TL;DR
AWS vs Azure vs Google Cloud has no single winner, only a best fit for the workload in front of you. AWS has the widest catalog and the deepest bench of engineers, so it suits unusual and long tail requirements. Azure wins when your identity, licensing and reporting already sit inside Microsoft. Google Cloud is fastest to value when analytics and model training are the product. List prices for general purpose compute and standard storage now sit within about five percent of each other, so the real difference shows up in egress, commitment coverage and idle capacity. Score the three against your own workloads with weighted criteria, and price the exit before you sign the commitment.
Key Takeaways AWS held 28 percent of cloud infrastructure spend in Q2 2026, Azure 20 percent and Google Cloud 15 percent, in a market that grew 43 percent year over year. A two vCPU general purpose virtual machine costs $0.1008 an hour on AWS, $0.096 on Azure and $0.0971 on Google Cloud. Compute rates that close rarely decide a platform. Google Cloud charges $0.12 per GiB for internet egress against $0.09 on AWS and $0.087 on Azure, and its free allowance is 1 GiB a month rather than 100 GB. Azure Hybrid Benefit is the largest single discount lever in the market. It is worth up to 80 percent on Windows Server and 85 percent on SQL Server against pay as you go rates. Microsoft and Google both renamed their entire AI stack during 2026. Any comparison still naming Azure Cognitive Services or Vertex AI is describing products that no longer exist under those names. Every frontier model family now runs on more than one hyperscaler, which moves the AI decision away from model access and toward data residency, quota and governance. Watch on YouTube
State of Enterprise Data Migration 2026: Why 73% of Migrations Fail
Kanerika’s own 2026 research on where enterprise cloud and data migrations break down, and what the teams that finish on time do differently.
The Cloud Market Just Reaccelerated, and the Old Comparison Broke Enterprises spent $143.4 billion on cloud infrastructure services in the second quarter of 2026. That is a 43 percent jump year over year, and the fastest growth Synergy Research Group has recorded in eight years. Overall, trailing twelve month spend crossed half a trillion dollars. For context, generative AI services alone grew 165 percent.
Naturally, that surge redrew the board. AWS has slipped to 28 percent share while Google Cloud has climbed to 15 percent, and nine specialist AI hosting firms now sit inside the top forty providers. Yet most published comparisons of AWS, Azure and Google Cloud still quote share figures from 2024.
At the same time, the service names moved too. Microsoft folded its entire AI stack into a brand called Microsoft Foundry, and Google retired the Vertex AI name in place of the Gemini Enterprise Agent Platform. As a result, a buyer working from a 2025 comparison is now shopping for products that do not exist under those names.
This guide compares the three platforms on the things that actually change a cloud strategy decision. Compute, storage, databases, analytics, AI, global reach, security posture, real pricing mechanics and exit cost, with a weighted scoring framework you can run against your own workload list.
AWS vs Azure vs Google Cloud at a Glance in 2026 Before the service by service detail, here is where the three platforms stand today on the numbers each vendor and the market analysts publish. Furthermore, every figure below was pulled from a primary source in September 2026.
Dimension AWS Microsoft Azure Google Cloud Market share, Q2 2026 28% 20% 15% Latest quarterly revenue $42.2B $39.3B (Intelligent Cloud) $24.8B Year over year growth 37% 32% segment, 43% Azure services 82% Announced regions 39 launched 80+ announced 43 Availability zones 124 Not published globally 130 zones General purpose VM, 2 vCPU, per hour $0.1008 (m7i.large) $0.096 (D2s v5) $0.0971 (n2-standard-2) Standard object storage, per GB month $0.023 $0.0208 $0.020 Internet egress, first tier $0.09 per GB $0.087 per GB $0.12 per GiB Free egress allowance 100 GB per month 100 GB per month 1 GiB per month AI platform brand Amazon Bedrock, SageMaker AI Microsoft Foundry Gemini Enterprise Agent Platform Best fit signal Breadth and control Existing Microsoft estate Analytics and model velocity
To begin with, two things in that table deserve attention before anything else. Azure counts announced regions rather than launched ones, so its 80 plus figure is not directly comparable with the 39 AWS operates today. Microsoft also does not publish a global availability zone count, which means any comparison quoting one is guessing.
Flexera’s 2026 State of the Cloud report , based on 753 respondents, puts AWS enterprise adoption at 83 percent and Azure at 79 percent, with Google a distant third. The same survey reports that organizations now waste 29 percent of their cloud spend. In practice, that waste number matters more to most buyers than a two tenths of a cent difference in hourly compute.
AWS vs Azure vs Google Cloud: Where Each One Wins and Where It Costs You Of course, every vendor comparison eventually lands on strengths and weaknesses. Conversely, the useful version names the trade you are making, not the feature you are buying.
AWS: Breadth You Pay for in Operating Complexity To start with, AWS has the largest service catalog and the longest running set of primitives. So if a workload needs something unusual, a specialised instance family, a niche database engine, an obscure region pairing, AWS usually has it first. Similarly, its hiring pool is the deepest of the three, which shortens time to staff a platform team.
