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Tag: cloud cost management

  • Survey: Much Greater Focus on Reining in IT Costs

    Survey: Much Greater Focus on Reining in IT Costs

    A survey from Vega Cloud found 62% of respondents are more closely monitoring all IT costs, with more than half (52%) more closely evaluating their cloud service provider options.

    The survey polled 100 vice presidents from organizations with more than 1,000 employees and was published today by Vega Cloud, a provider of a platform for managing multiple clouds.

    Overall, nearly half of respondents (48%) said they expected to reduce their IT spending a little or a lot in 2023, with 65% identifying the reduction of cloud spending as a specific goal. Three quarters (75%) are looking to cut cloud costs by 10-50% in 2023, the survey found.

    A full 93% reported they have invested at least a fair amount of money on tools to optimize cloud costs, with 82% having invested in FinOps staff or people with FinOps experience. The challenge is that 70% of respondents said it was difficult to find people with these skill sets, the survey found.

    The best way for organizations to reduce cloud costs is to become more informed about what they’re spending (40%), and 33% said that the best approach is to consistently optimize use of cloud resources. A total of 61% identified reserved instances/savings plans as the best way to optimize cloud use, while 55% said optimization was best achieved through waste elimination followed closely by right-sizing (54%).

    Vega Cloud CEO Kris Bliesner said it’s clear the days when organizations allowed developers to consume cloud resources at will are coming to an end as cloud services consume a much larger percentage of the IT budget. In fact, the survey noted that 91% of respondents are spending at least one million dollars annually on cloud infrastructure alone.

    The issue is that finance departments not only want to reduce cloud spend but also want cloud spending to become more predictable from a budget perspective. Nearly half of survey respondents (45%) said they’d received a surprise in their cloud bills “a number of times” over the past 12 months, with 70% of respondents noting surprises have happened at least a half dozen times in the past year.

    A total of 40% admitted they had exceeded budgets multiple times in the past year, and 38% said they had to modify their forecast cloud spending budget numerous times over the past 12 months. More than half of respondents (55%) said database costs are the metric that matters most when looking at cloud usage followed by bandwidth costs (51%), storage costs (49%) and compute costs (47%). Nearly two-thirds (64%) said they track metrics at least somewhat often, while 46% said they have somewhat optimized their cloud use. A total of 11% said they’re not optimized at all.

    A full 88% said they’re using artificial intelligence (AI) at least somewhat to optimize cloud use and costs, with nearly three-quarters (74%) noting they think ChatGPT or a similar generative AI tool could be used to optimize cloud use/costs further. Specifically, 42% said they thought it could improve decision making and 30% said it could automate decision making.

    Among the cloud service providers, Google is cited as doing the most to help customers manage and reduce cloud costs (38%), followed by Amazon Web Services (34%) and Microsoft (28%)—but a full 85% said they could all be doing more.

    Of course, changing economic conditions are naturally driving organizations to look harder at cloud spending. Well over half of respondents (56%) said they are concerned about the economy, and 95% of those respondents said those concerns have, at least somewhat, impacted their IT strategy.

    It’s not clear whether organizations will continue to exercise greater control over cloud spending should the economy recover. For now, at least, there’s clearly more desire for control than ever.

  • Vega Cloud Commits to Reducing Cloud Costs

    Vega Cloud Commits to Reducing Cloud Costs

    Vega Cloud today made a commitment to save customers at least 10% on their cloud infrastructure spending or it won’t charge for its software. Vega Cloud’s software automates cloud infrastructure management.

    The Vega Cloud platform enables IT teams to connect, automate, manage and optimize new and existing networks, applications and other multi-cloud IT environments from a single console. As part of those capabilities, the company includes cost allocation and cost identification tools using a curated set of data to surface daily recommendations for cost improvements.

    Vega Cloud CEO Kris Bliesner said the goal is to provide a financial operations (FinOps) capability within the context of a platform capable of acting on those recommendations that, on average, reduces cloud costs by 15% to 25%.

    During uncertain economic times, IT leaders are under more pressure than ever to reduce costs. FinOps has emerged as a discipline for applying a set of best practices for specifically reducing cloud costs. In addition to reducing cloud costs, Vega Cloud is also making a case for reducing the total cost of IT by relying on a single automation framework to manage multiple clouds.

    In the cloud era, many organizations have found themselves employing multiple clouds, each of which requires a separate set of tools to manage. IT teams then need to hire additional staff to manage each of those tools. As a result, the total cost of IT increases with each additional platform added to the overall IT estate.

