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Category: IT Administration

AWS AI Agent Surfaces Recommendations to Optimize Cloud Computing Environments

Amazon Web Services (AWS) today made available a preview of an artificial intelligence (AI) agent that surfaces recommendations to optimize cost, security, performance and resilience based on the business objectives an organization defines.

Jill Fariss, vice president of AWS Support, said the AWS Well-Architected Agent generates code that can be implemented via a command line interface (CLI), an infrastructure-as-code tool or some type of runbook automation.

The overall goal is not to eliminate the need for humans to manage IT infrastructure but rather make it simpler for engineers and IT administrators to manage workloads at much higher levels of scale, added Fariss. That capability is critical because most organizations today simply don’t have enough IT staff to effectively manage and optimize the workloads they currently have deployed, an issue that is only going to be further exacerbated as advances in AI make it possible to build and deploy even more applications, she noted.

The AWS Well-Architected Agent is designed to function much like a cloud architect that continuously correlates metrics, resource configurations, and application topology that are then analyzed using a set of best practices that AWS has defined across more than 65 of its services. Based on the goals an organization might have, it will then surface recommendations that highlight the tradeoffs that might need to be made to achieve one goal versus another, said Fariss.

In addition to presenting those consolidated findings, the AWS Well-Architected Agent also provides step-by-step remediation guidance and, when applicable, the costs that might be incurred. DevOps engineers or an IT administrator can define the business goals they are trying to meet or, alternatively, opt to run AWS Well-Architected Agent simply to discover issues and suggestions for resolving them.

It’s not clear what the future will hold for DevOps engineers and IT administrators in the AI era, but the only thing that is certain is the level of expertise required to manage complex IT environments will continue to steadily decline. In some cases, organizations will opt to rely on smaller teams of engineers to manage workloads at much higher levels of scale. In other cases, organizations may opt to augment IT administrators that typically cost less to hire and retain.

Regardless of approach, each organization will ultimately need to determine the scope of the tasks that it is comfortable with an AI agent automating with little to no intervention on the part of an IT professional necessarily being required. For now, however, AI agents that are deployed to optimize IT operations still need to establish a level of trust, said Fariss.

IT teams, ultimately, should expect the number of AI agents being made available by providers of IT infrastructure to only increase in both number and capabilities. The challenge and the opportunity then becomes determining precisely where the separation of duties and responsibilities between various AI agents and the humans that supervise them is going to lie not just today but, more importantly, a few short months from now as the capabilities of AI agents continue to expand and evolve.

October 1, 2026
Red Hat Previews AI Agent Integration with Ansible Automation Platform

Red Hat today revealed it is extending the reach of its Ansible Automation Platform for IT operations to artificial intelligence (AI) agents, in addition to making it simpler to build AI agents using existing application development tools.

Announced at the Red Hat Summit conference, version 2.7 of the Ansible Automation Platform adds a technology preview of an orchestration engine for AI agents that are able to invoke capabilities via an integrated Model Context Protocol (MCP) server.

Sathish Balakrishnan, vice president and general manager for Ansible at Red Hat, said these capabilities provide AI agents with a trusted execution layer through which they can automate IT operations. The overall goal is to make new and existing libraries of automation playbooks available to AI agents in a way that can be governed using a set of policies enforced via the Red Hat Ansible Automation Platform, he added.

As part of that effort, the Red Hat Ansible Automation Platform can now serve as an OpenID Connect (OIDC) authentication provider for HashiCorp Vault, which is provided by Red Hat’s sister subsidiary of IBM. That capability makes it possible to issue short-lived, job-specific tokens for event-driven tasks to reduce potential risks.

At the same time, Red Hat announced that a version of its Red Hat Desktop tool for building applications now includes an instance of the Red Hat Podman tool for building and deploying containers that can be used to isolate AI agents in a sandbox environment.

Additionally, Red Hat has enhanced Red Hat Advanced Developer Suite to include a set of Red Hat Trusted Libraries and a set of AI tools to determine if known vulnerabilities in generated code are relevant to a specific application runtime.