Even so, that breadth arrives as surface area. In other words, AWS gives you more knobs, which means more ways to misconfigure identity, networking and cost. Teams that adopt AWS without a landing zone and a tagging standard discover both problems at once. That usually happens around month nine, when the first real bill lands.
Separately, Graviton is the quiet cost story. An m7g.large runs at $0.0816 an hour against $0.1008 for the equivalent Intel instance, a 19 percent saving for workloads that can move to Arm. In fact, most Java, Go, Python and Node services can. Still, many teams never test it.
Microsoft Azure: Gravity From the Estate You Already Own Azure’s advantage is rarely technical. Rather, it is contractual and organizational. Say your identity already runs on Entra ID, your reporting on Power BI and your governance on Microsoft Purview. Azure then removes an integration project that the other two would each require.
Above all, the licensing lever is real money. Azure Hybrid Benefit lets you apply existing Windows Server licenses with Software Assurance against Azure compute. Microsoft advertises that at up to 80 percent off pay as you go rates, and up to 85 percent for SQL Server. By comparison, no equivalent exists on the other two platforms.
On the downside, the cost is pace and consistency. Azure services vary more in maturity between regions than AWS services do, and Microsoft rebrands aggressively. Three AI brand changes in three years is a genuine operational tax on documentation, training material and internal runbooks, and it lands hardest on teams midway through an Azure modernization program.
Google Cloud: Fastest to Value, Narrowest Bench Certainly, Google Cloud grew 82 percent last quarter, the fastest of the three, and analytics is why. In particular, BigQuery removes cluster sizing from the analyst’s job entirely, which is why it keeps appearing on shortlists of cloud analytics tools . For example, a team that would need weeks to stand up a warehouse on either competitor can be querying production scale data in an afternoon.
Meanwhile, Kubernetes is the second draw. Specifically, Google Kubernetes Engine remains the most opinionated and most automated managed Kubernetes of the three, which suits small platform teams running container heavy estates.
On the other hand, the weaknesses are commercial rather than technical. The partner bench is thinner, enterprise support relationships are younger, and egress list pricing is roughly a third higher than the other two. Google also narrowed its automatic sustained use discounts, which we cover in the pricing section.
Compute Compared: Virtual Machines, Containers and Serverless Generally, compute is where most of the bill sits and where the three platforms look most alike. However, the differences that matter are in instance family breadth, Arm availability and how each handles bursty or event driven work.
Capability AWS Microsoft Azure Google Cloud Virtual machines Amazon EC2 Azure Virtual Machines Compute Engine Own Arm silicon Graviton, now at generation five Azure Cobalt Google Axion Managed Kubernetes Amazon EKS Azure Kubernetes Service Google Kubernetes Engine Serverless functions AWS Lambda Azure Functions Cloud Run functions Serverless containers AWS Fargate Azure Container Apps Cloud Run Interruptible capacity Spot Instances Spot Virtual Machines Spot VMs AI training silicon Trainium 3, plus NVIDIA NVIDIA, including GB300 TPU, plus NVIDIA
Equally, the pattern repeats at every layer. All three offer the same shapes of service, and the naming differs more than the capability does. In contrast, where they genuinely diverge is silicon strategy. AWS pushes hardest on its own chips for both general compute and training. Google leads on accelerator design with TPU. Microsoft leans on NVIDIA supply, plus its own Cobalt line for general purpose work.
For a migration team the practical question is narrower. Which of your workloads can run on Arm, and which are pinned to x86 by a vendor binary or an unmaintained dependency. Ultimately, that inventory decides more of your compute bill than the platform choice does.
Case Study
60% Fewer Process Delays With EC2 to EKS on AWS
A UK insurance compliance provider serving more than 2,000 retail sites moved its microservices from EC2 to EKS, cutting process delays by 60 percent and cloud costs by 40 percent.
Read the Case Study → Storage and Data Services Compared By now, object storage list prices have converged to within a tenth of a cent per gigabyte. Instead, the decisions worth making are about tiering policy, retrieval charges and how each platform handles a lakehouse table format.
Storage type AWS Microsoft Azure Google Cloud Object storage Amazon S3, $0.023 per GB Blob Storage Hot, $0.0208 per GB Cloud Storage Standard, $0.020 per GiB Volume discount at 500 TB $0.021 per GB $0.0191 per GB Flat rate, discount by contract Block storage Amazon EBS Azure Managed Disks Persistent Disk and Hyperdisk Managed file shares Amazon EFS and FSx Azure Files and NetApp Files Filestore Archive tier S3 Glacier Deep Archive Blob Archive Archive Storage Automatic tiering S3 Intelligent Tiering Blob lifecycle management Autoclass
Even so, storage rates are a rounding error against retrieval and request charges on an active dataset. A team storing 200 TB in an archive tier and then reading 5 percent of it every month will pay more in retrieval than in storage, on any of the three.
Increasingly, open table formats now matter more than the bucket underneath them. Iceberg, Delta and Hudi all run on all three object stores, and every major query engine reads at least one of them. Choosing a table format deliberately is the single most effective thing you can do to keep your data lake portable across clouds. It is also the decision that most shapes downstream data engineering work.
