Principled Technologies’ recent hands‑on evaluation argues that running an end‑to‑end Retrieval‑Augmented Generation (RAG) stack entirely on Microsoft Azure — instead of splitting model hosting, retrieval, and compute across multiple clouds — can produce measurable improvements in latency, simplify governance, and deliver more predictable multi‑year costs for many enterprise GenAI workloads.
Background / Overview
Principled Technologies (PT) built a canonical RAG pipeline twice: once as an Azure‑only deployment (Azure OpenAI + Azure AI Search + Azure GPU VMs and managed storage) and once as a mixed deployment that used Azure OpenAI for the model but routed retrieval and supporting compute to AWS (Amazon Kendra + AWS compute/storage). PT measured end‑to‑end latency, retrieval/search latency, per‑token throughput, and modeled a three‑year Total Cost of Ownership (TCO) using defined utilization and discount assumptions.The study’s headline numbers are attention‑grabbing: PT reports approximately a 59.7% reduction in end‑to‑end execution time and up to an 88.8% reduction in search‑layer latency for the all‑Azure topology versus the mixed deployment in the specific test envelope PT used. PT pairs these measurements with modeled TCO outcomes that favor consolidation in many of their scenarios, while repeatedly cautioning that the exact deltas depend on chosen SKUs, regions, dataset sizes, concurrency profiles, and negotiated discounts.
This article explains what PT actually tested, verifies the technical mechanics that make the results plausible, highlights strengths and caveats, and provides a rigorous, workload‑level playbook for Windows‑centric IT teams deciding whether to pilot a single‑cloud Azure approach for AI.
What PT tested — experiment, topology, and metrics
Architecture under test
PT implemented a standard RAG flow that included document ingestion and chunking, embedding generation, vector storage / managed search indexing, retrieval of relevant passages, and LLM synthesis of answers. The two topologies compared were:- Single‑cloud (Azure): Azure OpenAI (GPT‑4o mini) for inference, Azure AI Search for retrieval and indexing, Azure GPU‑backed VMs and Blob/managed storage for data and compute.
- Mixed deployment (Azure + AWS): Azure OpenAI for model calls, Amazon Kendra for retrieval/indexing, and AWS compute/storage for other components.
Measurement methodology and caveats
PT’s measurements were controlled and configuration‑specific. That means results are valid and replicable for the precise SKUs, region topologies, dataset sizes, concurrency profiles, and discount assumptions PT declared — but they are not universal. The authors explicitly recommend that organizations re‑run the experiments or rebuild the TCO model using internal telemetry and negotiated pricing before making procurement decisions.The key findings — what the numbers say (and what they don’t)
Headline outcomes
PT’s press materials report two primary performance deltas for the tested RAG workload:- ~59.7% reduction in end‑to‑end execution time when the entire stack ran on Azure versus the mixed deployment.
- Up to 88.8% reduction in search‑layer latency when using Azure AI Search compared with Amazon Kendra in the configuration PT tested.
