devopsy.kinsta.cloud

Tag: Microsoft Azure

GitHub Takes Down 73 Microsoft Repos After Miasma Worm Attack

The software supply chain took another hit last week. On June 5, GitHub disabled 73 Microsoft-owned repositories after the Miasma worm infiltrated projects across four organizations: Azure, Azure-Samples, Microsoft and MicrosoftDocs.

GitHub’s automated systems triggered the takedown within 105 seconds of detecting the infection — a fast response, but the damage was already done. The attack began when a malicious commit was pushed to the Azure/durabletask repository using a previously compromised contributor account. The commit planted configuration files that execute a credential-harvesting payload when a developer opens the repository in an IDE or AI coding tool.

That last detail is worth paying attention to. The affected tools include Claude Code, Gemini CLI, Cursor and VS Code — tools that millions of developers use every day. Just opening a repository in a trusted environment was enough to trigger the payload.

A Worm With History

Miasma is a variant of the Mini Shai-Hulud worm that a group called TeamPCP publicly released in mid-May 2026. The original Shai-Hulud appeared in September 2025 as the first self-replicating malware observed in the npm ecosystem. Since then, it has mutated across npm and PyPI, previously compromising 32 Red Hat packages and affecting packages from TanStack, Mistral AI and UiPath.

This wasn’t a random attack on Microsoft. The same compromised contributor account was used in both the May PyPI attack and the June GitHub incident, and the payloads are highly similar. Someone picked a target and came back for a second round.

Among the disabled repositories are notable projects including azure-search-openai-demo, the durabletask library and its .NET, Go, Java, JavaScript and MSSQL implementations, functions-container-action, llm-fine-tuning, and windows-driver-docs. These aren’t obscure side projects. They are infrastructure that development teams depend on.

The Real Problem: The Developer Environment is Now an Attack Surface

Traditional supply chain attacks focus on packages — something gets installed, and the malicious code runs. Miasma works differently. The immediate blast radius was not cloud infrastructure itself, but the software factory around it: GitHub Actions workflows, Azure Functions tooling, Durable Task libraries, and developer machines.

Instead of relying on traditional package installation hooks, Miasma targets the developer’s local environment. It abuses legitimate auto-run, hook, and rule engines within modern IDEs and AI coding assistants to execute its payload.

That is a meaningful shift. Mitch Ashley, VP and practice lead for software lifecycle engineering and AI-native software engineering at The Futurum Group, put it plainly: “Software’s trust boundary has moved from the installed package to the act of opening code in a tool. Miasma weaponizes the auto-run and hook engines that make IDEs and AI coding agents productive, turning the developer environment into an execution surface.”

Ashley doesn’t stop at the diagnosis. “Opening a trusted repository is no longer a safe, read-only act. Engineering teams now have to govern what their coding tools may auto-execute and how far the credentials they hold can travel once one account is compromised.”

What Comes Next

The attack harvested credentials for cloud platforms and developer tools, then used them to propagate to additional repositories. That self-replicating behavior is what makes Miasma different from a typical compromise. It doesn’t wait to be discovered — it moves.

For security and DevOps teams, this incident reinforces something that has been true for a while but is harder to ignore now: the people writing your software are targets, not just the software itself. A compromised developer account or a stolen personal access token can do just as much damage as a vulnerability in production code.

Microsoft and GitHub sit at the center of the developer trust economy. The Miasma attack is a reminder that even that center is now exposed to attacks that behave less like traditional intrusions and more like contagion.

The containment in this case was fast — 105 seconds is genuinely impressive. But the downstream impact on teams relying on those 73 repositories is still being assessed. And the bigger question isn’t how quickly GitHub can respond. It’s how organizations can build development pipelines that don’t treat trusted sources as unconditionally safe.

That work starts with visibility. Teams need to know what’s in their dependency chains, who has access to their repositories, and what happens when code gets opened in an IDE. The Miasma attack showed that the threat model for software development has expanded. Security teams need to expand with it.

June 9, 2026
Microsoft Azure Skills Plugin Gives AI Coding Agents a Playbook for Cloud Deployment

AI coding agents are good at writing code. They’re not good at knowing which Azure service fits your workload, which SKU makes sense, what needs to be validated before deployment, or which permissions and quotas matter. That gap between writing code and getting it to production is exactly what Microsoft’s new Azure Skills Plugin is designed to close.