Databases and Analytics Platforms Compared As a rule, most cloud comparisons stop at compute and storage. For an enterprise the database and analytics layer usually carries the heavier switching cost, because it is where the schemas, the pipelines and the reports live.
Workload AWS Microsoft Azure Google Cloud Managed relational Amazon RDS and Aurora Azure SQL Database Cloud SQL and AlloyDB Globally distributed Aurora DSQL and DynamoDB global tables Azure Cosmos DB Cloud Spanner Key value and document Amazon DynamoDB Azure Cosmos DB Firestore and Bigtable Cloud data warehouse Amazon Redshift Microsoft Fabric and Synapse BigQuery Pipeline and orchestration AWS Glue and Step Functions Azure Data Factory and Fabric pipelines Dataflow and Cloud Composer Catalog and governance AWS Lake Formation and Glue Catalog Microsoft Purview Dataplex Business intelligence Amazon QuickSight Power BI Looker
How Do the Three Warehouses Actually Differ? BigQuery still sets the bar for time to first query. Serverless by design, it separates storage and compute without asking the analyst to size anything, and its pricing model rewards short bursty query patterns.
Meanwhile, Microsoft’s answer is consolidation rather than a single better engine. Microsoft Fabric bundles warehousing, lakehouse, pipelines, real time analytics and Power BI under one capacity purchase, with Purview governing the lot. For an organization already reporting in Power BI, that bundle removes several integration projects at once, which is a different kind of value from raw query speed.
Managed relational pricing follows a similar pattern, and Azure SQL Database pricing is a useful worked example of how tiering changes the bill. By contrast, Redshift is the most conventional warehouse of the three and the most tunable. Teams with existing warehouse expertise and a strong preference for control tend to stay productive on it, and its Iceberg support keeps the underlying data readable by other engines.
One point matters for anyone weighing the warehouse separately from the platform. Synapse and Databricks solve overlapping problems , and both Databricks and Snowflake run on all three clouds. Picking a hyperscaler does not have to mean picking that hyperscaler’s warehouse.
AI and Machine Learning Platforms in 2026 Unsurprisingly, this is the section where stale comparisons fail hardest. Both Microsoft and Google renamed their entire AI stack during the last twelve months, and the competitive logic changed underneath the names.
What Each Platform Is Actually Called Now First, Microsoft has moved its AI brand off the Azure name entirely. Azure Cognitive Services became Azure AI Services and is now Foundry Tools . Azure AI Studio became Azure AI Foundry and is now Microsoft Foundry . The Assistants API retired on 26 August 2026, replaced by the Responses API. Microsoft’s own docs now prompt customers to upgrade an Azure OpenAI resource to a Foundry resource. The details sit in Microsoft’s Foundry documentation .
Second, Google retired the Vertex AI brand at Cloud Next 2026. The product is now the Gemini Enterprise Agent Platform , with Model Garden surviving as a feature inside it rather than a product of its own. The Vertex AI SDK generative modules were removed on 24 June 2026, and Vertex AI Extensions shuts down after 26 November 2026. Accordingly, teams with code written against the old SDK have a real migration to schedule.
Meanwhile, AWS kept its names. Amazon Bedrock is still Amazon Bedrock. The original SageMaker is now SageMaker AI , and the next generation SageMaker is a unified studio built on an Iceberg lakehouse with governance attached.
Model Access Stopped Being a Differentiator Through 2024 and 2025 the simplest argument for Azure was exclusive access to OpenAI models. Today that argument is gone. Microsoft’s exclusivity ended in April 2026, and OpenAI signed a large multi year compute agreement with AWS. GPT 6 Astra now appears in both the Bedrock model catalog and the Microsoft Foundry catalog.
What Still Separates the Three AI Platforms? Anthropic’s Claude models tell the same story from the other direction. They have been on Bedrock and in Google’s Model Garden for some time and reached general availability in Microsoft Foundry on 29 June 2026. All three hyperscalers now sell access to the same frontier families.
What still differs is everything around the model. Data residency, accelerator quota in your region, guardrail configuration, evaluation tooling and how model usage shows up in your governance reporting. Claude on Microsoft Foundry is a concrete example. It does not yet offer EU data residency, which is a hard blocker for some European buyers and irrelevant to everyone else.
Which AI Capabilities Map to Which Platform? AI capability AWS Microsoft Azure Google Cloud Managed model catalog Amazon Bedrock Microsoft Foundry Model Garden in the Agent Platform First party frontier models Amazon Nova 2 family Microsoft MAI family Gemini 3.1 Pro and Gemini Omni OpenAI models Yes, via Bedrock Yes, via Foundry No Anthropic Claude models Yes Yes, GA June 2026, no EU residency yet Yes, via Model Garden Model training and tuning SageMaker AI Microsoft Foundry Gemini Enterprise Agent Platform Custom training silicon Trainium 3 None, NVIDIA based TPU Enterprise search and retrieval Amazon Kendra and Bedrock Knowledge Bases Azure AI Search Vertex AI Search, now under the Agent Platform
For a buying decision, treat AI platform choice as a data question first. After all, the model you can call from any of the three is the same model. The data it can reach, under which controls, is not. That is why teams building retrieval augmented applications usually end up on the platform where their governed data already lives, and why AI and machine learning delivery starts with the data estate rather than the model.