Red Hat also announced the general availability of Red Hat Hardened Images to provide application developers with a set of more secure containers for building cloud-native applications, along with a Technical Supportability Review capability for Red Hat with AI that transforms manual environmental audits into a set of automated, self-service function that can validate more than 600 touchpoints.

Finally, Red Hat also announced it is making available a Red Hat Enterprise Linux Long-Life Add-On option for organizations that need support for longer than three years.

It’s not clear to what degree DevOps teams are adding AI agents to workflows, but it’s now more a question of what degree of trust they will place in them rather than if. The ultimate proof in the proverbial pudding will be determining to what degree AI agents will enable software engineers to reliably automate workflows at scale across increasingly complex application environments that have large numbers of interdependencies.

Ultimately, a small army of AI agents that are supervised by software engineers should enable organizations to deploy more applications than ever at higher levels of scale. The challenge, of course, will be ensuring that the AI agents confine their activities to the narrow set of tasks they have been assigned versus, for example, deleting a production database for one unexplained reason or another.

May 12, 2026
Why Senior Engineers Still Do Manual Work in Highly Automated Environments
Automation has been part of enterprise IT for many years, and in many environments, it has grown into an extensive network of interdependent workflows that keep routine operations running smoothly.

Scripts provision accounts, automated workflows manage cloud resources, orchestration tools coordinate ITSM processes, and AI-driven tools help employees across the organization complete tasks more efficiently.

On paper, this level of automation should allow the most experienced engineers to spend less time on routine operational work and more time on architecture, optimization, and long-term improvements.

In practice, however, many teams experience the opposite. Even in highly automated environments, senior engineers are frequently pulled back into day-to-day operational tasks. They are asked to rerun failed jobs, correct permissions, verify provisioning results, or investigate why an automated workflow behaved differently than expected. Instead of focusing on higher-value work, they become the people responsible for keeping the automation running when something goes wrong.

As automation architectures grow larger and more complex, they can also become harder to predict and more prone to failure. When execution is inconsistent or difficult to troubleshoot, the most experienced engineers inevitably become the safety net. At that point, the productivity gains automation was meant to deliver begin to erode, and the workload shifts back onto the very people it was supposed to free up.

If Automation Grows Without Control, Consistency Suffers

Most automated environments are not designed all at once. Instead, they evolve over time as different teams solve different problems in different ways. Each new script or workflow improves a specific process, and each addition makes the environment more capable than before.

As the number of automation assets increases, however, consistency often begins to break down. In organically grown environments, scripts may run from different servers, under different user accounts, and with different approaches to permissions, credential management, and logging.

This lack of consistency creates uncertainty:
- A script that works perfectly in one workflow may start to fail when it comes into conflict with a newly created script in another.
- Tasks that succeed during testing may behave differently in production, where dependencies are more complex and systems are more tightly connected.
- Without enforceable permission policies, with credentials embedded directly in scripts, and with logging spread across multiple tools, automation can also introduce security and compliance risks that are unacceptable in an enterprise environment.
These issues are common when automation grows organically rather than as part of a centrally planned architecture.

When something fails in this kind of environment, diagnosing the problem can take significant time. Engineers must determine which workflow ran, what the expected outcome was, which dependencies were involved, and whether permissions or credentials behaved differently than expected.

In these situations, automation no longer reduces complexity as intended. Instead, it creates an environment that demands constant attention, where skilled engineers must step in regularly just to keep automated processes running as they should.

When Automation Becomes Unpredictable, Expertise Becomes a Bottleneck

When automation cannot be relied upon to execute consistently, responsibility naturally shifts upward. Less experienced administrators are often reluctant to run processes they do not fully understand, particularly when those processes affect critical systems. As a result, workflows that should be routine are frequently escalated to senior engineers, who are trusted to diagnose issues quickly and minimise risk.

Over time, this creates a productivity bottleneck that begins to outweigh the intended productivity return on automation. Senior engineers spend less time designing improvements and more time maintaining and correcting existing workflows. Projects slow down, innovation is delayed, and the organisation’s most skilled resources remain tied up in operational work.

