Azure-Only RAG AI Delivers Latency Wins and Lower TCO, PT Study

A new Principled Technologies (PT) study circulating as a press release this week argues that deploying a retrieval‑augmented generation (RAG) AI application entirely on Microsoft Azure — instead of splitting model hosting and search/compute across providers — can materially improve latency, simplify governance, and reduce multi‑year costs for many common enterprise GenAI workloads. The study’s headline numbers are eye‑catching: PT reports roughly a 59.7% reduction in end‑to‑end execution time and up to an 88.8% reduction in search‑layer latency when the full stack runs on Azure (Azure OpenAI + Azure AI Search + Azure compute) versus a mixed deployment that used Azure OpenAI models but hosted other components on AWS (Amazon Kendra + AWS compute). These results add fresh, empirical ammunition to an intensifying debate inside enterprise IT: when does single‑cloud consolidation for AI make sense — and when does multi‑cloud remain the safer, more strategic choice?

Background / Overview​

Principled Technologies is a long‑standing independent testing and benchmarking firm that runs hands‑on comparisons of cloud and on‑prem platforms. In the PT scenario, the team built a simple RAG pipeline and executed it in two topologies:

• A mixed, multi‑cloud deployment that used Azure OpenAI (GPT‑4o mini) for model inference but hosted search and other components on AWS (using Amazon Kendra for retrieval).
• A single‑cloud deployment that ran the whole stack on Microsoft Azure — Azure OpenAI, Azure AI Search, and Azure compute/storage.

PT measured key operational metrics (end‑to‑end latency, search query latency, token throughput) and modeled a three‑year Total Cost of Ownership (TCO) using defined utilization and discount assumptions. The press summary concludes that, for the tested configuration, the Azure‑only configuration produced both faster responses and better cost predictability — outcomes PT ties to collocation, integrated tooling, and elimination of cross‑provider data egress and operational complexity.
The proof‑point is straightforward and, at a conceptual level, uncontroversial: placing tightly coupled components — vector retrieval, model inference, and GPUs/VMs — within the same cloud region and vendor often reduces network hops, avoids egress charges, and simplifies authentication and policy management. The crucial questions for practitioners are: how broadly do PT’s numbers generalize, what assumptions underlie the TCO model, and what architectural or business trade‑offs should teams weigh before moving a fleet of AI workloads to a single vendor?

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What PT tested — a closer look at the experiment​

The architecture under test​

PT’s experiment used a canonical RAG flow: ingest documents into a managed search/index, embed and store vectors, query the index for relevant passages, then call a cloud LLM to synthesize answers. Key tech elements in the two deployments:

• Single‑cloud (Azure):
• Azure OpenAI models (GPT‑4o mini).
• Azure AI Search as the retrieval service.
• Azure GPU‑backed VMs and Azure storage for data.
• Mixed deployment (Azure + AWS):
• Azure OpenAI for the model calls.
• Amazon Kendra for retrieval and indexing.
• AWS compute/storage for hosting components other than the model.

Both tests used roughly equivalent service tiers and model choices (the study references GPT‑4o mini for inference). Measurements included the user request → model response latency (end‑to‑end), the time spent at the search/retrieval layer, and tokens produced per second.

Headline measurements​

• ~59.7% reduction in end‑to‑end execution time for the Azure‑only deployment in PT’s tests.
• Up to 88.8% faster search‑layer performance for Azure AI Search versus Amazon Kendra in the tested configuration.
• PT also reported a more predictable TCO when the stack was consolidated into Azure, driven by consolidated billing and the ability to leverage committed discounts for sustained GPU usage.

These are PT’s reported outcomes for the specific RAG workload, service SKUs, regions, and utilization assumptions they selected.

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Technical validation — do the platform claims hold up?​

To evaluate PT’s conclusions, it’s necessary to separate technical mechanics that are well understood from the precise numeric deltas that are configuration‑specific.

Why collocation often helps​

• Data gravity and reduced network hops: When retrieval storage, vector databases, and model inference live in the same cloud region, there are fewer network transitions, less cross‑cloud routing, and lower latency windows for RPCs and HTTP calls. This is a general networking reality, not specific to one vendor.
• Egress and billing effects: Cross‑cloud traffic commonly incurs egress charges and may add measurable latency, especially for large payloads or synchronous flows (for example, if a retrieval returns long context and the model call is not local).
• Integrated service optimizations: Clouds that integrate model routing, search, and identity in a single control plane can streamline authentication, logging, policy enforcement, and observability — reducing operational overhead and potential for misconfiguration.