    Many organizations, of course, have allowed the number of platforms they manage to proliferate. This is because many application deployment decisions were driven by developers who can easily provision cloud infrastructure on their own. However, as concerns over the cost of cloud computing continue to rise, many organizations are revisiting how cloud infrastructure resources are used. Many of those reviews are being driven by finance teams that want cloud costs to become more predictable as cloud computing continues to account for a much larger percentage of the IT budget, noted Bliesner.

    In fact, one of the challenges IT leaders have is finding a way to make sure developers and DevOps teams are more motivated to reduce costs rather than focusing entirely on how fast they can build and deploy applications, he added.

    The reality is the average developer doesn’t need application build infrastructure running 24/7, so application development and testing is a great place to focus on cutting cloud costs initially, noted Bliesner. There’s also plenty of opportunity to better schedule workloads to reduce costs by relying more on lower-cost instances of cloud infrastructure, he said.

    DevOps engineers, by nature, don’t want to be inefficient, so the issue comes down to providing them with the tools needed to identify resources that are being wasted, added Bliesner.

    Most cloud service providers, of course, provide tools that enable IT teams to optimize spending, but Bliesner said those tools don’t go deep enough in terms of exposing issues. As a provider of an independent management framework, Vega Cloud is more committed to providing cost optimization capabilities that can be applied to multiple clouds, he added.

    In general, interest in controlling cloud costs has risen sharply. Vega Cloud has been engaged with more customers researching this issue in the last four weeks than in the last three years, said Bliesner. The challenge—and the opportunity—is to not just cut cloud costs but also identify patterns that make it easier to predict them, noted Bliesner.

    At this juncture, cloud costs are coming down. The only issue left to be determined is whether that goal will be accomplished with precision versus a fiat that adversely impacts an organization’s ability to continue to build and deploy software.

  • Cloud Management Issues Are Coming to a Head

    Cloud Management Issues Are Coming to a Head

    A survey of 1,300 technology and data executives found that more than a decade after the initial rise of cloud computing, organizations are still struggling to manage these environments.

    Conducted by Wakefield Research on behalf of NetApp, the survey found 98% of respondents reported their business has been impacted by everything from the increasing complexity of data and heightened cybersecurity risk to lack of visibility into business operations and staff burnout.

    Top challenges include cybersecurity risk (45%), staff not taking full advantage of business applications (44%), increased skepticism about the cloud from leadership (44%), lack of visibility into business operations (42%), going over budget (37%) and burning out staff (31%).

    A full 88% continue to encounter technical challenges, including data mobility between clouds (33%), security risks (33%) and interoperability between cloud services (30%).

    The top drivers of cloud investments are scaling artificial intelligence (AI) and automation (39%), followed by driving business innovation (35%) and creating digital experiences (34%).

    Organizational challenges encountered include having a clear vision for cloud strategy (32%), establishing the right level of governance (31%) and managing costs (30%). A full 84% are under pressure to show short-term return on investments (ROI) in cloud computing platforms, with 76% noting CFOs and other business leaders are skeptical of savings. Nearly half of the tech executive respondents (49%) reported that when cloud strategy discussions occur, cost concerns come up often or all the time. As a result, nearly three-fourths of executives (74%) said tiered workloads are either very or extremely important, the survey found.

    Other factors impacting multi-cloud strategy include environmental, social and governance (ESG) concerns (79%) and data sovereignty (74%), the survey found.

    Ronen Schwartz, senior vice president and general manager for cloud volumes at NetApp, said as more organizations employ multiple clouds, total costs tend to rise. Each cloud platform requires mastering another control plane, which means someone in the IT organization must have the right skills to manage, he noted. IT leaders are attempting to exercise more control over cloud deployments, but developers, for a variety of reasons, have already deployed workloads across multiple cloud computing environments, added Schwartz. One approach to achieving that goal is to make sure data is stored on the least expensive tiered service possible for any given specific use case, he noted.

    Less clear is whether an uncertain economy might push organizations to consolidate workloads on fewer cloud computing platforms. Even then, the cost of moving a workload is not trivial given the proprietary application programming interfaces (APIs) that many developers have likely invoked as DevOps built and deployed cloud applications.