Cloud Services
Pick the Right Cloud Before You Commit to It
Kanerika builds the workload inventory, models the real unit cost on each candidate platform and proves it on one production shaped workload before anyone signs a commitment.
Explore Cloud Services → Global Footprint, Networking and Uptime Commitments Admittedly, region and zone counts get quoted a lot and misread almost as often. Namely, the number that matters is not the global total. It is whether the specific services you need are generally available in the two or three regions your users and regulators care about.
AWS publishes 39 launched regions and 124 availability zones , with a minimum of three zones in every region and further regions announced for Saudi Arabia and Chile. Google Cloud publishes 43 regions and 130 zones , with a handful still consolidating to three physical data centers. Microsoft advertises more than 80 regions and 500 plus data centers . Moreover, it does not publish a global availability zone total, and its region count includes announced as well as live locations.
On the whole, published uptime commitments sit close together. Multi zone virtual machine deployments carry a 99.99 percent service level agreement on all three platforms, and single instance commitments land at 99.5 to 99.9 percent depending on storage type. Since service credits are a small percentage of the monthly bill in every case, treat the agreement as a design constraint rather than insurance.
Similarly, networking service names map almost one to one. Amazon VPC, Azure Virtual Network and Google Virtual Private Cloud cover the same ground. Private connectivity to your own data center is AWS Direct Connect, Azure ExpressRoute or Google Cloud Interconnect. The genuine difference in cloud architecture is that a Google VPC is global by default while AWS and Azure virtual networks are regional, which changes how you design multi region routing.
Security, Compliance and Data Sovereignty By and large, security capability across the three has converged to the point where feature comparison rarely decides anything. Today, identity, encryption at rest and in transit, network isolation, key management and audit logging are table stakes everywhere. Instead, the differences that change a shortlist are jurisdictional.
The Controls Worth Comparing Line by Line Key custody model. All three support customer managed keys. Google goes furthest with External Key Manager, which keeps the key material outside Google’s infrastructure entirely. If your regulator requires hold your own key, check this first.Confidential computing. Memory encryption for data in use is available on all three, but instance family coverage differs by region. Verify availability in your target region rather than at the global level.Sovereign and government regions. AWS and Azure both operate dedicated government and sovereign offerings with separate accreditation. Coverage varies sharply by country, so this is a per market question.Certification breadth. All three carry the usual set, including ISO 27001, SOC reports, PCI DSS and HIPAA business associate agreements. Ask instead which certifications apply to the specific services you plan to use, since scope differs service by service.Posture management. Native tooling exists on each platform, and third party cloud security posture management remains common in multi cloud estates because native tools do not see across boundaries.Above all, data residency is the control that most often eliminates a platform outright. A European insurer that needs its models and its embeddings inside EU borders will find the answer differs by provider and by model, not just by cloud. Kanerika’s teams treat residency as a gating question at the start of a platform assessment rather than a detail to resolve during build.
AWS vs Azure vs Google Cloud Pricing: On Demand, Commitments and Egress In general, pricing is the section buyers care about most and the one most comparisons handle worst, usually by listing discount names without a single number. Accordingly, here are the numbers, pulled from vendor pricing feeds in September 2026.
On Demand Rates Are Effectively Tied A two vCPU, eight gibibyte Linux virtual machine costs $0.1008 an hour on AWS in North Virginia. The same shape costs $0.096 on Azure in East US and $0.0971 on Google Cloud in us-central1. That is a spread of about five percent. Any claim that one platform is dramatically cheaper on general purpose compute does not survive contact with the price list.
Likewise, standard object storage is closer still. Amazon S3 charges $0.023 per gigabyte month for the first 50 TB, Azure Blob Hot charges $0.0208 and Google Cloud Storage charges $0.020 per gibibyte month. On a petabyte that difference is real money, but it is not the difference that decides an architecture.
Commitment Discounts Are Where the Platforms Diverge Mechanism Platform Advertised maximum saving Flexibility Compute Savings Plans AWS Up to 66% Highest, spans EC2, Fargate and Lambda EC2 Instance Savings Plans AWS Up to 72% Locked to an instance family and region Reserved VM Instances, three year Azure Up to 72% Exchangeable within a family Azure Hybrid Benefit, Windows Server Azure Up to 80% Requires existing licenses with Software Assurance Azure Hybrid Benefit, SQL Server Azure Up to 85% Requires existing licenses with Software Assurance Resource based committed use, three year Google Cloud About 55% Locked to machine type and region Flexible committed use, three year Google Cloud About 46% Spend based, portable across families
Still, two observations change how you read that table. First, Azure Hybrid Benefit is the largest single discount available anywhere in this market, and it is not a cloud discount at all. It is a licensing transfer. An enterprise with a large Windows and SQL Server estate can reach effective rates the other two platforms cannot match, which is why Azure cost optimization usually starts with the licensing position.