These mechanics are corroborated by platform documentation and vendor materials: Azure publishes purpose‑built AI infrastructure, integrated model catalogs (Azure AI Foundry), and dedicated, low‑latency GPU families that target inference workloads. AWS documents the design and capabilities of Amazon Kendra as an enterprise semantic search service and provides guidance on capacity planning and query scaling. Both vendor platforms are entirely capable; the advantage PT measured is largely about where components run and how they interconnect.

On reported performance deltas: configuration matters​

PT’s percent reductions are plausible in a real test where the mixed configuration introduced extra network hops, cross‑cloud serialization, and differing service latencies. But the magnitude of the gain — ~60% end‑to‑end and ~89% search layer — is sensitive to many variables:

• Region pairings: If the Azure OpenAI models and Kendra instances are in different physical regions, cross‑region latency could dominate. Conversely, if both clouds had service endpoints in the same metro region and low‑latency interconnects were used, deltas may shrink.
• VM and GPU SKUs: Different VM families and GPU bins deliver dramatically different token‑per‑second performance; mixing modern Azure GPU SKUs versus older AWS instances (or vice versa) changes results.
• Index size and query complexity: Retrieval latency scales with index size, index type, and scoring complexity. Small synthetic corpora will show different deltas than real enterprise document sets.
• Concurrency and throttling: Throughput under load depends on configured capacity units, autoscaling behavior, and rate limits at provider APIs.
• Commercial discounts and sustained use commitments: TCO calculations depend heavily on negotiated rates, committed use discounts, and actual utilization curves.

Bottom line: the technical rationale behind PT’s result is sound; the numeric outcomes are test‑envelope specific and must be revalidated for any real production workload.

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Cost and TCO: predictable savings or modeling artifacts?​

PT models a three‑year TCO and argues that single‑cloud consolidation yields more predictable costs and, under PT’s utilization assumptions, a lower TCO. That can be true — but only when the following conditions are present:

• Sustained GPU usage that justifies committed‑use discounts (reservations or savings plans).
• Stable throughput expectations that match reserved capacity; otherwise, pay‑as‑you‑go spikes can negate savings.
• Minimized cross‑cloud egress and operational overhead (fewer subscriptions, unified observability, fewer engineers to manage cloud connectors).

However, TCO is notoriously sensitive to assumptions. Small changes to concurrency profiles, egress volumes, or discount negotiations can flip outcomes. Independent analyses and practitioner guidance recommend reconstructing any third‑party TCO model against internal telemetry and negotiated pricing before making binding procurement choices.

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Governance, compliance, and operational simplicity​

PT emphasizes governance benefits of a single control plane: centralized identity, unified audit trails, and consolidated policy enforcement. Those benefits are real:

• Azure’s integrated identity and policy tooling (Azure AD, role‑based access control, policy as code) can reduce cross‑cloud IAM friction.
• A single vendor reduces the number of compliance attestations to manage and makes automated policy enforcement simpler to apply enterprise‑wide.
• Observability and incident response are simpler when telemetry flows into a single cloud monitoring stack.

But single‑cloud consolidation concentrates responsibility and risk. Organizations must balance simplified governance against vendor dependency, and they should formalize exit and migration runbooks as an integral part of any consolidation decision.

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Risks and caveats — what PT’s press summary underplays​

• Vendor lock‑in risk: Centralizing core AI workloads with one cloud increases the friction and cost to move later. Preserve portability for critical workloads or maintain export/runbooks.
• Resilience and business continuity: Multi‑cloud architectures are sometimes chosen to increase resilience; single‑cloud consolidations should be paired with geo‑redundancy and robust DR planning.
• Negotiation and pricing sensitivity: TCO advantages often presuppose favorable discounts. Organizations without leverage or sustained usage patterns may see smaller or no savings.
• Feature tradeoffs: Amazon Kendra and Azure AI Search have different feature sets and ingestion/connectors; Kendra offers rich built‑in connectors and certain enterprise search capabilities that may be required for some use cases.
• Security assumptions: Centralizing can simplify some controls but magnifies impact of a single misconfiguration. Strong identity‑first design and least privilege controls remain essential.
• Workload diversity: Some workloads (specialty ML tools, data sovereignty, partner ecosystems) may still be best placed outside a single cloud.

PT itself is careful to couch numerical claims as test‑specific; engineering teams should treat the report as hypothesis‑forming rather than definitive procurement guidance.