Announced March 9 by Chris Harris on the All Things Azure blog, the plugin bundles 19+ curated Azure skills, the Azure MCP Server with over 200 tools across 40+ services, and the Foundry MCP Server for AI model workflows — all in a single install. It works across GitHub Copilot in VS Code, Copilot CLI, Claude Code, and other tools that support the agent plugin and skills patterns.

The timing isn’t accidental. This is one of the first major plugins built on the VS Code agent plugin architecture that shipped in VS Code 1.110 just days earlier. And it demonstrates what that architecture looks like when a cloud platform vendor fills it with real operational knowledge.

Three Layers in One Install

The plugin packages three distinct capabilities that work together.

Skills are the decision layer. The 19+ Azure skills package expert knowledge as reusable workflows, decision trees, and guardrails. They don’t just list tools — they tell the agent when to use them, in what order, and what to avoid. Key skills include azure-prepare (analyzes your project and generates infrastructure code, Dockerfiles, and deployment config), azure-validate (runs pre-flight checks before deployment), azure-deploy (orchestrates the actual deployment pipeline), azure-cost-optimization (finds waste and produces savings recommendations), and azure-diagnostics (troubleshoots failures with logs, metrics, and KQL queries). Additional skills cover compute, observability, compliance, storage, migration, RBAC, and messaging.

The Azure MCP Server is the execution layer. It provides over 200 structured tools across 40+ Azure services — listing resources, checking prices, querying logs, running diagnostics, provisioning infrastructure, and driving deployment workflows. The agent doesn’t just advise. It acts.

The Foundry MCP Server is the AI specialist. It connects the agent to Microsoft Foundry for model deployment, agent management, and model catalog workflows. For teams building AI-powered applications on Azure, this layer handles model and agent scenarios that generic cloud guidance can’t.

The design principle: skills guide, MCP executes, the plugin packages both so the guidance and execution layers stay aligned.

What Changes for Developers

The before-and-after comparison in the announcement is telling. Ask an agent without the plugin to deploy a Python Flask API to Azure, and you get a generic tutorial — maybe some az commands, maybe a docs link. Ask the same agent with the plugin, and it activates azure-prepare, azure-validate, and azure-deploy in sequence, producing a Dockerfile, infrastructure files, deployment config, validation output, and real MCP tool calls against Azure.

Same prompt. The difference is that the agent has a playbook and the tools to follow it, rather than generating advice.

Skills load on demand — the agent reads their name and description to decide which are relevant, then loads only what’s needed. You can install the full skill set without cluttering every prompt. They’re plain text, version-controlled, and auditable. One team can package hard-won Azure knowledge once and reuse it across projects and engineers.

According to Mitch Ashley, VP and practice lead for software lifecycle engineering at The Futurum Group, “Microsoft is packaging institutional cloud knowledge as versioned, executable skills that agents consume automatically, collapsing the gap between writing code and deploying it to production. Skills encode the decision logic, MCP tools execute against real resources, and the plugin architecture keeps guidance and execution aligned. That’s agent orchestration with guardrails, beyond just automation with documentation.”

Ashley continues, “The tribal knowledge that once lived in a senior engineer’s head becomes an installable, reviewable artifact. Teams that figure out how to package their own expertise in this model will accelerate deployment cycles. Those waiting for agents to infer that knowledge from generic training will keep getting generic answers.”

Why This Matters for DevOps

The Azure Skills Plugin is a concrete example of a pattern that’s becoming central to how DevOps teams work with AI agents. We’ve covered agent plugins as infrastructure, IronCurtain’s policy-as-code model, Google ADK’s integration ecosystem, and Copilot’s Jira bridge. The Azure Skills Plugin sits at the intersection of all of them: Packaged expertise, delivered through a plugin architecture, with both guidance and execution capabilities built in.

For platform engineering teams, the plugin represents a model worth watching. Instead of writing documentation that developers may or may not read, you package the knowledge as skills that agents consume automatically. The agent doesn’t need to read your Confluence page about which Azure services to use. It activates the skill that encodes that decision tree.

That’s a shift in how institutional knowledge gets distributed. Documentation explains. Skills execute. The tribal knowledge that used to live in a senior engineer’s head — which SKU to pick, which pre-flight checks to run, which cost traps to avoid — becomes a versioned, reviewable, installable package.

The plugin also ships guardrails alongside capabilities. Skills include validation steps, permission checks, and cost awareness. The agent operates against real Azure resources, so curated skills, trusted plugin sources, tool approvals, and least-privilege access all matter. This isn’t simulated cloud work. It’s real provisioning, real deployment, real cost implications.