    Of course, there is no going back; the issue now becomes how to define a set of best practices for managing cloud computing environments in a way that minimizes costs. It turns out that cloud computing can be more costly than an on-premises IT environment simply because the infrastructure resources made available to developers are almost infinite. In the final analysis, however, that can turn out to be too much of a good thing—especially if organizations wind up spending more on IT to support these environments. Many of those environments are not rigorously managed because, for example, developers may not be inclined to spin down a development environment on a Friday because it takes too long to spin it back up the following Monday.

    One way or another, however, a level of long overdue scrutiny of cloud computing costs may be on the horizon.

  • AWS Delivers on Latest Graviton3 Price/Performance Promise

    AWS Delivers on Latest Graviton3 Price/Performance Promise

    Amazon Web Services (AWS) has expanded the number of virtual machines based on the latest generation of its Graviton processors to include instances that support DDR-5 memory.

    The latest Amazon EC2 instance types, dubbed M7g and R7g, that are now generally available are based on the AWS Graviton3 processors developed using an architecture created by Arm. Previously, these instances were only available as part of a technology preview program.

    Rahul Kulkarni, director of product management at AWS, said these latest offerings take advantage of DDR-5 memory to provide 25% better performance than the previous sixth generation of Graviton instances. DDR5 memory increases the amount of bandwidth available by 50%, he noted.

    Graviton processors now provide the lowest-priced instance of virtual machines in addition to the best performance and overall price/performance across the AWS portfolio, Kulkarni added.

    AWS is making a case for employing its AWS Graviton processors for general-purpose workloads as more IT teams are being tasked with reducing costs as the overall number of applications being deployed in the cloud continues to increase. In fact, all of the top Fortune 50 companies have already deployed workloads on Graviton processors, noted Kulkarni.

    While it’s not clear how many applications are being developed to run natively on Arm processors, the process of refactoring existing applications to run on Arm processors is not as intensive for most applications as it was when previously moving applications from one class of processors to another.

    In addition, Kulkarni noted AWS has committed to providing a full range of DevOps services, including a continuous integration/continuous delivery (CI/CD) platform, to help facilitate any transition to Graviton processors.

    There are, of course, multiple strategies for containing cloud costs that range from committing to consuming a specified amount of compute resources annually at discounted rates to relying more on spot instances that are available for limited amounts of time. Regardless of approach, the days when developers were allowed to invoke cloud resources at will appear to have come to an end as enterprise IT organizations become more aware of the total cost of IT. As a result, many of them are adopting financial operations (FinOps) best practices to maximize the utilization of cloud infrastructure resources.

    AWS, like most cloud service providers, gives organizations a wide range of processor options. However, it’s apparent that AWS views Arm processors in the form of Graviton to be the most efficient from both a price and performance perspective. It’s not clear how quickly organizations are embracing Graviton in the cloud given the dominance of x86-based platforms, but it’s apparent there is now a clear alternative for general-purpose workloads. If fact, the more data-intensive those workloads become, the greater the interest in more efficient processor architectures.

    Of course, x86 platforms are not going away anytime soon. But as more Arm-based processors find their way into the enterprise, DevOps workflows will only become more challenging to manage.

  • 5 Technologies Powering Cloud Optimization

    5 Technologies Powering Cloud Optimization

    Cloud optimization is the process of improving the performance, cost-effectiveness, and reliability of cloud computing resources and services. It involves identifying and addressing inefficiencies in cloud architecture, application design and resource utilization, as well as implementing best practices for security, scalability and disaster recovery. 

    The goal of cloud optimization is to maximize the benefits of cloud computing while minimizing the risks and costs associated with it. It is a multi-disciplinary process that may involve several key elements, such as:

    • Capacity planning: Evaluating the current and future resource requirements of your cloud environment and ensuring that the right amount of resources are available to meet your needs.
    • Resource utilization: Monitoring and managing the utilization of cloud resources to ensure that they are used efficiently and effectively.
    • Cost optimization: Minimizing the cost of cloud resources and services by identifying and addressing areas of inefficiency and waste, such as over-provisioning or underutilized resources.
    • Performance optimization: Improving the performance of cloud applications and services by optimizing network bandwidth, storage performance and database performance.
    • Security optimization: Enhancing the security of cloud resources and data by implementing best practices for access control, encryption and threat mitigation.
    • Scalability optimization: Making sure that your cloud environment can scale up or down as needed to meet changing resource demands.
    • Disaster recovery: Establishing a disaster recovery plan to ensure that your cloud environment can quickly recover from failures or outages.

    Why is Cloud Optimization Important?

