ServiceNow Validates AI Cloud Parity With Azure Ultra Disk Over On Prem NVMe

ServiceNow’s claim that it has achieved “AI cloud parity” with on‑premises NVMe using Microsoft Azure Ultra Disk Storage marks a notable moment in the enterprise AI era: a major SaaS platform says managed cloud block storage now meets the throughput, tail‑latency and availability demands previously reserved for direct‑attached NVMe, and that this parity has real operational consequences—faster regional rollouts, simpler hardware refresh cycles, and a cleaner path to global SaaS scale. ServiceNow’s case study with Azure describes a three‑month validation against their Dell NVMe baseline, deployment of Ultra Disk across 14 Azure regions, and measurable operational changes such as reducing new‑region provisioning from months to roughly six weeks.

Background / Overview​

ServiceNow has been re‑architecting its platform around an “AI‑first” posture—introducing RaptorDB (a PostgreSQL‑based HTAP engine), AI Control Tower, Agent Fabric, and tightly coupled data fabrics to feed agents and low‑latency services. Those changes make the underlying storage substrate a core determinant of user experience and automated agent behavior: inconsistent I/O or long latency tails can cascade into slow agents, missed SLAs, and degraded automation outcomes. To address that, ServiceNow validated Azure Ultra Disk Storage and select Azure VM SKUs (notably E‑series, M‑series and FX‑series families) against on‑prem Dell NVMe systems and reports parity for production database workloads including MariaDB and its proprietary Raptor implementation.
This move is both technical and strategic. Technically, it asks whether a managed, virtualized block device can deliver the microsecond/sub‑millisecond tail latencies, high random IOPS, and sustained throughput required by HTAP databases and agentic AI. Strategically, it signals a shift in how major SaaS vendors think about capital vs. operating spend, regional expansion cadence, and hyperscaler partnerships.

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What Azure Ultra Disk actually offers — verified technical specs​

Azure Ultra Disk is Microsoft’s high‑end managed block storage tier designed for latency‑sensitive, high‑IOPS workloads. Recent Microsoft documentation lists configurable IOPS and throughput limits that scale with disk size, with very high ceilings for large disks. For the largest Ultra Disks, Microsoft’s published table shows provisionable IOPS up to 400,000 IOPS per disk and 10,000 MB/s throughput per disk for very large disk sizes, and it states Ultra Disks are designed to provide their configured IOPS and throughput 99.99% of the time. These are device‑level performance availability and provisioning guarantees rather than a VM‑or‑region uptime SLA. Microsoft’s platform updates and blog posts have also described complementary platform features—“Azure Boost” VM types and memory‑optimized instances with expanded performance envelopes—that enable Ultra Disk to be paired with VM SKUs capable of consuming very high I/O. In short: the raw numbers ServiceNow cites are consistent with Microsoft’s published Ultra Disk performance envelope as of the most recent public documentation and product blogs. Key Ultra Disk characteristics IT teams should note:

• Provisionable IOPS per disk: thousands to hundreds of thousands depending on disk size and VM limits.
• Throughput per disk: up to 10,000 MB/s (10 GB/s) in Microsoft’s documented tables for large sizes.
• Configurable performance at runtime: Ultra Disk allows performance resizes (with operational constraints) without detaching disks in many cases.
• Performance availability expectations: Microsoft’s documentation describes Ultra Disk performance as designed to deliver provisioned IOPS/throughput 99.99% of the time, which is a performance reliability metric, distinct from VM connectivity SLA.

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What ServiceNow tested and claims it achieved​

ServiceNow’s validation program reportedly included a three‑month benchmarking and load‑simulation exercise comparing Azure Ultra Disk against its own on‑prem Dell NVMe baseline. The benchmarks included simulated load, CPU/memory telemetry, and latency analytics focused on MariaDB and RaptorDB workloads. After validation, ServiceNow reported:

• Performance parity with their on‑prem NVMe systems for the key database workloads powering AI features.
• Consistency of user‑facing performance for customers migrating to ServiceNow’s SaaS on Azure.
• Operational acceleration: Ultra Disk was deployed across 14 Azure regions and ServiceNow reduced typical region implementation time from roughly a month to about six weeks.