Install the Azure Skills Plugin at aka.ms/azure-plugin. One install, three layers, and a much different answer to “deploy my app to Azure.”

March 11, 2026
Digital.ai Extends Scope and Reach of DevSecOps Platform

An Erawan release of the DevSecOps platform from Digtal.ai adds an ability to automated applications security along with integration with Backstage, an open-source internal developer platform originally developed by Spotify that is now being advanced under the auspices of the Cloud Native Computing Foundation (CNCF).

In addition, Digital.ai is tightening its integration with the Microsoft Azure cloud platform by making it simpler to validate and manage Helm charts.

Digital.ai CEO Derek Holt said as the pace of application development starts to further accelerate, thanks to the rise of generative artificial intelligence (AI) tools, organizations will need to revisit their DevSecOps workflows. Specifically, they will need a platform with the governance capabilities required, to ensure that only code that has been thoroughly reviewed and tested makes it into a production environment, said Holt.

The company is also working toward adding generative AI tools to its DevSecOps platform to make it simpler to manage code bases that are about to rapidly expand, he noted. Digital.ai has been laying the foundation for that effort via a Digital.ai Intelligence data lake that already applies predictive machine learning algorithms to data collected by both Digital.ai and third-party partners, said Holt.

Over time, many of the capabilities of individual tools that DevSecOps teams once needed to integrate are becoming features of a larger platform to enable DevSecOps teams to better embrace platform engineering as a methodology for managing DevSecOps workflows at scale, he noted.

There are no shortage of options when it comes to DevSecOps platforms, but the ability to apply software engineering intelligence across normalized data collected from increasingly hybrid application development environments will prove crucial, added Holt.

Best DevOps Practices

It’s not clear to what degree organizations that have already invested in a DevOps platform might be willing to switch platforms in the age of AI-assisted application development. However, the number of organizations that will be able to create and deploy custom applications is about to substantially increase. Many of those organizations will eventually need to adopt a set of best DevSecOps practices at a time when governments around the world are moving to hold organizations that build and deploy software more accountable for application security.

In the meantime, DevSecOps teams should assume the amount of code simultaneously moving across pipelines will increase as developers take advantage of various generative AI tools to help them write. The challenge is the first generation of these tools has been trained using code samples of varying quality collected from across the internet. As such, the code generated by these tools might have known vulnerabilities or might not in some cases work at all. Each DevSecOps team, as always, will need to verify that code — regardless of whether it was created by a human or a machine — can be used in a production environment.

After all, the benefits of rapidly building applications are generally erased if the end-user experience deteriorates to the point where DevSecOps teams spend more time troubleshooting existing applications than deploying new ones.

June 17, 2024
Cisco Adds Generative AI and Deeper Splunk Integrations to AppDynamics

Cisco this week previewed a generative artificial intelligence (AI) assistant to the Cisco AppDynamics observability platform along with integrations to the IT Service Intelligence (ITSI) platform from Splunk and a Splunk Log Observer Connect for Cisco AppDynamics to the platform it gained earlier this year by acquiring Splunk.

Announced at the Cisco Live! 2024 event, Cisco in the third quarter will also make it possible to host Cisco AppDynamics on the Microsoft Azure platform.

Cisco also announced that it will no longer sell the Cisco AppDynamics Platform for Cloud Native Application Performance Management (APM). That platform will be replaced by Splunk Observability Cloud.

Cisco also added additional data science and machine learning algorithms capabilities to ITSI to streamline the number of alerts the platform generates. Now generally available, the Configuration Assistant in Splunk ITSI provides a centralized console by providing insights into outdated threshold models that can be optimized using guided remediations surfaced by AI models.

Finally, Cisco has enabled single sign-on capabilities to streamline shared workflows between Cisco AppDynamics and Splunk via a single console. Those SSO capabilities will be extended across the Cisco portfolio to enable increased collaboration.

Tom Casey, senior vice president and general manager for products and technology for the Splunk product portfolio, told conference attendees the platforms will gradually be integrated into a single data platform, including Cisco AppDynamics, the Cisco ThousandEyes networking monitor service and the Splunk platform. That approach will also allow Cisco to provide best-in-class capabilities for everything from application performance monitoring to digital experience monitoring across a hybrid cloud computing environment, he added.

Paul Nashawaty, practice lead for application development at The Futurum Group, said these combined capabilities highlight Cisco’s commitment to providing comprehensive visibility at a time when distributed IT environments are becoming increasingly more challenging to manage.

