TL;DR
Microsoft Azure is Microsoft’s cloud platform for compute, storage, data, and AI, built to extend an existing Microsoft estate into the cloud rather than replace it outright. Gartner predicts 90% of organizations will adopt hybrid cloud through 2027, a setup Azure is built around via Azure Arc. This guide covers Azure’s core services, how it compares to AWS and Google Cloud, migration patterns, where cost commonly leaks, Azure’s AI capabilities, and how Kanerika delivers Azure work as a provider Everest Group has recognized as a Major Contender in its 2026 assessment.
A hybrid rollout that runs partly on-premises and partly in the cloud is quickly becoming the default setup for large enterprises, not a temporary bridge on the way to somewhere else. Gartner predicts that 90% of organizations will adopt a hybrid cloud approach through 2027, and Microsoft Azure sits at the center of that shift for enterprises already running Microsoft 365, Windows Server, or SQL Server on-premises. Azure is Microsoft’s cloud platform for compute, storage, data, and AI, built to extend an existing Microsoft estate into the cloud rather than replace it outright.
This guide covers what Azure actually offers, how it compares to AWS and Google Cloud, the cost and migration decisions enterprises face, and how Kanerika delivers Azure work as a provider Everest Group has recognized as a Major Contender.
Key Takeaways Azure is Microsoft’s cloud platform, built to extend an existing Microsoft 365, Windows Server, or SQL Server estate rather than replace it. Gartner predicts 90% of organizations will run a hybrid cloud approach through 2027, a model Azure is architected around through Azure Arc and Azure Stack. Azure holds the second-largest share of the cloud infrastructure market behind AWS, and leads among enterprises already standardized on Microsoft technology. Unoptimized Azure deployments commonly overspend through idle resources and oversized virtual machines; reserved instances and auto-scaling meaningfully reduce that cost. Azure AI Foundry and Azure Machine Learning give enterprises a native path to build AI directly against data already stored in Azure. Kanerika was named a Major Contender in Everest Group’s Microsoft Azure Services PEAK Matrix Assessment 2026. What Is Microsoft Azure, and What Does It Actually Offer? Microsoft Azure is Microsoft’s cloud computing platform, providing infrastructure, platform, and software services for compute, storage, databases, networking, data, and AI. It is built to extend an existing Microsoft estate into the cloud, which is why it tends to be the default choice for organizations already standardized on Microsoft 365, Windows Server, or SQL Server.
The Core Service Categories Compute. Virtual machines, containers, and serverless functions for running applicationsStorage. Blob storage, managed disks, and file shares for structured and unstructured data Databases. Azure SQL Database and Azure Cosmos DB for relational and globally distributed NoSQL workloadsData and analytics. Native integration with Microsoft Fabric , so data engineering, warehousing, and reporting run on the same underlying platformAI and machine learning. Azure AI Foundry and Azure Machine Learning for building and deploying models
Kanerika’s Azure SQL Database pricing guide covers the database layer’s cost structure specifically, since it is one of the most frequently underestimated line items in an Azure budget.
Why Enterprises Default to Azure An organization already paying for Microsoft 365 and Windows Server licensing through an Enterprise Agreement often finds that extending into Azure costs less incrementally than starting fresh on a different provider, and identity and access management carry over directly through Microsoft Entra ID. That existing investment, more than any single technical feature, is usually what puts Azure at the top of the shortlist before a formal evaluation even begins.
Azure Regions and Global Availability Azure runs across a large number of global regions, which matters for two practical reasons beyond raw geographic reach. Latency drops when compute sits closer to the end user, and data residency requirements in regulated industries or specific countries often mandate that certain data never leaves a defined geographic boundary. Enterprises planning a multi-region deployment need to map this requirement early, since retrofitting data residency controls after a deployment is live is considerably more disruptive than designing for it up front.
Azure Arc and the Hybrid Cloud Model Azure Arc extends Azure’s management plane to infrastructure running outside Azure entirely, including on-premises servers, other cloud providers, and edge locations, letting a single team apply consistent policy, security, and monitoring across all of it. This is the specific mechanism behind Gartner’s hybrid cloud prediction: enterprises are not necessarily moving everything into Azure, they are extending Azure’s control plane out to wherever their workloads already sit. For an enterprise with data residency constraints or hardware that cannot realistically move to the cloud, Arc is often the practical path to hybrid rather than a full migration.
How Does Azure Compare to AWS and Google Cloud? The three major cloud providers overlap heavily on core infrastructure, but the right choice usually comes down to existing licensing, ecosystem fit, and workload type rather than any single feature.
