New Licensing Cost Optimization Analysis from Cloudamize Accelerates Windows Workload Migration to Amazon Web Services (AWS)

Cloudamize is pleased to announce today the addition of licensing cost optimization analysis to our platform, which will enable companies to quickly and easily understand which options will yield the greatest cost savings from Microsoft product licensing when moving Windows workloads to AWS.

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With Cloudamize’s Automated Analytics, The Instillery Now Plans Migrations 3x Faster – and Can Take On More Projects

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How to Instill Confidence in Cloud Migrations with a Detailed TCO Assessment Report

Among the biggest hurdles that systems integrators and cloud service providers encounter is helping clients understand the costs associated with migrating to the cloud. Many companies begin their migration journey with hesitations, fears, and doubts, as well as a lack of understanding on how to properly calculate a cloud TCO. Systems integrators and cloud service providers can give confidence to their clients by helping them see in precise detail exactly what they can expect to pay after their migration to the cloud is complete.

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Cloudamize Announces DMA Integration and Azure SQL Right-Sizing for Easing and Optimizing SQL Server Migration to Azure

It’s extremely challenging for companies to understand what their data estate currently looks like, whether to migrate their databases using PaaS or IaaS, and how to ensure a smooth migration that will not result in cost or performance issues. Today, we're announcing new capabilities to the Cloudamize platform that will help our partners address these questions for the clients and modernize their data estate with better ease and accuracy. 

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How Right-Sizing Reduced One Company’s TCO Calculation by 74%--and Accelerated Adoption of AWS

Despite the increasingly common success stories of companies migrating to the cloud, cloud adoption doesn’t happen quickly or easily. Many companies face uncertainties on what their cost and performance will be in the cloud, causing systems integrators and cloud service providers to encounter hesitation and friction around adoption —especially when it comes to presenting a TCO calculation.

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Don’t Have Automated Analytics? Then You Won’t Be Able to Accomplish These 6 Cloud Migration Planning Steps

Launching a successful cloud migration strategy that doesn’t result in performance degradation or unsatisfied users requires analyzing both a company’s own infrastructure as well as the numerous public cloud options available. Unfortunately, many cloud migration strategies still rely on extremely time-consuming manual analysis.

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To Accelerate Cloud Adoption, Predict the Unpredictable: Give Companies Precise Cost and Performance Projections Using Automated Analytics

 

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Customer Success Spotlight: How 6 Cloudamize Customers Overcame 4 Common Cloud Migration Challenges

For many organizations planning a cloud migration strategy, it may seem that the road to a successful cloud migration is littered with potholes. When migrating on-premises infrastructure to the cloud, failing to avoid problems along the way can lead to a migration breakdown.

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Cloudamize Winter ‘18 Release Includes First True Agentless Data Collector for Automated, In-Depth TCO Calculation and Discovery

We’re very pleased to announce today that the Cloudamize platform now offers the industry’s first true agentless solution, Agentless Data Collector. This update enables enterprises that cannot install agents due to internal policy, change management, or security standards to now leverage a solution for truly in-depth discovery, TCO analysis, and dependency mapping.

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Public Cloud Benchmarks: Why They're Essential for Migration Planning

Unlike on-premises environments—which have comprehensive, publicly available benchmarks—there are no publicly available benchmarks on CPU and memory for instances in the public cloud. Benchmarks, which refer to how much CPU and memory each machine can handle, enable IT teams to determine how many workloads and what type of workloads to run on which machines.  

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