Interpreting the numbers responsibly
Those percentages are test envelope outcomes — not universal guarantees. The mechanics that produce the improvements are well understood (collocation, reduced egress, fewer control planes), but the magnitude of gains is tightly tied to the test configuration. PT states this repeatedly and frames the report as an empirical hypothesis IT teams should validate internally. Treat the reported deltas as plausible outcomes for similar workloads and configurations, not as a one‑size‑fits‑all mandate.Why a single‑cloud deployment can outperform a mixed topology: the technical mechanics
Data gravity and network hops
When vector indexes, storage, and inference compute exist within the same cloud region and vendor, the system avoids repeated cross‑cloud hops and egress paths that add latency and cost. This data gravity effect is mechanical: fewer network transitions equals lower round‑trip times and reduced variability. PT’s latency improvements align with this foundational reality.Purpose‑built GPU SKUs and host‑to‑GPU interconnects
Azure publishes GPU‑optimized VM families (ND‑class with H100, NC/A100 variants) that deliver high‑bandwidth host‑to‑GPU interconnects and NVLink‑style topologies. These SKUs materially impact throughput and latency for inference and training workloads. PT’s performance claims rely on using modern Azure GPU SKUs, which plausibly produce the throughput and latency improvements seen when workloads are collocated in the same cloud and region.Integrated managed services and a single control plane
Azure’s managed services (Blob Storage, Azure AI Search, Azure OpenAI Service, Microsoft Entra, Microsoft Purview, Defender) create a single identity and governance plane. That reduces integration code, authentication complexity, and operational friction — all of which shorten time‑to‑value for MLOps teams and improve observability. PT’s operational simplicity claim rests on this reduced engineering surface area.Strengths of PT’s study — what it shows well
- Empirical, repeatable test design: PT documents a reproducible RAG topology and declares SKUs and regions; that makes the test actionable for teams who want to replicate it.
- Clear demonstration of data gravity effects: The measured latency reductions are consistent with expected gains when retrieval and compute are collocated.
- Practical TCO modeling: PT translates observed metrics into multi‑year costs with explicit assumptions, making it possible to swap in organization‑specific numbers.
- Balanced framing: PT repeatedly warns readers that numbers are configuration‑sensitive and encourages re‑running the experiments with local inputs. That explicit caution strengthens the study’s practical value.
Real risks and limitations — what consolidation does not solve
Vendor lock‑in and exit costs
Consolidating compute, storage, embeddings, and model hosting in one vendor increases the friction and cost of moving workloads later. PT’s TCO models include sensitivity analyses showing how migration and one‑time exit costs can flip decision math; procurement teams must include those figures before signing long commitments.Resilience and provider diversity
Relying on a single cloud increases operational exposure to vendor outages, regional failures, or policy changes. Multi‑cloud or hybrid patterns offer resilience via distribution and enable use of best‑of‑breed services where required.Functional gaps and feature parity
Not every managed service has feature parity across clouds. A retrieval service on one vendor might offer functionalities — relevance tuning, semantic ranking, or enterprise connectors — that differ materially. PT tested Amazon Kendra versus Azure AI Search in one configuration; results could flip for datasets or search demands that favor Kendra’s features. Treat functional requirements as first‑class constraints.Pricing volatility and negotiated discounts
Headline cloud prices are only part of the picture. Volume discounts, committed use agreements, and negotiated rates matter. PT models this explicitly; small changes to utilization, burstiness, or discount structure materially change multi‑year outcomes. Organizations must rebuild TCO with internal commitments and negotiated pricing.Regulatory and data sovereignty constraints
Where law or policy requires data residency or on‑prem processing, Azure’s hybrid offerings (Azure Arc, Azure Local) can mitigate but may not fully replicate the economics and simplicity of a pure cloud deployment. PT acknowledges hybrid needs and frames Azure’s hybrid tooling as a complementary option rather than a universal fix.A practical validation playbook for Windows IT teams
The PT study is best used as a template to guide a short, rigorous validation program. Below is a step‑by‑step, actionable plan to test the single‑cloud hypothesis for a real workload.- Inventory and classify workloads
- Tag AI workloads by data gravity, latency sensitivity, compliance needs, and business criticality.
- Prioritize one high‑value, latency‑sensitive RAG workload for the pilot.
- Rebuild PT’s TCO spreadsheet with internal inputs
- Include GPU hours (training + inference), storage IOPS, egress, network costs, committed discounts, and one‑time migration/exit fees.
- Run sensitivity scenarios (±20–50% utilization, burst events, egress spikes).
- Match SKUs and regions for parity testing
- In the pilot, use Azure GPU SKUs equivalent to PT’s (e.g., ND H100 or A100 variants) and choose a region consistent with production traffic patterns. Measure host‑to‑GPU timings.