Second, Google’s ceiling is lower than the headline numbers on AWS and Azure. A three year resource based commitment on an n2-standard-2 brings the rate from $0.0971 to $0.0437, which is 55 percent. Meanwhile, the flexible version lands at about 46 percent. Both are meaningful, yet neither reaches 72 percent.
Why Did Google Narrow Its Sustained Use Discounts? Until recently, sustained use discounts were historically a real Google advantage. Run an instance for most of the month and the discount applied automatically, with no commitment and no forecasting.
However, that is now limited to older machine families. Google’s current eligibility list covers N1, M1, M2 and the legacy micro types at up to 30 percent, and N2, N2D and C2 at up to 20 percent. The current generation families, including C4, N4 and E2, do not appear on it at all.
In effect, this creates a hidden cost of modernising. A team moving from N2 to C4 for better price performance loses the automatic discount and has to buy a commitment to get back to where it was. Consequently, that belongs in your migration model, not in a surprise invoice.
Egress Is the Line Item That Actually Separates Them Internet egress from AWS costs $0.09 per gigabyte for the first 10 TB each month, falling to $0.05 above 150 TB. The first 100 GB each month is free across all regions. Azure charges $0.087 per gigabyte on its default routing, or $0.080 on internet routing, also with 100 GB free. Google Cloud charges $0.12 per gibibyte to North America on Premium Tier, falling to $0.08 above 10 TiB, with a free allowance of just 1 gibibyte.
Because of that, on a data heavy workload the gap compounds fast. Serving 50 TB a month to end users costs roughly $4,500 on AWS, $4,350 on Azure and $5,900 on Google Cloud at list rates. For a media platform or an API business, that single line can outweigh every compute saving on the table.
Nevertheless, exit egress is a different question and the regulation has moved. All three still run the 2024 free data transfer on exit programs, which waive egress for a one time bulk retrieval when you leave. Google’s version requires a formal request, completes within 60 days and covers only Premium Tier. Under the EU Data Act, switching charges may only be cost covering until 12 January 2027 and are prohibited entirely after that date for EU customers. Everyday operational egress stays billable throughout.
Modelling Real Cost for Three Workload Archetypes Again, list prices tell you very little until they are attached to a shape of work. Here are three archetypes most enterprises recognise, and which cost drivers dominate each. Therefore, treat the pattern as the output, not a quotation.
Workload archetype Dominant cost driver Usually cheapest What decides it 10 TB warehouse with nightly ETL Query compute and storage, egress near zero Google Cloud on BigQuery Whether queries are bursty or continuous Always on microservices tier, 40 instances Committed compute and licensing AWS on Graviton, or Azure with Hybrid Benefit Whether the stack runs on Arm, and whether you own Windows licenses Batch inference over a large corpus Accelerator hours and egress on results Depends entirely on quota availability Which provider can actually allocate accelerators in your region
Of these, the third row is the one most teams underestimate. Accelerator capacity is constrained across the industry, and Microsoft has said publicly that Azure demand is running ahead of available capacity. A quota approval timeline can move a project date further than a price difference ever will.
The Flexera finding that 29 percent of cloud spend is wasted also belongs in this model. Idle capacity, oversized instances and forgotten environments routinely cost more than the delta between two providers. Furthermore, fixing waste on the platform you already run is often a better return than switching platforms at all, which is the point of any serious cloud cost management program.
Case Study
32% Lower Infrastructure Cost Moving Across Cloud Providers
A global logistics spend management provider migrated its data focused platform across cloud providers, cutting error resolution time by 60 percent and infrastructure costs by 32 percent.
Read the Case Study → Hybrid, Multi Cloud and the Real Cost of Lock In These days, almost every large enterprise runs workloads on more than one cloud, usually through acquisition rather than design. Therefore, the useful question is not whether to be multi cloud. It is which layers you keep portable and which you happily bind.
Bind the Layers Where Managed Services Pay Off Generally, managed services earn their premium by removing operational work. A team that refuses every proprietary service in the name of portability ends up running its own Kafka, its own Postgres and its own scheduler. That is the most expensive form of independence there is.
On balance, the pragmatic split is to bind the operational layer and keep the data layer portable. Use the managed queue, the managed database and the managed identity service. Keep your table format open, your transformations in standard SQL or a portable framework, and your infrastructure defined as code.
What Hybrid Looks Like on Each Platform Azure has the strongest hybrid story, because Azure Arc extends Azure governance and policy to servers and Kubernetes clusters running anywhere, including on the other two clouds.Google Cloud offers Anthos and GKE Enterprise for consistent Kubernetes across on premises and other clouds, which suits container heavy estates.AWS covers the same ground with Outposts for on premises AWS hardware and EKS Anywhere for clusters outside AWS, with less emphasis on governing other clouds.So if your estate is genuinely split and you need one governance plane over all of it, Azure Arc is the shortest path today. If your estate is container first, Google’s approach fits better. A hybrid cloud model is an operating decision as much as a technical one.
Price the Exit Before You Sign Typically, exit cost has three parts and most buyers only model the first. Egress on the data itself, re-platform effort for anything bound to a proprietary service, and the parallel run period where you pay both bills at once.