This dynamic becomes even more pronounced with the introduction of agentic automation. Autonomous processes can execute actions far more frequently and dynamically than traditional workflows. When execution is not fully controlled, any inconsistency is amplified, increasing both the volume and the impact of failures. In such cases, automation can place a greater strain on operations than manual processes ever did.

Automation is meant to reduce reliance on expert intervention. Without consistent and predictable execution, however, it can produce the opposite outcome, making experienced engineers more essential to routine operations, not less.

Productivity Improves Only When Automation Becomes Predictable

For automation to reduce operational effort, it must behave consistently every time it runs. The same action should produce the same outcome regardless of who initiates it, which system triggers it, or when it executes. Permissions should not depend on individual user accounts, credentials should never be embedded in scripts, and logging should not be fragmented across multiple tools.

Equally important is the ability to delegate automation safely. In many organizations, automation remains the domain of senior engineers not because it is inherently complex, but because the surrounding tools and processes are difficult to use securely. Interfaces are often unintuitive, configuration is buried within scripts, and security models are too fragile to allow broader access without introducing risk.

For automation to scale effectively, setup, deployment, and monitoring must be accessible through clear and intuitive interfaces, rather than requiring deep technical knowledge of underlying code. At the same time, credentials must be handled in a way that keeps them fully protected. Sensitive information should never be hardcoded or shared between individuals, but instead stored securely and applied automatically during execution.

When scripts and workflows can be reused and shared without exposing credentials, teams gain the confidence to delegate tasks more widely. This reduces dependency on senior engineers, lowers the risk of errors, and ensures that automation remains secure even as adoption increases.

Achieving this level of consistency and control requires a structured execution model that operates beneath the workflows themselves, ensuring that every action runs under defined, repeatable conditions.

How ScriptRunner Keeps Engineers Focused on High-Value Work

ScriptRunner provides the controlled execution layer that allows automation to run consistently across complex Microsoft environments. Instead of scripts executing from multiple tools, servers, and user accounts, all automation actions are routed through a centralized, policy-driven platform.

This ensures that every task follows the same rules, regardless of where it is created or triggered:
- Permissions are assigned through roles and policies, so execution does not depend on individual administrator privileges.
- Credentials are stored securely in a central vault and applied automatically when workflows run.
- Scripts can be reused across service management, orchestration, monitoring, and AI-driven processes without needing to be rewritten for each context.
- Every execution is logged centrally, creating a clear audit trail that simplifies troubleshooting, auditing, and compliance.
With execution standardized, automation becomes something teams can rely on rather than something they need to supervise. Routine operational work can be delegated with confidence, escalations become less frequent, and experienced engineers no longer need to intervene simply to ensure automation behaves correctly.

The result is an environment where skilled staff can focus on architecture, optimization, and innovation instead of spending their time on operational corrections. When senior engineers are no longer required to act as a safety net for routine processes, that’s the point when productivity skyrockets.
May 11, 2026
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April 17, 2024
Edge Computing Requires DevOps at Scale

The rise of edge computing is about to drive a long-overdue convergence of DevOps, data engineering, security, networking, operational technology (OT) and machine learning operations (MLOps) best practices, which will ultimately make IT teams more responsive than ever to the needs of the business.

Historically, each of these IT disciplines has tended to work in isolation, with deliberate handoffs being made between the various teams employed to provision and maintain various services. The challenge is that as more applications are deployed at the network edge, the need for IT teams to work hand-in-glove is crucial. Applications not only need to be deployed across a much more distributed computing environment, but they also need to be regularly updated and patched.

At the same time, increasing amounts of data are being processed and analyzed at the point where it is collected and consumed. More stateful applications running at the network edge are passing aggregated analytics data back to applications running in the cloud or on-premises IT environment. That shift requires a fundamentally different approach to how storage is managed across multiple edge computing environments and the various federated backend systems spanning the enterprise.