These are vendor–case‑study level claims and should be read in that context: the tests were run by ServiceNow against configurations they selected, and they reflect the outcomes of a carefully staged program tailored to their workloads. That makes the results highly useful as a real‑world datapoint—but not a universal guarantee for every workload or environment.

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Why storage parity matters for AI‑first SaaS​

Modern HTAP and agentic AI workloads stress storage in three principal ways:

• Tail latency matters: Agents, UI interactions, and transactional systems all depend on predictably low tails. Long or variable latencies create user‑visible lag and can cause automated workflows to time out.
• Mixed I/O profiles: Transactional HTAP workloads combine high random IOPS with periodic sustained bandwidth needs (analytics, model checkpoints). Storage must handle mixed workloads without tail‑latency degradation.
• Global scale and regional availability: SaaS businesses must provide regional presence for latency, compliance and data residency—managed, multi‑region storage that behaves predictably simplifies expansion.

ServiceNow’s validation suggests that, for at least one large, real‑world HTAP/agent workload, Azure Ultra Disk can satisfy these constraints when combined with suitable VM SKUs and careful configuration. That lowers the barrier to moving mission‑critical AI workloads to managed cloud primitives—if the results are reproducible in other contexts.

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Strengths demonstrated by the case study​

• Operational agility: Shifting from long hardware procurement cycles to managed disk provisioning enables faster region rollouts and simpler hardware refresh. ServiceNow frames this as a mindset shift away from buying and holding physical gear for years.
• Elasticity and manageability: Ultra Disk’s ability to change IOPS/throughput profiles (within limits) means ServiceNow can tune performance as workloads evolve without hardware swaps. Microsoft docs confirm runtime performance adjustments are supported with operational caveats.
• Managed SLAs and multi‑region footprint: Running on a hyperscaler simplifies disaster recovery, snapshot management, and region provisioning compared with operating dozens of privately owned datacenters. ServiceNow’s 14‑region deployment is an operational signal that the approach can scale geographically.
• Vendor engineering leverage: Partnering with Azure gives ServiceNow access to ongoing platform innovations (VM types, network advances, Azure Boost) that can increase available I/O without forklift hardware changes. Microsoft has documented VM and storage improvements that raise performance ceilings.

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Risks, caveats and what remains unproven​

• Vendor‑validated benchmarks need independent replication. Case studies are valuable but often reflect tuned configurations. Any buyer should require proof‑of‑value using production‑representative workloads and third‑party or internal benchmarking rigs before committing to a wholesale migration. ServiceNow’s three‑month validation is a positive signal, but it is specific to their workload and deployment choices.
• Cost discipline is essential. Ultra Disk is provisioned and billed by size, IOPS and throughput. High‑IOPS/throughput configurations can be expensive relative to on‑prem CAPEX amortized across years. Without aggressive storage governance and tiering, operational costs can rise quickly. Microsoft’s pricing pages and documentation note the multi‑dimensional billing model that drives this sensitivity.
• Tail latency under contention. HTAP workloads require I/O isolation and careful allocation of disks to avoid noisy‑neighbor effects. While Ultra Disk provides performance guarantees at the disk level, end‑to‑end tail latency can still suffer if VMs, network, or adjacent services are overloaded. Real‑world multi‑tenant contention and region‑level resource pressure can produce variability not captured in lab runs.
• SLA semantics and what “99.99%” refers to. Microsoft’s documentation that describes Ultra Disk delivering provisioned performance 99.99% of the time is about the performance profile of the disk. It is different from VM connectivity or region uptime SLAs. Organizations must understand which guarantee protects which failure mode (disk performance vs VM connectivity vs regional outage). Microsoft’s SLA pages and disk docs make these distinctions explicit.
• Provider coupling and exit planning. Deep attachment to managed storage shapes architecture and recovery patterns. Buyers should bake in multi‑region resilience, cross‑cloud exit plans, and tested portability strategies if vendor independence is a strategic requirement. ServiceNow’s hybrid stance—retaining some Dell NVMe—illustrates a pragmatic guarded approach.
• Feature parity is not uniform across regions and VM families. Ultra Disk’s maximal performance often depends on pairing with specific VM families that expose the necessary IOPS/throughput caps. For extreme configurations, Azure may require “Boost” VM SKUs or latest generation instances; availability of those SKUs can vary by region. Microsoft’s blog and product notes stress the need to match VM and disk capabilities.