It’s not clear to what degree organizations are unifying observability, network monitoring, and traditional IT service management (ITSM), but Cisco is clearly betting IT leaders will prefer to standardize on a single vendor to reduce integration costs that would otherwise be incurred when using platforms provided by disparate vendors. That centralized approach will also make it simpler to train multiple AI models that will be used to further automate a range of workflows that today span multiple IT siloes.

Less clear, however, is to what degree the acquisition of Splunk might contribute to a consolidation of IT platforms as rivals make similar acquisitions to drive a more platform-centric approach to unifying the management of IT.

Each IT organization needs to decide how to best structure themselves as workflows across those teams become more integrated. Certainly, the overall size of application portfolio to be managed is only going to continue to expand as AI makes it simpler to build and deploy software. As a result, IT teams most certainly need to apply best DevSecOps practices at higher levels of scale to ensure application availability across highly distributed computing environments.

June 6, 2024
Azure Cost Optimization: A Complete Guide

Microsoft Azure is one of the “big three” cloud providers, and is a primary part of IT expenditure for many companies. Azure cost optimization is a strategy that involves managing and controlling Azure costs to ensure efficient usage and spending. It requires a thorough understanding of where and how your resources are being used, as well as insight into the various pricing options available.

Azure cost optimization involves implementing best practices related to managing cloud costs, such as turning off resources when not in use, choosing the right-sized resources, taking advantage of Azure discounts and more. By optimizing these aspects, you can ensure that you are only paying for the resources you need and are using them efficiently.

Azure cost optimization is an ongoing strategy that involves constant monitoring and adjustments as your usage and needs evolve. This means that you need to be proactive and vigilant in your cost management efforts, regularly reviewing your usage patterns and making adjustments as necessary.

Why is Azure Cost Optimization Important?

As organizations migrate more and more of their workloads to the cloud, the costs associated with Azure usage can quickly escalate if not properly managed. This can lead to budget overruns and decreased profitability.

By leveraging Azure cost optimization strategies, businesses can gain greater control over their Azure spending. This not only helps in reducing costs but also enables businesses to more accurately predict their future cloud spending. This can be a major advantage when it comes to financial planning and budgeting.

Moreover, Azure cost optimization can also lead to improved operational efficiency. By ensuring that resources are used efficiently, businesses can avoid wasted spend and focus their resources on the tasks that truly add value to their operations. This can lead to increased productivity and better overall performance.

Azure Cost Optimization Tools

Azure provides several free tools designed to help customers optimize their cloud spending. These tools provide insights into your usage patterns, help you identify potential cost savings, and guide you in implementing best practices for cost management.

Azure Cost Management and Billing

Azure Cost Management and Billing provides detailed insights into your Azure spending. It allows you to track your spending by resource, location, service, and more, enabling you to identify where your money is going.

With Azure Cost Management and Billing, you can set up budgets to control your spending and set up alerts to notify you when you’re nearing your budget limits. You can also analyze your spending trends over time, which can be useful for forecasting future costs.

Azure Advisor

Azure Advisor provides personalized recommendations based on your usage and configurations, helping you optimize your resources for cost, performance, security and reliability.

Azure Advisor can identify unused or underutilized resources you can shut down or resize to save money. It also provides recommendations for purchasing reserved instances or using Azure Hybrid Benefit, which can lead to significant cost savings.

Azure Pricing Calculator

The Azure Pricing Calculator is a tool for estimating the cost of Azure services before you use them. You can input your expected resource usage, and the calculator will provide an estimate of the costs.

This tool can be especially useful when you’re planning a new workload or considering a migration to Azure. By understanding the potential costs upfront, you can make more informed decisions and avoid surprises.

Azure Monitor

Azure Monitor is a comprehensive service that provides insights into your application performance, infrastructure, and network. While primarily used for performance monitoring, it can also provide valuable insights for cost optimization.

For example, Azure Monitor can help you identify overutilized or underutilized resources, enabling you to make adjustments to optimize your costs. It can also help you track your resource usage over time, providing insights into your spending patterns.

Azure Resource Graph

Lastly, Azure Resource Graph is a service that enables you to explore your Azure resources at scale. It provides a unified view of your resources across all your subscriptions, making it easier to manage and optimize your resources.

With Azure Resource Graph, you can quickly identify resources that are not in use or are underutilized, allowing you to reduce unnecessary spending. It also enables you to visualize your resource usage, helping you understand your spending patterns better.

6 Strategies for Azure Cost Optimization

Now that we are familiar with the basics of cost optimization on Azure and the tools the platform provides, let’s review some strategies that can help you optimize and reduce costs in Azure.