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When single‑cloud AI on Azure makes the most sense​

Consolidating AI workloads onto Azure is most likely to deliver the benefits PT describes when several conditions line up:

• The organization already has a large Microsoft footprint (Office 365, Azure AD, Microsoft 365, Dynamics) and strong commercial incentives to deepen Azure usage.
• Workloads are latency‑sensitive and the RAG flow is chatty — frequent retrievals and synchronous model calls amplify cross‑cloud penalties.
• GPU usage is sustained and predictable, enabling committed discounts and reservations to lower unit costs.
• Data residency and compliance profiles allow centralization within Azure regions.
• The application relies heavily on features or integrations that are more mature or performant in the Azure stack (e.g., data lake integration, certain Azure cognitive services).

In those scenarios, PT’s recommendation — pilot an all‑Azure topology, instrument, and measure — is pragmatic and aligns with sound engineering practice.

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When to preserve a multi‑cloud strategy​

Multi‑cloud remains the sensible choice when:

• Regulatory or sovereignty requirements mandate using specific clouds or on‑prem locations.
• Organizations must avoid increased vendor dependency for strategic or procurement reasons.
• Specialized services or tools exist only (or better) on another provider and migration costs outweigh the performance benefits of collocation.
• Usage is highly spiky and unpredictable, making reserved discounts less effective.
• Teams prioritize global resilience via provider diversity.

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A pragmatic validation checklist for IT leaders​

Before you adopt PT’s Azure‑only recommendation wholesale, run a short, repeatable validation program. Use this checklist:

• Instrument and baseline
• Capture current end‑to‑end latency, retrieval latency, tokens/sec, egress volumes, and concurrency for representative workloads.
• Recreate PT’s topology
• Build a minimal RAG pilot that mirrors your production dataset, connectors, and SLAs, then deploy it in both single‑cloud and mixed configurations.
• Measure under realistic load
• Run tests across multiple time windows, with concurrency and query mix that reflect real traffic.
• Recompute TCO with your negotiated pricing
• Replace public list prices with your organization’s discounts, reserved pricing, and committed usage plans. Run sensitivity analysis.
• Assess governance and risk
• Map compliance requirements, exit routes, and disaster recovery for a single‑cloud plan. Create migration/export runbooks.
• Evaluate functional gaps
• Confirm search features, connectors, analytics, and relevancy tuning available in the chosen cloud meet business needs.
• Reassess every 6–12 months
• Cloud features, model economics, and regional availability evolve quickly — make the decision periodic, not permanent.

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Practical engineering recommendations​

• Use asynchronous or batched retrieval where possible to reduce the impact of transient latency spikes.
• Cache high‑value embeddings or partial results when the freshness window allows, reducing repeated cross‑service calls.
• Apply policy‑as‑code and automated compliance checks from day one if consolidating to reduce misconfiguration risk.
• Maintain data export utilities and automated backups to avoid operational lock‑in.
• Test failover scenarios: simulate losing the primary cloud region or service and measure recovery time objectives (RTOs) and recovery point objectives (RPOs).

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Strengths and limitations of PT’s study​

Strengths:

• PT performed reproducible hands‑on tests using a real RAG architecture, not just synthetic microbenchmarks.
• The study highlights real operational levers: collocation, egress reduction, and simplified governance.
• It provides a concise, actionable hypothesis for teams to pilot.

Limitations:

• Results are tightly coupled to test configuration, service SKUs, region choices, and utilization assumptions.
• Press materials summarize findings without fully exposing the raw test corpus, code, and exact provisioning details necessary for absolute replication.
• Commercial and contractual differences (customer discounts, reserved instances, marketplace pricing) can materially alter TCO outcomes for other organizations.

Because PT’s numerical claims are contingent, treat the study as a launching point for localized testing rather than a plug‑and‑play procurement justification.

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Conclusion​

The PT study adds a practical, empirically grounded voice to an important enterprise architecture debate. The technical logic — collocating retrieval, models, and compute to reduce latency and operational complexity — is sound and aligns with platform documentation and practitioner experience. For organizations that already live heavily in the Microsoft ecosystem, run latency‑sensitive RAG workloads, and can commit to sustained GPU usage, the Azure‑only approach described by PT can be a sensible and defensible strategy.
But the decision maps to business context and trade‑offs: negotiated pricing, regulatory constraints, functional search features, and resilience goals can all favor a mixed or multi‑cloud approach. The prudent course for IT leaders is clear: pilot the topology that PT highlights, instrument everything, rebuild the TCO with internal pricing and utilization, and bake migration/exit plans into any long‑term strategy. That disciplined approach preserves the potential performance and cost benefits PT observed while limiting the operational and strategic risks that come with deeper platform consolidation.

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Source: Louisiana First News PT study shows that using a single-cloud approach for AI on Microsoft Azure can deliver benefits