Therefore, a useful discipline is to write a one page exit note for every major platform commitment. Which services are proprietary, what the open equivalent is, roughly how long a move would take and what it would cost. Teams that do this rarely regret a commitment, because they made it with the number in front of them.
How to Choose: A Weighted Decision Framework To begin, generic advice to consider your requirements is not a framework. Instead, a framework assigns weights, forces a score and produces an answer you can defend to a steering committee. Here is the one Kanerika’s cloud architects run with clients.
Score Each Criterion Out of Five, Then Multiply by the Weight Notice that total cost of ownership carries the heaviest weight at 25 percent, and it is scored against a modelled workload rather than a price list. Security and compliance fit takes 20 percent, because a residency failure eliminates a platform outright rather than costing points.
Data and analytics maturity and AI and model platform each take 15 percent. Region and latency coverage and portability and exit cost each take 10 percent. Finally, skills and partner availability takes the last 5 percent, which is deliberately small because skills can be bought and architecture cannot be unbought.
In practice, score honestly and the result is usually clear within a few points. When two platforms tie, the tiebreaker is almost always the criterion your organization will not be able to change later, which in practice means residency or licensing.
Which Cloud Should You Actually Pick? Choose AWS when the estate is varied and the requirements include something unusual. It suits teams that already have platform engineers, and cases where multi region resilience is a hard requirement rather than an aspiration.Choose Microsoft Azure when identity already runs on Entra ID and reporting already runs on Power BI. It also wins if you hold Windows Server or SQL Server licenses with Software Assurance, or need one governance plane across cloud and on premises.Choose Google Cloud when analytics is the primary workload and the platform team is small. It fits best where Kubernetes is the deployment model and model training or serving is core to the product.Lastly, one caveat is worth stating plainly. If your answer comes out close, the correct decision is usually to stay where you are and shut down what is idle, not to move. Migration has a cost and a risk profile that a five percent price difference does not justify, which any honest cloud migration roadmap will show.
What Actually Goes Wrong During the Move Granted, platform selection is the easy half. Indeed, the failure modes Kanerika’s migration teams see most often have very little to do with which logo is on the console.
Which Five Patterns Derail Cloud Programs? Lift and shift with no re-sizing. On premises servers are provisioned for peak plus headroom plus a safety margin. Moving that shape unchanged into hourly billing is how a migration business case turns negative in month three.No landing zone before the first workload. Identity boundaries, network topology, guardrails and cost tags are cheap to design on day one. Retrofitting that architecture across 200 accounts is not.Data gravity ignored in the sequencing plan. Moving an application before the dataset it reads creates cross cloud chatter that bills as egress on every request. The right data migration tools sequence the dataset first.Commitments bought too early. A three year reservation against a workload shape that changes in month six locks in the wrong instance family at a discount you cannot fully use.Governance deferred to phase two. Lineage, catalog and access policy retrofitted after the fact usually means re-running a migration you already paid for, so data governance belongs in wave one.Importantly for planning, none of these are platform specific. They show up on AWS, Azure and Google Cloud in the same proportions, which is a useful reminder that execution discipline matters more than vendor choice. The same pattern shows up in wider cloud migration practice across industries.
How Kanerika Helps Enterprises Pick and Move to the Right Cloud In practice, Kanerika works with enterprises in manufacturing, logistics, insurance, healthcare and retail on exactly this decision, and then on the migration that follows it. Specifically, the engagement runs in four stages rather than as a single recommendation document.
Assess, Model, Prove, Then Move Assess. First, inventory every workload and group it by data gravity, latency tolerance and compliance class. This is where residency constraints surface, and where roughly a third of the inventory usually turns out to be a candidate for retirement rather than modernization .
Model. Next, build the unit economics for each group on each candidate platform, with egress, inter zone transfer, licensing and realistic usage levels included. The weighted scoring matrix above is populated here, from measured numbers rather than vendor estimates.
Prove. Then run one real workload end to end on the shortlisted platform against the same performance baseline it meets today. After all, a proof of concept that does not carry production data volumes proves very little.
Move. Finally, build the landing zone first, then migrate in waves ordered by data gravity, with FLIP automating pipeline and code conversion where the target is a modern data platform, and Karl surfacing insights on the migrated estate once it lands. FLIP is deployed across Azure, AWS and Google Cloud. Its Azure to Microsoft Fabric accelerator has delivered 80 percent faster migration timelines, 50 percent lower migration costs and 65 percent fewer resources required on Microsoft estates.
Where Kanerika Is Credentialed, and Where It Is Not Kanerika is a Microsoft Solutions Partner for Data and AI with the Analytics Specialization. It also holds Microsoft Advanced Specializations for Data Warehouse Migration to Microsoft Azure and for Azure Data and Analytics. Everest Group named Kanerika a Major Contender in its Microsoft Azure Services PEAK Matrix Assessment 2026 .
To be clear, the deepest credentials sit on the Microsoft side, which is worth saying plainly rather than implying a symmetry that does not exist. AWS and Google Cloud work is delivered on engineering capability and delivered outcomes, not on partner tier. Kanerika is also ISO 27001, ISO 27701 and ISO 9001 certified, SOC 2 Type II compliant and CMMI Level 3 appraised.