1. Right-Sizing Azure Resources

One of the simplest yet most impactful strategies for Azure cost optimization is to right-size your Azure resources. This involves matching your business requirements with the appropriate Azure resources in terms of type, size and quantity.

Too often, organizations over-provision their Azure resources, which leads to unnecessary expenditures. By right-sizing, you ensure that you’re only paying for what you need. This can be achieved by closely monitoring your Azure resources usage, identifying any underutilized resources, and scaling them back accordingly.

Remember, Azure provides a range of resource types, each with its own set of capabilities and costs. Always cross-verify whether the selected resource is the most cost-effective one for your specific use case. For instance, if your application doesn’t require high compute power, choosing a lower-tier virtual machine could significantly reduce your costs.

2. Utilizing Azure Budgets and Alerts

Azure Budgets and Alerts are powerful tools for cost management. They provide you with the ability to set a budget for your Azure spending and receive alerts when your spending approaches or exceeds your budget.

Setting up an Azure budget is a proactive approach to cost management. It allows you to allocate your finances effectively and avoid any unexpected charges. Furthermore, with Azure alerts, you can instantly get notified when your consumption is nearing your budget. This gives you ample time to adjust your usage or increase your budget, preventing any service disruptions due to overspending.

Moreover, Azure Budgets and Alerts are highly customizable. You can create multiple budgets for different scopes (like management groups, subscriptions, or resource groups), set your alert conditions, and even automate actions based on these alerts using Azure Logic Apps.

3. Tagging and Resource Organization

Azure tagging and resource organization is another crucial strategy for Azure cost optimization. Tags are user-defined key-value pairs that you can assign to your Azure resources. They help you categorize and manage your resources based on your business needs.

By effectively tagging your Azure resources, you can gain visibility into your resource consumption at a granular level. This can help you identify any high-cost resources, track your spending by project, department or environment, and make informed decisions about cost optimization.

Organizing your resources into logical groups can also simplify management and governance. For instance, you can group your resources based on their life cycle or purpose and apply consistent policies and access controls across the group. This not only improves security and compliance but also helps in identifying any redundant resources that can be eliminated to save costs.

4. Utilizing Storage Tiers Based on Usage Requirements

Another effective strategy for Azure cost optimization is to utilize different storage tiers based on your usage requirements. Azure Storage provides three access tiers – hot, cool and archive, each designed for different types of data.

The Hot tier is for data that is frequently accessed and requires low latency. It has higher storage costs but lower access costs. The Cool tier is for infrequently accessed data, offering lower storage costs but higher access costs. Finally, the Archive tier is the most cost-effective solution for long-term retention of rarely accessed data.

By carefully analyzing your data usage patterns and selecting the right storage tier, you can significantly reduce your Azure storage costs. Moreover, Azure offers automatic tiering options like blob-level tiering and life cycle management policies, which can further optimize your storage costs based on your data lif ecycle.

5. Leveraging Azure Hybrid Benefit

If you’re an existing Microsoft customer, leveraging Azure Hybrid Benefit can be a game-changer for your Azure cost optimization. Azure Hybrid Benefit allows you to bring your on-premises Windows Server and SQL Server licenses with software assurance to Azure, offering significant cost savings.

With Azure Hybrid Benefit, you can run Windows Server and SQL Server workloads on Azure at a reduced rate, providing substantial savings as compared to pay-as-you-go prices. Furthermore, Azure is the only cloud provider that offers such benefits, making it an ideal choice for businesses looking to optimize their cloud costs while leveraging their existing Microsoft investments. Azure Hybrid Benefit is starting to support software from other companies, including Oracle and Red Hat.

6. Regularly Reviewing and Optimizing Subscriptions

Lastly, regularly reviewing and optimizing your Azure subscriptions is a must for Azure cost optimization. This involves keeping track of your Azure usage and costs, identifying any inefficiencies and making necessary adjustments.

Azure provides several tools like Azure Cost Management and Azure Advisor that can help you gain insights into your Azure spending and offer personalized recommendations for cost optimization. For instance, Azure Advisor can identify idle resources that can be shut down to save costs or suggest reserved instances for predictable workloads to avail discounts.

Moreover, consider exploring Azure Cost Management Power BI App for advanced reporting and analysis of your Azure spend. It offers a rich set of visuals and filters that can help you uncover deep insights into your cost data and drive cost efficiency.