Organizations will typically need to store files locally on edge computing platforms while also storing copies of those files in the cloud using an S3-compatible storage service that provides access to inexpensive object-based storage to enable everything from data protection to driving artificial intelligence (AI) applications. That multi-protocol capability creates an opportunity to not just build new stateful distributed applications but also modernize existing applications regardless of what protocols are being used to store data.

Most edge computing platforms today are managed by OT teams that report directly to the leaders of specific business units. However, as more of these platforms are connected to networks, the greater the imperative to centrally manage them as part of any effort to lower the total cost of IT. After all, the most expensive element of IT remains the cost of the labor still required to manage it.

Naturally, sharing data in near real-time across a distributed computing environment puts more pressure on network operations (NetOps) teams. Instead of updating applications in batch mode once a day, data continuously streaming from thousands of edge computing platforms needs to be synchronized with multiple applications to surface the latest, most accurate insights.

More challenging still is that much of that data is being used to train AI models that ultimately require the associated inference engine created to be deployed at the edge. To process terabytes of data, the underlying platform required to support those inference engines also will need to be measured in terabytes. Over time, AI models tend to drift or, in the case of generative AI, outright hallucinate, over time. Data scientists will need to work closely with DevOps teams to replace those AI models because, unlike a traditional application, they can’t be updated via a patch. The entire AI model needs to be replaced with one that is not just more reliable but also—and just as importantly—safer.

Finally, the need to secure edge computing platform is nothing short of critical. Cybercriminals tend to view these platforms as gateways to the rest of the enterprise. Each new edge computing platform deployed extends the size of the attack surface that needs to be defended. Unless cybersecurity teams are deeply involved in building and deploying edge computing applications and platforms, it’s only a question of when, rather than if, a breach will occur.

It may be a while before these various IT fiefdoms converge, but at this point, it is all but inevitable. In fact, it will soon be difficult to distinguish between edge computing and the rest of the IT environment as event-driven applications spanning multiple platforms become more the norm than the exception.

DevOps teams, as part of their relentless commitment to automation, should naturally be at the forefront of these efforts. Rather than confining DevOps principles to how applications are built and deployed, the time has clearly come to automate IT workflows on an end-to-end basis. The only way to programmatically manage edge computing at scale is to also apply DevOps principles at scale. Also known as platform engineering, that approach enables organizations to manage distributed IT environments at scale without requiring organizations to hire a small army of IT teams to manage it all. There will always be a need for specialized expertise, but as IT continues to evolve the siloes that slow down the pace of innovation will need to come down.

Not every member of an IT organization needs to know how to expertly manage every task, but they should be familiar with the inherent dependencies that exist between applications, networking, storage and cybersecurity to advance the goals of businesses that have never been more dependent on IT.

Each organization needs to determine the pace of convergence that best suits its needs, but the faster it occurs, the greater the return on the investment in edge computing will become. The challenge and the opportunity, as always, will be managing yet another major IT transition, where the issues that need to be resolved are as much cultural as they are technical.

March 22, 2024
Controlling Cloud Costs With Efficient Ramp Plans

A mix of rapid migrations to the cloud and complexity over usage and costs has companies desperately trying to reduce waste and gain control over their spending. Making matters worse is a volatile economy that has decision-makers on edge, considering cost-cutting measures and placing a priority on locking down revenue.

Without a formal FinOps or cost optimization strategy in place, it’s easy for companies to see cloud expenses snowball out of control. In fact, analysts from Gartner report that organizations that do not employ cloud cost optimization can overspend by up to 70%. That said, ramp plans are becoming a vital tool for containing cloud resources.

Still, while this strategy can show how to effectively grow your environment and increase workloads, the way a ramp plan is carried out is key to its success. The following looks at what companies should do to succeed with ramp planning accompanied by some strategic advice for handling unique needs that may arise.

Ramping Up

A ramp plan, at its most rudimentary level, is data tracking toward how your organization consumes cloud resources versus what it commits to with a cloud service provider (CSP) or reseller. By committing to spend a set dollar amount, you can get healthy discounts or other incentives. As an example, a 50% increase in consumption by the end of the year might earn a 10% discount from a CSP on compute workloads. But there is risk: A company is still obligated to pay that figure, even if it uses far fewer resources than it committed to.