Delivered Outcomes on All Three Platforms Finally, those four sit across three platforms deliberately. Besides, a partner that only ever recommends one cloud is describing its own bench, not your workload. Kanerika’s cloud services and migration practice start from the workload inventory every time.
Talk to an Architect
Run the Scoring Matrix Against Your Own Workloads
Bring your workload inventory to a working session with Kanerika cloud architects and leave with a weighted score, a modelled cost per platform and a sequenced migration plan.
Book a Discovery Session → Wrapping Up By now, the three hyperscalers have converged on capability and on price. General purpose compute, standard storage and published uptime commitments now sit within a few percent of each other, and every frontier model family runs on more than one platform.
Instead, what separates them is contextual. Specifically, it is licensing you already own, data you already govern, regions your regulator will accept, egress volumes your product generates and accelerator quota your region can actually allocate. Yet none of that appears on a feature grid.
So run the weighted matrix against your own workload inventory. Model the real unit cost including egress and idle capacity, prove it on one production shaped workload, and write down what leaving would cost. A decision made that way holds up for the length of the commitment you sign against it.
Frequently Asked Questions
Which is best: Google Cloud, AWS, or Azure? The best cloud platform depends on your specific workload requirements and existing technology stack. AWS offers the broadest service portfolio and mature infrastructure, making it ideal for diverse enterprise needs. Azure excels for organizations invested in Microsoft ecosystems with seamless Office 365 and Active Directory integration. Google Cloud leads in data analytics, machine learning capabilities, and Kubernetes-native workloads. Each cloud provider delivers distinct advantages for compute, storage, and hybrid deployments. Kanerika’s cloud specialists assess your infrastructure requirements and recommend the optimal platform strategy—schedule a consultation to identify your best fit.
Which is better: AWS, GCP, or Azure? AWS, Azure, and GCP each dominate different use cases rather than one being universally better. AWS provides unmatched service breadth with over 200 offerings, making it suitable for complex enterprise architectures. Azure integrates tightly with Microsoft tools, benefiting organizations running Windows Server, SQL Server, or Microsoft 365. GCP delivers superior performance for big data analytics, AI workloads, and containerized applications through its Kubernetes heritage. Your decision should align with technical requirements, team expertise, and long-term scalability goals. Kanerika helps enterprises evaluate AWS vs Azure vs GCP objectively—connect with our cloud architects for tailored guidance.
What are the top 3 cloud providers? Amazon Web Services, Microsoft Azure, and Google Cloud Platform are the top three cloud providers dominating the global market. AWS leads with approximately 31% market share, pioneering infrastructure-as-a-service since 2006. Azure follows at around 25%, leveraging Microsoft’s enterprise relationships and hybrid cloud capabilities. Google Cloud holds roughly 11%, excelling in analytics, AI services, and multi-cloud Kubernetes orchestration. Together, these hyperscalers control over two-thirds of worldwide cloud infrastructure spending. Kanerika partners with all three leading cloud platforms to deliver optimized migrations and data solutions—reach out to explore your options.
Who is bigger: Azure or AWS? AWS remains larger than Azure in total cloud infrastructure market share, commanding approximately 31% compared to Azure’s 25%. Amazon launched its cloud services in 2006, giving it a significant head start in building global data center presence and service maturity. However, Azure has grown rapidly through Microsoft’s enterprise software dominance and hybrid cloud strategy. AWS generates higher cloud revenue annually, though Azure’s growth rate has consistently outpaced AWS in recent quarters. The gap continues narrowing as enterprises adopt multi-cloud strategies. Kanerika supports both AWS and Azure implementations—contact us to architect your cloud foundation.
Which cloud platform is best? The best cloud platform aligns with your organization’s technical requirements, existing investments, and strategic priorities. AWS suits enterprises needing extensive service variety and global reach across 30+ regions. Azure provides the strongest path for Microsoft-centric organizations seeking unified identity management, hybrid connectivity, and enterprise licensing advantages. Google Cloud excels for data-intensive workloads, machine learning development, and containerized microservices architectures. Cost structures, compliance needs, and team skillsets should drive your decision beyond feature comparisons alone. Kanerika’s platform-agnostic approach ensures you select the right cloud foundation—book a discovery session with our experts.
Do most companies use Azure or AWS? Most companies use AWS when measuring total adoption, though Azure dominates among enterprises with existing Microsoft investments. AWS serves millions of active customers globally, including startups, digital natives, and large enterprises across industries. Azure captures significant market share in regulated sectors like healthcare, finance, and government where Microsoft’s compliance certifications and on-premises integration matter. Many organizations now adopt multi-cloud strategies, running workloads across both AWS and Azure simultaneously. The choice often depends on workforce expertise and application stack requirements. Kanerika implements solutions across both leading cloud platforms—let us help optimize your deployment strategy.