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Practical guidance: how enterprise IT and Windows teams should evaluate claims of parity​

• Run a production‑representative pilot. Emulate real concurrency, transactional mixes, background analytics, and agent traffic. Measure tail latencies (p99.9, p99.99), time‑to‑query, and end‑to‑end UX impact. Use the same query and feature mix your users exercise in production.
• Test across VM families and availability zones. Align disk performance tests with the exact VM SKU catalogue you plan to use. Validate that Azure supports the required VM/disk SKU combination in the target regions.
• Measure cost at scale. Model the TCO for a full‑region deployment including provisioned IOPS/throughput, snapshot/backup costs, inter‑region replication, and potential egress. Compare that to on‑prem amortized costs plus staff/operational overhead.
• Keep a hybrid fallback during ramp. Maintain on‑prem NVMe capacity during an initial migration window to provide an escape hatch if tail‑latency or cost tradeoffs emerge. ServiceNow’s hybrid approach shows this pragmatism in action.
• Define DR and portability runbooks. Test failover, disaster recovery, and data egress to ensure recovery time objectives (RTOs) and recovery point objectives (RPOs) are achievable in practice—not just on paper.
• Negotiate operational commitments. For highly regulated or SLA‑sensitive workloads, include contractual commitments, audit rights, performance baselines and remedies for when performance targets or provisioning windows aren’t met.

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Checklist for Windows/IT teams evaluating Ultra Disk for AI/HTAP workloads​

• Confirm the specific Ultra Disk size and provisioned IOPS/throughput needed for your workload.
• Validate the VM series compatibility in target regions (E, M, FX or Azure Boost types).
• Pilot with real queries and agent workflows; measure p50/p95/p99/p99.9/p99.99.
• Model monthly TCO at full scale, including snapshots, backup, replication and monitoring.
• Keep a hybrid rollback plan with on‑prem NVMe during the migration window.
• Demand operational proofs (independent benchmarking, audit reports) from the hyperscaler or platform partner where possible.

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Market context — why hyperscalers and storage matters now​

The AI era has shifted where value concentrates in the stack. Hyperscalers are not just selling raw compute; they are packaging compute, networking, storage and management primitives that make it operationally easier to run AI at scale. Storage has re‑emerged as a first‑class problem: inference latency, database responsiveness, and agent throughput all surface as storage‑bound constraints. AWS, Google Cloud and Azure are racing to expand the managed storage envelopes, remove bandwidth or throttling limits, and expose VM families capable of consuming massive IOPS budgets. ServiceNow’s partnership with Azure follows similar moves by other vendors that seek cloud parity without sacrificing performance. Microsoft’s documentation and product blogs show continuous investments raising Ultra Disk ceilings and pairing them with new VM types and “Boost” features to unlock higher IOPS/throughput.

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Bottom line — a pragmatic verdict​

ServiceNow’s public case study is an important datapoint: a major SaaS platform validated Azure Ultra Disk against its on‑prem NVMe baseline and reports production readiness for AI and HTAP workloads across multiple regions. That validation aligns with Microsoft’s published Ultra Disk performance envelopes and platform upgrades that raise I/O ceilings.
However, the claim of “parity” should be interpreted carefully:

• For organizations with similar workload shapes and the willingness to invest in rigorous validation, managed cloud block storage can be a credible foundation for latency‑sensitive AI and HTAP platforms.
• For others, the safe path is to pilot with production‑representative workloads, model costs accurately, and preserve a hybrid fallback while validating real p99/p99.9 tail latencies and multi‑region resilience.

The practical implication for Windows and enterprise IT teams is clear: managed Ultra Disk now deserves a seat at the evaluation table for mission‑critical AI platforms—but treat vendor case studies as starting points, not final proofs. Rigorous benchmarking, architecture planning, cost governance, and multi‑region resilience remain essential to translate a vendor’s “parity” claim into reliable production reality.

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Source: SDxCentral ServiceNow claims AI cloud parity with on-prem on Azure