In conclusion, while Azure offers a plethora of services and capabilities, managing costs effectively requires strategic planning and continuous monitoring. By implementing the above strategies, you can ensure that you’re making the most out of your Azure investments while keeping your costs under control.

February 1, 2024
DevOps Download: What to Know About Life in the Cloud(s)
According to the 2022 Flexera State of the Cloud Report, 89% of cloud customers now use multiple cloud hosting providers. Nearly nine out of 10 might sound high, but it tracks with the anecdotal evidence we’ve seen. For varied and good reasons, companies are spreading their usage across multiple hyperscalers rather than putting all their eggs in one basket.

In this article, I’ll take a look at what life in the cloud(s) looks like today. Along the way, I’ll examine the value that companies and their DevOps teams can derive from this approach, the challenges to be aware of, use cases for making the multi-cloud move and more.

What is Multi-Cloud?

Multi-cloud is a type of cloud strategy approach where an organization uses computing services from at least two cloud providers to run their applications. Instead of a single-cloud stack, these environments include two or more public or private clouds, or a combination of both. With multi-cloud, you can have an application running on Google Cloud Platform (GCP), another in Amazon Web Services (AWS), and maybe one on premise for hard-to-move applications.

There are three kinds of cloud setups. Single cloud is when workloads run solely on one environment. Hybrid cloud entails overlapping workloads across multiple clouds, such as operating the same programs on different providers for business continuity purposes. The final setup is planned multi-cloud, which disperses applications and usage across providers along specific lines, like having compute intensive applications with one provider and data analysis applications with another.

Got a Preference?

Make no mistake, using multiple cloud providers is more complex than a single host, but as its popularity shows, the benefits can carry more weight. Foremost, that’s because an organization gains the flexibility to create a best of all worlds situation, based on factors like the following:
- Operational preference: Users may like different features from different providers, and splitting types of use can yield the ideal setup. For instance, a customer may run compute on AWS but want their business intelligence and machine learning on GCP. In this deployment, the front-end app could gather the needed information, then send the data to the other provider for analysis and ML.
- Regulatory requirements: Certain workloads may require data storage in specific regions, but not all cloud vendors have multiple “regions” in each country. Companies that want to use the cloud can set up a “multi-region” environment by having one region on AWS and a second region on GCP within the same country.
- Cost savings: Cloud providers offer different savings plans. For a savvy user, structuring a flexible program can take advantage of each to significantly reduce costs. It’s also a great way to avoid the pitfalls of lock-in and can make it easier to go elsewhere should a provider lose your trust.
- Failsafe Options: Outages happen, so companies needing 24/7 access to apps will sometimes run portions redundantly on two hosts to ensure smooth operations. For example, a retail app may run on GCP and AWS with live failover between them. That way, if one goes down the other can kick in without noticeable downtime.
Not Without its Challenges

The benefits to running a multi-cloud setup are clear, but there are some challenges as well. A carefully planned multi-cloud setup can save money, but a careless approach will duplicate operations and cost. It also leaves companies vulnerable to mistakes that can lead to significant overrun, especially for hybrid cloud operations, which can be extremely complex. Specialists must be well versed in FinOps principles and experienced at writing modern applications that can scale and work across regions and clouds, to ensure success in a multicloud approach.

Coming to Terms

You’ll undoubtedly come across a lot of new ideas and terminology when researching cloud setups. Here’s a few of the more crucial ones to become familiar with as you plan strategy.
- Lift and shift: This refers to cloud-to-cloud migration, in other words, taking what you have in one cloud and putting it in another, whether it is private to public or public to public.
- Move and improve: This usually refers to migrating while making a small change to take advantage of a public cloud feature. For instance, you could be using open source Kubernetes on premise but want to move to a managed version. Eliminating management of Kubernetes would require a minor change but it improves operations greatly.
- Cloudbursting: On premise offers hard limits to capacity. For companies able to anticipate business peaks, cloudbursting can scale workloads to an on-demand public cloud for a short period. For example, a retailer who operates on premise may cloudburst on Black Friday in order to handle a volume increase.
- High Availability: The goal of this is to eliminate single points of failure by enabling users to continue working even if one of the IT components that an applications depends upon fails.
- Disaster Recovery: Clouds offer solutions to help an organization restore critical systems and data after a disaster strikes. Recovery comes in three “heat levels”: Cold is for data backup only, warm restores operations in several hours, hot is for near-instant resumption of cloud operations.
People and Processes

The most important considerations for operationalizing multi-cloud are people and processes. Providers pump out new services fast, and not only can it be difficult for staff to keep up, it can be demoralizing when they’re unable to. Allowing time to succeed, fail and learn is key, and larger companies might consider designated specialists for each platform.