Where Each Provider Leads How Azure, AWS, and Google Cloud Typically Get Chosen Provider Where It Tends to Lead Microsoft Azure Organizations already standardized on Microsoft 365, Windows Server, or SQL Server, and hybrid cloud deployments AWS The broadest overall service catalog and the largest share of the cloud infrastructure market Google Cloud Data and AI-native workloads, and organizations built around Kubernetes and open-source tooling
Kanerika’s AWS vs Azure vs Google Cloud comparison covers the three-way decision in more depth, and AWS Redshift vs Azure Synapse covers a specific, frequently asked data-warehouse comparison between the two ecosystems.
Moving Between Providers Enterprises switching providers, most commonly from AWS to Azure to consolidate around an existing Microsoft agreement, face a migration project with its own dependencies and licensing considerations. Kanerika’s AWS to Azure migration guide covers what that specific move typically involves.
Why “Best” Depends on What Is Already in Place A greenfield startup with no existing infrastructure can pick a cloud provider on pure technical merit. An enterprise with fifteen years of Windows Server deployments, Active Directory, and a Microsoft Enterprise Agreement already in place is optimizing a very different equation, where switching costs and identity migration effort often outweigh a marginal feature advantage on a competing platform.
Multicloud Rather Than Single-Provider by Default Most large enterprises today are not choosing one provider exclusively. A common pattern is running core business systems on Azure to align with an existing Microsoft agreement, while a specific team runs a data science workload on Google Cloud or a legacy application stays on AWS because migrating it is not worth the disruption. Treating this as multicloud by design, with a consistent identity and governance layer spanning providers, tends to produce fewer surprises than treating it as an unplanned byproduct of different teams making independent choices over time.
Is tech debt stalling your cloud growth? Get Kanerika’s 5-minute Azure Architecture Assessment.
Book a Meeting
Migrating to Azure: What the Process Actually Involves A migration to Azure is rarely a single event. It is a sequence of decisions about which workloads move, in what order, and using which migration pattern.
The Three Migration Patterns Rehost. Move a workload to Azure with minimal changes, sometimes called “lift and shift,” fastest but leaves cloud-native cost and scaling benefits on the tableReplatform. Make targeted changes, such as moving a database to a managed Azure service, to capture some cloud-native benefit without a full rebuildRearchitect. Redesign the application around cloud-native services, the most effort but the most long-term benefit for workloads that will run for years
Kanerika’s Azure data migration guide and Azure migration tools guide cover the tooling and planning behind each pattern, and Azure to Fabric migration covers a specific, high-demand move for enterprises consolidating their data platform onto Fabric.
Migrating Data Platforms on Azure Enterprises running Azure Data Factory or SQL Server workloads frequently evaluate a move to Microsoft Fabric or a lakehouse architecture as their analytics needs grow. Kanerika’s Azure Data Factory vs Databricks , Azure Databricks vs Snowflake , and Azure Synapse vs Databricks comparisons cover how these platforms sit alongside core Azure infrastructure, and Informatica vs Azure Data Factory vs Talend and dbt vs Informatica vs Azure Data Factory cover the ETL tooling layer specifically.
Why Migrations Slip Their Timeline Most Azure migration delays trace back to underestimated dependency mapping, not the actual data transfer. A workload that looks self-contained often turns out to call three other internal systems nobody documented, and each undiscovered dependency adds testing time the original plan did not budget for. An accelerator that automates dependency discovery and the repetitive parts of the move, rather than a fully manual migration, is what keeps enterprise timelines closer to the original estimate.
A Practical Sequence for a First Azure Migration Inventory dependencies first. Map every system a workload actually calls before committing to a migration datePilot on a low-risk workload. Prove the migration pattern on something that will not disrupt the business if the first attempt needs adjustmentRight-size before, not after. Provision the Azure equivalent based on actual observed usage, not a like-for-like copy of the old environment’s specsCut over with a rollback plan. Keep the original environment available until the new one has proven stable under real production loadDecommission on a schedule. Retire the legacy environment deliberately once confidence is established, rather than letting both run indefinitely and pay for duplicate infrastructure
Skipping the pilot step is the most common shortcut enterprises regret, since the first migration of a given pattern reveals issues a written plan alone rarely anticipates.
Security and Identity on Azure A migration plan that treats security as a follow-up phase after the move is complete tends to leave a gap regulators or attackers eventually find first. Microsoft Entra ID handles identity and access management across an Azure estate, and enterprises already running Active Directory on-premises typically extend it into Azure rather than standing up parallel identity systems. Network security groups, private endpoints, and Azure Policy for automated compliance enforcement round out the core controls most enterprise migrations need configured before, not after, workloads go live in production.
Azure Cost Optimization: Where Spend Actually Leaks An Azure bill that grows faster than usage almost always traces back to a small set of recurring, fixable patterns rather than the platform itself being expensive.