Still, just as you don’t want to spend money needlessly, CSPs—even high-volume hyperscalers like Amazon AWS and Google GCP—would rather see customers get what they need and grow their footprint. For some organizations, the answer is bringing in a partner to help forecast spend. Questions regarding what is needed, past usage, anticipated growth, potential for acquisitions and more need to be answered. You also have to examine such things as product and service offerings. For instance, a subscription app you offer could catch fire and bring in customers—but you’ll need to plan accordingly so the resources will be in place to meet this demand.

Compiling and projecting this all isn’t easy, but this type of data is needed so you can develop a ramp plan and budget effectively. In the past, this process required time-consuming manual research, a host of spreadsheets and sophisticated algorithms. However, technology has been developed to automate, drive and scale this process. For some businesses, the ideal is to find a partner with this capability in-house and a depth of expertise to use it correctly.

Processes and Partners

Ideally, a ramp plan should be developed before a commitment is made. It should be reviewed on at least a quarterly basis with the C-suite and appropriate technical executives and then revised as necessary. On the whole, CSPs like to sign customers to contracts extending more than a year, with three-year terms being typical.

Working with a third-party partner, reseller or directly with a hyperscaler comes down to personal preference. Big companies, much like hyperscalers, tend to have a lot of bureaucratic processes and requirements. So, if you want a tactical agreement with AWS, you may have to buy tactical support. You may get a good financial incentive in exchange, but the cost of enterprise-level support could offset that.

Partners and resellers of cloud services, on the other hand, help hyperscalers scale their businesses, so they often have special arrangements that lower support costs. The development of ramp plans is usually less when going through a third party versus registering with a CSP. These services are often designed for smaller companies that larger CSPs don’t have time to handle. Third parties tend to provide tailored services more in tune with customer needs, including more flexible usage, optimization and security. These partners tend to negotiate more effectively than customers themselves because they better understand the ecosystem and which levers to pull.

Measuring Success

It’s not easy to measure and track the success of a ramp plan. Beyond understanding and meeting your commitment—and getting the most from cloud spend—the best measure is profitability. Are you just eating costs or acquiring new customers? Is your efficiency boosting or lowering your margins? It’s even possible to apply unit economics to assess if you’re making money on the resources being used.

Making a commitment and ramp planning should involve more than financial incentives. Maybe there’s some other strategic aspect you want to achieve? From service-level agreements to support, a lot of things can be woven into a commercial contract. Benefits can come in various forms, but when they’re added together, you can take success in the cloud to a whole new level.

March 14, 2024
AWS Cost Management in 2024

AWS cost management involves strategies and practices aimed at controlling and optimizing the expenses associated with using Amazon Web Services (AWS). As businesses scale and diversify their use of AWS services, it becomes crucial to manage costs to ensure cloud expenditures remain aligned with business objectives and budgets. Effective cost management in AWS encompasses understanding billing and account structures, monitoring usage and costs in real-time and making informed decisions about resource allocation and deployment.

At the core of AWS cost management is the ability to visualize and analyze cloud spending. AWS provides detailed billing reports and cost management tools that enable users to track their usage patterns, identify trends and pinpoint areas of inefficiency. Many organizations use third-party tools to get more detailed insights and savings recommendations. This data-driven approach allows organizations to adjust their cloud usage to avoid unnecessary costs, such as by shutting down idle resources or choosing more cost-effective service options.

Furthermore, cost optimization techniques, such as selecting the appropriate pricing models (e.g., Reserved Instances, Savings Plans), optimizing resource utilization (e.g., scaling resources to match demand) and applying cost allocation tags for granular tracking, are critical components of AWS cost management. These practices help businesses allocate cloud costs accurately across departments or projects, enabling better planning, budgeting and accountability.