- Deploy end‑to‑end proofs of concept (PoCs) in both topologies
- Run the same dataset through an Azure‑only stack and a mixed or multi‑cloud stack. Instrument the RAG pipeline to capture end‑to‑end latency, search latency, tokens/sec, and error/timeout profiles.
- Instrument operational telemetry and engineering hours
- Track DevOps/MLOps time spent on integration, incident resolution, and ongoing maintenance. Measure deployment and incident MTTR for each topology.
- Test portability and exit procedures early
- Create and validate runbooks for data export, index migration, and model portability. Time and cost the extraction process to incorporate into the TCO.
- Harden governance and security controls from day one
- Codify policies (policy‑as‑code), enforce identity controls (Microsoft Entra), and capture model and data lineage (Purview/Defender) so that compliance is intrinsic, not retrofitted.
- Decide by workload and reassess regularly
- Collocate when pilots show clear, repeatable business value (latency, cost, or governance). Preserve hybrid/multi‑cloud for workloads requiring supplier diversity, sovereignty, or unique capabilities. Reassess every 6–12 months.
Decision rules — when to favor single‑cloud Azure vs preserve multi‑cloud
Favor single‑cloud Azure when:- Data gravity is high and egress materially impacts economics.
- The organization already has a significant Microsoft estate (M365, Dynamics, Azure AD) and seeks simplified governance.
- Workloads are latency‑sensitive and require collocated storage and inference for SLAs.
- Legal, sovereignty, or policy requirements mandate local processing or on‑prem operations.
- Critical SLAs demand provider diversity for resilience.
- Best‑of‑breed services on other clouds deliver unique capabilities essential to business outcomes.
Procurement, negotiation, and contract guardrails
- Build exit and migration costs into vendor negotiations and multi‑year TCO models. PT demonstrates how omitting one‑time migration fees can materially bias outcomes.
- Negotiate data portability and export terms, and validate extractability of indexes and embeddings before committing to long‑term contracts.
- Include staged commitments or trial periods that allow switching if the pilot fails to replicate PT’s outcomes under your real usage and negotiated pricing.
Final assessment — pragmatic endorsement with guardrails
Principled Technologies’ study provides useful, hands‑on evidence that consolidating retrieval, model inference, and compute on Microsoft Azure can reduce operational friction, lower end‑to‑end latency, and create more predictable multi‑year costs for sustained, latency‑sensitive AI workloads. The technical pillars PT leverages — data gravity, modern GPU SKUs, and a unified management plane — are real and documented, and they plausibly explain the latency and TCO improvements PT measured.That said, the study’s numeric claims are configuration‑sensitive. The 59.7% and 88.8% deltas are valid for PT’s specific test envelope and should be treated as hypotheses to validate rather than universal laws. IT leaders should re‑run measurements with internal data, rebuild TCO with negotiated pricing, and formalize exit plans before committing to deep vendor consolidation.
Concrete next steps for Windows‑focused organizations
- Run the PT‑style pilot on one high‑value RAG workload, matching SKUs and regions where possible.
- Instrument both topologies end‑to‑end and rebuild TCO with internal inputs, including migration costs and burst scenarios.
- Harden governance from day one and publish tested migration runbooks.
- Use workload‑level decision rules: collocate where it clearly adds business value; retain hybrid/multi‑cloud where portability, resilience, or niche capabilities warrant them.
Principled Technologies’ report is a practical, testable contribution to a nuanced debate: single‑cloud consolidation on Azure can deliver measurable benefits for many enterprise AI workloads, but every organization must validate those benefits against its own workloads, pricing, and risk tolerance. Use the study as a blueprint for disciplined experimentation, not as a shortcut to irreversible commitments.
Source: KHON2 https://www.khon2.com/business/press-releases/ein-presswire/850366910/pt-study-shows-that-using-a-single-cloud-approach-for-ai-on-microsoft-azure-can-deliver-benefits/