Common Sources of Azure Cost Overrun Cost Leak What Drives It Oversized virtual machines Provisioning for peak load and never resizing down once actual usage patterns are known Idle resources Development and test environments left running outside business hours Duplicated storage Data copied across environments without a retention or cleanup policy Unused reservations Reserved instances purchased for a workload that later moved or shrank Uncommitted spend Paying on-demand rates for steady-state workloads that qualify for a consumption commitment discount
Kanerika’s Azure cost optimization guide covers the fix for each of these in more depth, and Microsoft Azure Consumption Commitment guide covers how a MACC agreement can lower the effective rate on committed spend, an option many enterprises leave on the table simply because nobody owns tracking it.
Monitoring as a Cost Control, Not Just an Uptime Tool Cost visibility depends on monitoring that is already in place for reliability reasons. Kanerika’s Azure monitoring tools guide covers how the same telemetry used to catch performance issues also surfaces the idle and oversized resources that quietly inflate a monthly bill.
Building the Business Case for Azure Cost Optimization What a Credible Azure Cost Optimization Business Case Includes Component What It Answers Current spend baseline What the organization actually spends on Azure today, broken down by resource type and environment Waste estimate How much of that spend goes to idle, oversized, or duplicated resources based on a usage audit Commitment opportunity Which steady-state workloads qualify for reserved instance or MACC discounts not currently applied Target reduction The specific percentage or dollar reduction expected within a defined timeframe Ongoing monitoring cost What it costs to keep monitoring and right-sizing in place so savings do not erode again over time
The ongoing monitoring line is the one most cost optimization projects skip, which is exactly why the same waste tends to creep back within a year of a one-time cleanup effort.
Building AI on Azure Azure’s AI stack is built to run directly against data already stored in Azure, rather than requiring a separate export to a different AI platform.
The Core AI Services Azure AI Foundry. Covered in Kanerika’s Azure AI Foundry guide , the platform for building, evaluating, and deploying generative AI applications on AzureAzure Machine Learning. For training, tracking, and managing custom machine learning models, compared to alternative registries in MLflow vs Hugging Face Hub vs Azure ML Fabric-native AI. AI workloads that run directly inside Microsoft Fabric on top of Azure’s underlying infrastructure, avoiding a separate data copy
Enterprises building generative AI or machine learning on Azure benefit from the same identity, governance, and networking controls already in place for the rest of their Azure estate, rather than standing up a parallel security model for AI workloads specifically.
Azure Cloud Solutions Kanerika designs, migrates, and optimizes Azure environments, backed by an Everest Group Major Contender placement and Microsoft Advanced Specializations
Explore Azure Cloud Services
Microsoft Azure in Production: How Kanerika Delivers It Kanerika was named a Major Contender in Everest Group’s Microsoft Azure Services PEAK Matrix Assessment 2026 , placing Kanerika among a recognized group of Azure services providers evaluated by Everest Group’s standard, full assessment methodology. The full Everest Group report covers the assessment in detail.
Migrating From Azure Data Factory to Microsoft Fabric Kanerika migrated a client’s data pipelines from Azure Data Factory onto Microsoft Fabric, consolidating the data platform onto a single governed environment. The full case study covers how the migration was executed.
Improving System Performance Through Cloud Computing Kanerika improved system performance for a patient self-care platform by moving core workloads onto cloud infrastructure, giving the client the elasticity a fixed on-premises footprint could not provide. The full case study covers the performance improvement in more depth.
Migrating Data-Focused Applications Across Cloud Providers Kanerika executed a data-focused application migration across cloud providers for a client consolidating its infrastructure footprint. The full case study covers the migration approach and outcome.
Results From Azure Engagements Table 4: What These Azure Engagements Delivered Engagement Result Azure Data Factory to Fabric migration Data pipelines consolidated onto a single governed Microsoft Fabric environment Cloud computing for patient self-care Improved system performance and elasticity beyond what the fixed on-premises footprint could provide Cross-cloud application migration A data-focused application successfully migrated across cloud providers with infrastructure consolidated
The Azure to Fabric Migration Accelerator Kanerika’s Azure to Fabric Migration Accelerator, part of the FLIP platform and now available as a native Microsoft Fabric workload, delivers 80% faster migration timelines, 50% lower migration costs, and 65% fewer resources required compared to a fully manual migration. It was launched as a native Fabric workload at FabCon 2026 alongside Karl, Kanerika’s AI data insights agent.