The Importance of Cloud Cost Optimization for DevOps Teams

Cloud cost optimization is crucial for DevOps teams to ensure efficient resource utilization and cost-effectiveness in cloud environments. As cloud infrastructure becomes more complex and dynamic, DevOps teams face the challenge of balancing performance, scalability and cost. By adopting cost optimization strategies, teams can identify wasteful spending, optimize resource allocation and ensure that cloud investments align with business goals and operational requirements.

For DevOps teams, cloud cost optimization involves continuous monitoring, analysis and adjustment of cloud resources. This iterative process allows for the fine-tuning of systems to achieve optimal performance without overspending. Techniques such as auto-scaling, choosing the right instance types and employing cost-effective cloud services are part of this optimization.

Additionally, implementing budgeting controls and cost alerts helps prevent unexpected expenses, enabling teams to allocate resources more efficiently and respond quickly to changes in demand. Through effective cost optimization, DevOps teams can contribute to the overall financial health of an organization by maximizing the value of cloud investments while supporting rapid development and deployment cycles.

AWS Cost Management Best Practices in 2024

Cloud cost management is evolving. Here are some of the best practices that will become more important in 2024 and beyond.

Adopt Serverless Architectures Where Appropriate

Serverless architectures, also known as function-as-a-service (FaaS), allow developers to build and run applications without having to manage any servers. This means you only pay for what you use, which can significantly decrease costs when compared to traditional server-based architectures.

In addition, serverless architectures can scale automatically to meet demand, eliminating the need for manual intervention. This can free up valuable time for your IT team to focus on other important tasks. Furthermore, serverless architectures are highly flexible, allowing for rapid development and deployment of new features and applications. This can significantly reduce time-to-market, which can provide a competitive advantage in today’s fast-paced digital economy.

Utilize Spot Instances for Flexible Workloads

Another effective strategy for AWS cost management is to utilize Spot Instances for flexible workloads. Spot Instances are spare AWS EC2 instances that are available at up to 90% off the On-Demand price. This means you can significantly reduce your AWS costs by strategically bidding on and using Spot Instances when demand is low.

However, it’s important to note that Spot Instances are not suitable for all workloads. They are best suited for flexible, interruptible workloads that can withstand occasional downtime. This includes tasks such as batch processing, data analysis and non-critical background tasks.

In addition, it’s crucial to have a robust strategy in place to handle Spot Instance interruptions. This can include setting up Auto Scaling Groups, using Spot Fleet or leveraging third-party tools. By effectively managing Spot Instances, you can significantly reduce your AWS costs while maintaining operational efficiency.

Implement FinOps Principles

FinOps, or cloud financial management, is a set of principles and best practices designed to help organizations manage and optimize their cloud costs. Implementing FinOps principles is a critical component of AWS cost management in 2024.

FinOps involves three key principles: Visibility, optimization and governance. Visibility means having a clear understanding of your cloud usage and costs. This can be achieved through comprehensive tagging, detailed cost reporting and regular cost reviews.

Optimization involves ensuring that you’re using your cloud resources efficiently. This can involve right-sizing your instances, leveraging savings plans and reserved instances and automating your resource management.

Governance involves setting up policies and procedures to manage your cloud costs. This can include setting up budget alerts, implementing cost approval workflows and regularly auditing your cloud usage and costs.

Leverage AI for Cost Optimization

Leveraging artificial intelligence (AI) for cost optimization is a growing trend in AWS cost management. AI can analyze vast amounts of data quickly and accurately, providing valuable insights into your cloud usage and costs.

For example, AI can identify inefficiencies in your cloud infrastructure, such as underutilized instances, unnecessary data storage, or expensive data transfer. By addressing these inefficiencies, you can significantly reduce your AWS costs.

In addition, AI can provide predictive analytics, allowing you to forecast future cloud usage and costs. This can help you plan your budget more accurately and make informed decisions about resource allocation.

Use Sustainable Computing to Reduce Costs

Sustainable computing, or green computing, is the practice of using computing resources efficiently while minimizing environmental impact. By adopting sustainable computing practices, you can not only reduce your environmental footprint, but also reduce your AWS costs.