When it comes to processes, management and security are highly customizable, but it requires deep knowledge to get them right. Operating via infrastructure as code rather than ClickOps is a must. The latter is a time consuming approach that requires selecting options in a provider menu and introduces both operational and security risks in the form of configuration drift.

Life in the Clouds

Multi-cloud brings strong benefits to DevOps. Organizations can get the right infrastructure balance based on their needs. Distributing workloads can reduce the impact of outages and bolster resiliency. There’s greater opportunity for cost savings, and scaling is simple. Ultimately, multi-cloud operational enhancements can result in more reliable and innovative solutions, which delivers on the mantra of DevOps itself.
August 10, 2023
Google Calls Microsoft Azure Anti-Competitive ¦ Gen-Z Can’t Email

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Google complains to the FTC about Microsoft market abuses, and new graduates don’t know how to office. (more…)

June 22, 2023
Microsoft’s 9th Outage in 2023 ¦ RISE of RISC-V ¦ Meta Ends WFH

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: Redmond SaaS keeps failing, RISC-V is RISEing, and Meta is enforcing hybrid work. (more…)

June 7, 2023
Quick Guide to Azure Storage Pricing

Azure Storage is a cloud-based storage service from Microsoft Azure. It provides a scalable, secure and highly available data storage solution for businesses of any size.

The platform includes various services such as Azure Blob, which is a service for storing large amounts of unstructured data such as documents, images, videos and backups. Azure Files is a fully managed file share service for cloud-based applications. Azure Tables is a NoSQL key-value store used to store semi-structured data. Azure Queues is a messaging service used to store and process messages between applications or services.

Finally, Azure Disks is a service that allows you to store and manage data in virtual hard disks used in virtual machines. With Azure Storage, businesses can easily manage their data, control access and scale their storage as per their needs without worrying about hardware or infrastructure management.

How does Azure charge for storage space (bandwidth, capacity, number of transactions and quantity of transferred data)? What are the usage factors impacting Azure Storage fees: Region, account, access tier, replicas, egress?

How Azure Storage Pricing Works

Azure pricing for its storage services is based on several factors including storage capacity, data transfers and transaction counts. The cost of storage varies depending on the region and access tier selected, as well as the type of storage being used.

For Azure Blob Storage, customers are charged for storage capacity, data egress (outbound data transfers) and the number of transactions (read, write and list operations).

Azure Table Storage charges are based on the number of table entities stored and the amount of data transferred. Azure Queues are charged per transaction and data transfer, while Azure Disks are billed based on the provisioned capacity and the number of transactions.

Several usage factors can impact Azure Storage fees, including the region where the data is stored, the storage account type, the access tier selected, the number of replicas and the amount of data transferred out of the storage account.

Overview of Azure Storage Pricing

Blob Storage

Azure Blob Storage pricing varies depending on the storage tier, data volume and whether customers opt for pay-as-you-go or reserved capacity.

For pay-as-you-go pricing, Premium storage is charged at a flat rate of $0.015 per GB/month, while Cool and Archive storage are billed at $0.01 and $0.0099 per GB/month, respectively, regardless of data volume. Hot storage costs $0.018 per GB /month for the first 50 TB with the rate gradually decreasing with higher usage. For instance, customers who consume more than 500 TB/month pay $0.0166 per GB.

Customers can also reserve blob storage in 100-TB or 1-PB blocks for one or three years. The prices for reserved storage vary by tier. A three-year Archive storage reservation costs $84 per 100 TB or $810 per PB/month, while Hot storage costs $1,244 per 100 TB/month for three years. For one-year reservations, Archive storage costs $91 per 100 TB or $883 per PB/month, and Hot storage costs $1,545 per 100 TB/month. For a one-year reservation, the cost of Hot storage is $15,050 per PB/month, and $11,963 per month for three years.

Files

Microsoft charges customers for Azure Files depending on the storage tier and whether they reserve storage capacity. Pay-as-you-go pricing ranges from $0.015 per GB/month for Cool storage to $0.016 per provisioned GiB/month for Premium file storage.

Customers can commit to reserved storage capacity in increments of 10 TiB or 100 TiB for one or three years. A three-year reservation of 10 TiB Cool storage costs $101.39 per month, while one year costs $125.92 per month. However, the price increases significantly for the Premium offerings, with a three-year reservation of 10 TiB costing $1,081.34 per month and a one-year reservation costing $1,343.50 per month. 100 TiB reservations range from $983/month in the Cool tier for three years to $12,779.50/month in the Premium tier for one year.