Which cloud platform is used most? AWS is the most widely used cloud platform globally, holding the largest market share at approximately 31% of infrastructure spending. Amazon launched cloud computing services commercially in 2006, establishing first-mover advantage that persists today. AWS operates the most extensive global data center network and offers over 200 distinct services covering compute, storage, databases, networking, and machine learning. Azure follows as the second most used platform, particularly strong within enterprise organizations leveraging Microsoft technologies. Google Cloud ranks third with growing adoption in analytics-heavy sectors. Kanerika delivers expertise across all major cloud platforms—engage our team to accelerate your cloud journey.
Is Azure going to overtake AWS? Azure could potentially overtake AWS within the next decade given its consistent higher growth rate and enterprise momentum. Microsoft has grown Azure revenue faster than AWS for multiple consecutive quarters, narrowing the market share gap steadily. Azure benefits from bundled enterprise agreements, hybrid cloud capabilities through Azure Arc, and deep integration with productivity tools enterprises already use. However, AWS maintains significant infrastructure advantages, broader service portfolio, and loyal customer base among digital-native companies. The outcome depends on multi-cloud adoption trends and enterprise purchasing decisions. Kanerika monitors these market dynamics closely—consult with us to future-proof your cloud investments.
Is Azure growing faster than AWS? Azure has grown faster than AWS in percentage terms for several consecutive quarters, though AWS still adds more absolute revenue annually. Microsoft reports Azure growth rates between 25-30% year-over-year, while AWS growth has moderated to approximately 12-17%. This acceleration stems from Microsoft’s enterprise relationships, bundled licensing incentives, and strong hybrid cloud positioning that appeals to traditional IT organizations. Azure’s integration with Microsoft 365, Dynamics, and security tools creates natural expansion paths within existing accounts. Both platforms continue expanding infrastructure globally to meet enterprise demand. Kanerika helps organizations leverage Azure’s rapid innovation—connect with our Azure specialists today.
Who is AWS's biggest competitor? Microsoft Azure is AWS’s biggest competitor, holding the second-largest cloud infrastructure market share and aggressively pursuing enterprise customers. Azure competes directly across compute, storage, databases, AI services, and hybrid cloud solutions. Google Cloud Platform ranks as the third major competitor, particularly strong in data analytics, machine learning, and Kubernetes orchestration. Beyond hyperscalers, AWS also faces competition from specialized providers like Oracle Cloud for database workloads, IBM Cloud for regulated industries, and Alibaba Cloud in Asian markets. The cloud computing landscape continues consolidating around these primary competitors. Kanerika implements solutions across AWS and competing platforms—reach out for unbiased guidance.
What are the top 3 cloud platforms? AWS, Microsoft Azure, and Google Cloud Platform constitute the top three cloud platforms powering enterprise digital transformation globally. AWS dominates with comprehensive infrastructure services, extensive partner ecosystem, and mature operational tooling. Azure excels through Microsoft enterprise integration, hybrid cloud capabilities via Azure Stack, and strong security compliance certifications. Google Cloud differentiates through advanced analytics, BigQuery data warehousing, and industry-leading AI and machine learning services built on Google’s research heritage. These three hyperscalers collectively represent the foundation for modern cloud computing strategies across industries. Kanerika delivers expertise across all three leading platforms—start your cloud assessment with our specialists.
Which cloud is most expensive? AWS typically carries higher list prices for comparable compute and storage services compared to Azure and Google Cloud, though actual costs depend heavily on usage patterns and negotiated discounts. Google Cloud generally offers the most competitive pricing, particularly for sustained-use compute instances and data egress. Azure provides cost advantages for organizations with existing Microsoft Enterprise Agreements through Azure Hybrid Benefit and reserved instance discounts. True cloud cost optimization requires analyzing workload-specific requirements rather than comparing list prices alone, as each provider structures pricing differently. Kanerika’s cloud cost optimization experts help enterprises reduce spending across all platforms—request a cost analysis today.
Which is more costly: GCP or AWS? AWS generally costs more than Google Cloud Platform for equivalent compute and storage workloads based on published pricing. GCP offers sustained-use discounts automatically applied after consistent monthly usage, while AWS requires committing to reserved instances upfront. Google Cloud’s per-second billing and competitive egress pricing further reduce costs for variable workloads. However, AWS provides more granular instance sizing options that can optimize costs for specific applications. Enterprise negotiations significantly impact final pricing on both platforms, making direct comparisons difficult without workload-specific analysis. Kanerika helps enterprises benchmark cloud costs accurately—schedule a pricing analysis to optimize your cloud spend.
What's better: Google Cloud or AWS? AWS offers broader service coverage and infrastructure maturity, while Google Cloud delivers superior capabilities for data analytics and machine learning workloads. AWS suits organizations requiring extensive managed services, global reach across 30+ regions, and established enterprise support frameworks. Google Cloud excels when BigQuery data warehousing, Vertex AI machine learning, or Kubernetes-native architectures drive your requirements. GCP also provides competitive pricing and Google’s networking backbone advantages for latency-sensitive applications. Your existing team skills and technology investments should heavily influence this decision beyond feature comparisons. Kanerika architects solutions on both AWS and Google Cloud—contact us to determine your optimal platform.
