-prem AI deployment exercises every layer at machine pace under workload conditions that change without warning. Three properties have to hold at once: Performance at scale — fast enough not to bottleneck GPUs during training and high-concurrency inference, with every object placed on the right media for its access pattern. Control at the data plane — placement, access, retention, and operator boundaries enforced by the platform, not asserted in a runbook. Operational economy — the team running the platform does not grow linearly with the petabyte count or the workload variety. Traditional storage architectures handle one or two of these and break on the third. A more autonomous data infrastructure — one where the storage layer enforces placement, surfaces operational insights under policy, and aligns the right media to each workload without manual intervention — is what makes all three feasible together. Scality ADI (Autonomous Data Infrastructure) is built around that premise, with Sovereign Control as the centerpiece pillar for on-prem AI workloads. How Scality ADI applies Scality ADI is data infrastructure for enterprise AI, cyber resilience, and sovereign control that autonomously and sustainably aligns the right storage media at multi-petabyte to exabyte scale. The platform deploys on-premises, in a sovereign region, inside a private cloud, or in an air-gapped enclave — and presents a single namespace across them. For the design layers above specifically: Storage layer. Scality ADI spans four tiers — GPU-Direct flash with S3 over RDMA, hot QLC and NL-SSD, warm HDD, and cold tape and cloud-adjacent archival — under one operational model. Training, fine-tuning, inference cache, embeddings, checkpoints, and long-term retention each get the tier they need without forcing the team to operate four separate storage systems. Compute and network alignment. Scality ADI runs on the RING10 disaggregated architecture, which scales capacity, throughput, and operations independently. GPU-direct workloads use S3 over RDMA at sub-50-microsecond latency at the GPU-Direct tier and multi-TB/s throughput at the Hot tier. Identity, access, and audit. CORE5 cyber resilience — immutability, erasure-coded durability, metadata protection, multi-site protection, and policy enforcement — makes the retention and audit story hold up under scrutiny. Air-gapped operation is supported for the most sensitive enclaves. Operational layer. Guardian agents surface workload-aligned operational insights and recommendations within customer-defined policy. The team approves what to act on; the platform executes inside defined bounds. The benefit shows up as fewer repetitive tasks and fewer disruptive maintenance events, not as a black box that runs without supervision. Scality ADI is delivered as open-code software, available as a software appliance or managed-service model. Open code matters for regulated environments: agency security teams, accreditors, and red teams can inspect what the platform does instead of relying on vendor attestation. Scality gives enterprises and sovereign organizations a way to pursue AI-scale performance without giving up control, resilience, or long-term economic discipline. For infrastructure leaders designing a program that has to survive the next refresh cycle, a unified data layer is the part that does not get re-platformed every two years. See how Scality ADI delivers on-prem AI infrastructure at scale Frequently asked questions What is on-prem AI infrastructure? On-prem AI infrastructure is the set of compute, storage, network, identity, and operational layers running the AI lifecycle inside facilities the enterprise operates. It enforces placement, access, retention, and inspection of regulated data at the data plane, supports training and inference workloads at GPU pace, and scales capacity and compute independently as the program grows. What are the main benefits? Four benefits anchor the case: demonstrable sovereignty and control over the data plane, predictable cost at sustained scale once the workload runs continuously or the dataset crosses multi-petabyte volumes, data-gravity advantage when training corpora already live inside the enterprise, and operational freedom of motion through hardware refresh cycles without vendor lock-in. When does on-prem make more sense than public cloud for AI? On-prem typically wins when active training data exceeds two to three petabytes, when egress charges become a material line item, when classification or residency requirements rule out shared tenancy, or when the workload runs continuously enough that reserved capacity beats per-hour pricing. Air-gapped variants apply to the most sensitive classified or regulated enclaves. How is this different from a private cloud? Private cloud refers to a consumption model — multi-tenant, self-service, API-driven — that can run on-prem or in a sovereign region. The phrase here refers to where the workload physically runs. The two overlap: many on-prem deployments are operated as private clouds, but on-prem designs are also valid in single-tenant, classified, or air-gapped configurations that do not look like a private cloud at all. How does Scality ADI fit? Scality ADI provides the data-plane layer: a single namespace across GPU-direct flash, hot capacity, warm capacity, and cold archival, with policy-based placement, S3 over RDMA for GPU workloads, CORE5 cyber resilience for retention and recovery, and Guardian operational insights under customer-defined policy. It deploys on-premises or air-gapped, scales from a handful of nodes to exabyte scale on the RING10 architecture, and ships as open-code software for inspectability in regulated environments. Further reading Enterprise private AI deployment models: a practical guide AI deployment architecture: models and patterns Top on-premises object storage On-premise vs off-premise Cloud repatriation strategy Data gravity problem GPU-direct storage Sovereign cloud storage Cloud data sovereignty Hybrid cloud data strategy for AI workloads Distributed object storage