Nevertheless, there are other fees that add to the total cost such as read and write transactions, snapshots, metadata storage and early file deletion fees. These ancillary fees can impact the overall cost of using Azure Files and customers should account for them when estimating their expenses.

Tables

Azure Tables pricing is available in various tiers, including locally redundant storage (LRS), geo-redundant storage (GRS), read-access geo-redundant storage (RA-GRS), zone-redundant storage and geo-zone-redundant storage.

Customers are charged between $0.045 per GB/month for LRS and $0.01265 per GB/month for geozone-redundant read-only storage. Additionally, Microsoft charges a $0.0036 fee for each set of 10,000 transactions made on Azure Tables.

Queues

Compared to Blob and File Storage, the pricing model for Azure Queues is more straightforward. Customers are charged 4.5 cents to $0.075 cents per GB/month, depending on whether they choose local redundant storage (LRS) or read-access geozone-redundant storage (RA-GRS). Additionally, there is a $0.004 fee for every set of 10,000 operations in Class 1 and 2.

Managed Disks

Azure Managed Disks have numerous pricing options that can be overwhelming. The cost of using Managed Disks depends on several factors, including the disk size, performance, reservation period, capacity and disk type.

The cheapest option for Managed Disks is the P1 disk, which costs $0.6 per month for 4 GiB of capacity in addition to $0.03 per mount/month. On the other hand, the P80 disk is the most expensive option, as it has a 32 TiB capacity costing $3,276.80 per month, in addition to $219/mount. However, the cost of a P80 disk can be reduced to $3,113/month (plus the $219/mount fee) with a reservation of one year.

Azure Storage Vs. AWS Storage Pricing

Here is a comparison of the pricing for similar storage services offered by Azure and AWS. As part of an Azure or AWS cost optimization strategy, you might consider combining services from both providers.

Block Storage

Amazon EBS is Amazon EC2’s basic storage device, with a price of $0.045 per GB for HDD and $0.10 per GB for SDD, with higher IOPS resulting in higher prices. The free tier includes 30 GB of SDD.

For comparable regions in the U.S., Azure charges $0.05 per GB for HDD and $19.71 for 128 GB of SSD per month.

Azure Managed Disks offer greater flexibility and easier backup than Azure Virtual Disks or Amazon EBS, with adjustable volume sizes and available IOPS. Managed Disks pricing starts at $19/month for a 128 GB disk, providing 500 IOPS.

Object Storage

Amazon S3 and Azure Blob Storage are both scalable services for storing objects (unstructured data) where data is stored together and cannot be changed in parts. Prices vary depending on the region, but for a comparable region in the U.S., Amazon S3 storage costs $0.023 per GB/month for the first 50 TB in addition to network usage fees. The free tier offers 5 GB of storage, while archive storage (Glacier) is $0.004 per GB/month.

Azure Blob Storage starts at $0.0184 per GB/month for hot storage, although prices decrease to $0.01 per GB per month for cool storage and $0.002 for archive storage. Prices for both services increase for greater redundancy.

File Storage

Amazon Elastic File System (EFS) is an equivalent service to Azure File storage and allows users to mount files to Amazon EC2 servers. It uses NFS V4.0/4.2 to mount files and is not available in all AWS regions (currently, four are supported), requiring a Direct Connect line or VPN for on-premises mounting.

For a comparable U.S. region, Amazon EFS costs $0.30 per GB/month and the free tier offers 5GB on sign-up. Azure File Storage prices start at $0.06 per GB/month for local redundant storage, with prices increasing for more distributed redundancy setups. It is worth noting that both Azure and AWS charge additional fees for data access.

Conclusion

In conclusion, Azure Storage offers a range of services for storing and managing data in the cloud, each with its own pricing structure. While the pricing varies based on several factors including storage capacity, data transfers and transaction counts, it is essential to consider other usage factors, such as region, account, access tier, replicas and egress.

Customers should review these factors carefully and estimate their expenses to select the right storage service that meets their needs and budget. By doing so, they can leverage Azure Storage’s flexibility and scalability to store and manage their data efficiently in the cloud.

May 16, 2023
npm is Scam-Spam Cesspool ¦ Google in Microsoft Antitrust Thrust

Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.

This week: The npm registry suffers spam infestation, and Microsoft makes Google sad. (more…)

March 31, 2023