One strategy is to use energy-efficient instances, such as those powered by the AWS Graviton2 processor. These instances deliver superior performance while consuming less power, resulting in lower costs.

Another strategy is to utilize AWS’s carbon footprint tool, which can help you measure the carbon emissions associated with your AWS usage. By reducing your carbon emissions, you can reduce your environmental impact and potentially qualify for carbon credits, which can offset your AWS costs.

In conclusion, AWS cost management in 2024 requires a comprehensive approach that includes adopting serverless architectures, utilizing Spot Instances, implementing FinOps principles, leveraging AI and using sustainable computing. By following these best practices, you can effectively manage your AWS costs, optimize your cloud infrastructure and ensure the maximum return on your cloud investments.

February 23, 2024
IT Professionals Have Mixed Emotions About AI

A survey of 1,213 IT decision-makers working for small-to-medium enterprises (SMEs) in the U.S., United Kingdom and India published today found nearly half (45%) are worried about the impact artificial intelligence (AI) will have on their job.

Conducted by the research firm Propeller Insights on behalf of JumpCloud, a provider of an IT and cybersecurity management platform, the survey also found more than three quarters (76%) agree their organizations should be investing in AI, with 79% viewing AI as a net positive for their organization.

Most respondents also agree their organizations are approaching AI at exactly the right speed (55%), compared to 22% who said their organization is moving too quickly and 19% who said their organization is moving too slowly.

However, a full 62% also noted that AI is outpacing their organization’s ability to protect against threats, the survey finds.

Tom Bridge, a principal product manager for JumpCloud, said while there is no doubt that AI will change roles within organizations, it’s not likely to replace the need for IT administrators. Instead, IT administrators will be able to manage complex IT environments at much higher levels of scale as AI becomes a force multiplier for managing IT. Many of the low-level tasks that conspire to make managing IT tedious, such as generating reports, will become increasingly automated, noted Bridge.

It’s not clear what impact AI will have on IT roles, but it is arriving at a time when IT organizations are being challenged on several fronts. For example, the survey finds that 56% of respondents are more concerned about their organization’s security posture than they were a year ago. Nearly three-quarters (72%) said any cuts to their security budget would increase organizational risk.

More than half (56%) identified security as their biggest IT challenge, followed by new services and application rollouts (45%), increased work burden (44%) and the cost of remote work solutions (42%). Respondents said network attacks were their biggest challenge (40%), followed by software vulnerability exploits (34%) and ransomware (29%). A full 83% also now require multi-factor authentication (MFA), while two-thirds require biometrics (66%) to access some portion of their IT resources. A total of 83% are also still managing passwords.

More than three quarters (76%) also report increased compliance and regulations requirements in their region.

In addition, a full 88% noted increased vendor prices over the last six months, the survey finds. More than a third of respondents need five to 10 applications to manage worker life cycle (37%), but 22% require 11 or more. Three-quarters (75%) said they would prefer a single tool to do their job over several point solutions.

More than three-quarters (76%) rely on a managed service provider (MSP) to manage some portion of their IT environment, with 42% now relying on an MSP to manage their IT environment completely.

Finally, 57% said their organization has experienced layoffs in the last year. In addition to having to offboard those employees, IT professionals will naturally become more uneasy about their own job security.

Arguably, when it comes to managing IT, there is less certainty today than at any time in recent memory. The challenge, as always, is to aggressively embrace automation as much as possible to reduce a level of IT complexity that is rapidly becoming too complex to manage otherwise.

February 14, 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
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

SimplyHired.com

D2 Consulting
Springfield, Virginia
DevOps Engineer
$150,000 to $180,000

Indeed.com

Mariner Finance
Remote
DevOps Engineer
$120,000 to $150,000

ZipRecruiter

Cogility Software
Irvine, California
DevOps Engineer
$110,000 to $140,000

CareerBuilder.com

BAE Systems
Manassas, Virginia
DevOps Engineer
$92,290 to $156,860

Dice.com

Rackner
Falls Church, Virginia
Software DevOps Engineer
$90,000 to $140,000

January 29, 2024