What the Everest Group Placement Actually Means A PEAK Matrix placement is not a self-reported badge. Everest Group runs its own independent research and evaluation process, assessing providers against defined criteria for market impact and delivery capability rather than accepting a vendor’s own claims at face value. Being placed as a Major Contender in the Microsoft Azure Services PEAK Matrix Assessment 2026, through Everest Group’s standard full assessment methodology, puts Kanerika’s Azure practice in a peer group evaluated on the same criteria as considerably larger global providers, which is a meaningfully different signal than a marketing claim with no independent verification behind it.
Case Study: Migrating From Azure Data Factory to Microsoft Fabric How Kanerika consolidated a client’s data pipelines from Azure Data Factory onto a single governed Microsoft Fabric environment.
Read full case study
Which Azure Partner Should You Choose? Building Azure expertise in-house is possible, but most enterprises underestimate the migration tooling, cost governance, and multi-workload experience required to run Azure efficiently at scale.
What to Look For Independently verified recognition, such as a PEAK Matrix placement, rather than only a vendor’s own marketing claims Named migration accelerators with measured time and cost reductions, not just a services menu Certified Microsoft partner status specific to the workload, such as data warehouse migration or analytics Experience across the full Azure data and AI stack, not only infrastructure provisioning Why Enterprises Choose Kanerika for Azure Most Azure partners sell provisioning and step back. Kanerika’s Azure cloud solutions practice stays through migration, cost governance, and AI enablement, backed by an independently verified market placement rather than a self-reported credential.
What Backs the Delivery Everest Group Major Contender, Microsoft Azure Services PEAK Matrix Assessment 2026 Microsoft Advanced Specialization: Data Warehouse Migration to Microsoft Azure, earned December 2025 Microsoft Advanced Specialization: Azure Data & Analytics Microsoft Solutions Partner for Data and AI with Analytics Specialization, and a Microsoft Featured Fabric Partner FLIP’s Azure to Fabric Migration Accelerator, available as a native Microsoft Fabric workload ISO 27001, ISO 9001:2015, SOC 2 Type II, and CMMI Level 3 certified Wrapping Up Azure rewards enterprises that treat it as an extension of infrastructure they already run, not a blank slate to build from scratch. The organizations getting the most value are the ones pairing Azure’s native services with disciplined cost monitoring and a migration plan that accounts for dependencies most teams underestimate on the first pass. Gartner’s own hybrid cloud forecast suggests that pattern is only becoming more common, not less, over the next few years.
Choosing a partner who has been through this independently verified, rather than one making the claim alone, is a reasonable filter when the decision involves a platform an enterprise will likely run on for the next decade. The work rarely ends at go-live, and the providers that stay engaged through cost governance and platform change are the ones enterprises tend to keep working with well past the first project.
Planning an Azure Migration or Cost Review? Get a working session on your current Azure environment, where cost is leaking, and a realistic migration or optimization plan with Kanerika.
Book a Meeting
Explore the Full Microsoft Azure Library Browse every Azure guide by what you need to do.
Fundamentals and Comparisons Migration Data Platform on Azure Cost and Monitoring AI and Build FAQs
What is Microsoft Azure? Microsoft Azure is Microsoft’s cloud computing platform, providing infrastructure, platform, and software services for compute, storage, databases, networking, data, and AI. It is built to extend an existing Microsoft estate, such as Microsoft 365, Windows Server, or SQL Server, into the cloud rather than replace it outright, which is why it is the default choice for many enterprises already standardized on Microsoft technology.
How does Azure compare to AWS and Google Cloud? AWS holds the largest share of the cloud infrastructure market, Azure holds the second-largest share and leads among enterprises already running Microsoft workloads, and Google Cloud leads on certain data and AI-native workloads. The right choice usually depends on the existing technology stack, licensing agreements already in place, and which platform’s ecosystem the organization’s applications and identity systems already run on.
How much does it cost to run workloads on Azure? Azure costs depend on compute, storage, and data transfer volume, and unoptimized deployments commonly overspend through idle resources, oversized virtual machines, and duplicated storage. Reserved instances, auto-scaling, and a Microsoft Azure Consumption Commitment can reduce costs meaningfully, but require ongoing monitoring rather than a one-time setup to keep spend aligned with actual usage.
What does it take to migrate to Azure? A migration to Azure typically starts with an assessment of the current workload’s dependencies and licensing, followed by a choice between rehosting, replatforming, or rearchitecting the application. Timelines vary widely by complexity, and enterprises moving from another cloud provider or a legacy on-premises data platform generally benefit from an accelerator that automates the repetitive parts of the move.
What AI capabilities does Azure offer? Azure offers Azure AI Foundry for building and deploying generative AI applications, Azure Machine Learning for training and managing custom models, and native integration with Microsoft Fabric for AI workloads that need to run directly against an organization’s existing data estate rather than a separate copy of it.