    Optimization is important for several reasons, including:

    • Cost savings: By optimizing the use of cloud resources, organizations can reduce their cloud computing costs and ensure that they are only paying for what they actually need.
    • Improved performance: By optimizing cloud applications and services, organizations can improve the performance and responsiveness of their systems, leading to a better user experience and increased productivity.
    • Increased reliability: Optimization helps organizations to improve the reliability and availability of their cloud environment, reducing the risk of downtime and ensuring that their systems are always available when they are needed.
    • Better security: By implementing best practices for security, organizations can better protect their cloud resources and data, reducing the risk of data breaches and other security incidents.
    • Scalability: Cloud optimization enables organizations to scale their environment up or down as needed, ensuring that they always have the resources they need to support their growing business.
    • Improved agility: Organizations can be more agile and responsive to changing business needs, enabling them to quickly and effectively respond to new opportunities and challenges.

    5 Technologies Powering Cloud Optimization

    Cloud Cost Management

    Cloud cost management is a critical component of optimization that helps organizations to monitor and manage their cloud spend. The goal is to ensure that organizations are only paying for the cloud resources they actually need and that they are using those resources efficiently.

    Cloud cost management solutions (such as Azure Cost Management or AWS Trusted Advisor) provide detailed cost reports, usage analytics and cost optimization recommendations to help organizations understand their cloud spend and identify areas of inefficiency. For example, these solutions can:

    • Monitor and report on cloud resource usage: By monitoring the usage of resources, cloud cost management solutions can provide detailed cost reports that show how much an organization is spending on each resource, such as compute instances, storage and network traffic.
    • Automate cost optimization: Many cloud cost management solutions can automate cost optimization tasks, such as turning off unused resources or right-sizing instances to reduce waste.
    • Provide cost analysis and forecasting: These solutions can provide detailed cost analysis and forecasting to help organizations understand the impact of changes to their cloud environment and plan for future spending.

    Autoscaling

    Autoscaling is a technology that enables organizations to automatically scale their cloud resources up or down as needed to meet changing demands. The goal of autoscaling is to ensure that organizations always have the right amount of resources to support their workloads while minimizing costs and ensuring that their systems are always available when they are needed.

    Autoscaling works by monitoring the performance and usage of cloud resources, such as compute instances, storage and network traffic, and automatically adjusting the size of those resources to meet changing demand. For example, autoscaling can:

    • Add or remove instances as needed: If demand for a resource increases, autoscaling can automatically add more instances to meet that demand. If demand decreases, autoscaling can automatically remove instances to reduce waste.
    • Optimize resource utilization: Autoscaling can optimize the utilization of cloud resources by right-sizing instances to meet demand, ensuring that resources are not over-provisioned or underutilized.
    • Improve system availability: By automatically adding or removing resources as needed, autoscaling can ensure that systems are always available and responsive, reducing the risk of downtime and improving the user experience.

    API Gateway

    An API gateway is a server that acts as an intermediary between an application and one or more microservices. The API gateway is responsible for request routing, composition and protocol translation, which enables microservices to communicate with each other securely and efficiently.

    An API gateway can help with cloud optimization in several ways:

    • Load balancing: The API gateway can distribute incoming API requests to multiple microservices, improving the overall performance and reliability of the system. It can also provide features such as automatic failover and load balancing, which helps to ensure that the system remains available and responsive even in the face of high traffic volumes or other performance challenges.
    • Resource optimization: By acting as a single entry point for API requests, the API gateway can help to reduce the overall resource usage of the system. For example, it can cache frequently used data and serve it directly to clients without having to go back to the microservices, reducing the load on the underlying system.
    • Monitoring and management: API gateways provide a centralized management interface for the microservices, making it easier to monitor and manage the system as a whole. This includes features such as logging, monitoring and alerting, which can help to identify and resolve performance issues quickly and effectively.

    GitOps Platforms

    GitOps is a paradigm that helps automate application deployment and infrastructure provisioning. It typically involves using Git, an open source version control system, as a single source of truth for declarative infrastructure and applications. 

    GitOps aims to improve the developer experience by letting teams use their familiar software development tools to manage application and infrastructure changes.

    With GitOps, cloud optimization can be achieved by:

    • Automating the deployment process: GitOps automates the deployment process, reducing the risk of human error and speeding up the deployment process. This helps to ensure that resources are deployed consistently and correctly, improving the reliability and performance of the environment.
    • Managing cloud infrastructure as code: With GitOps, cloud infrastructure can be defined and managed as code, making version control and collaborating on configurations easier. This helps to optimize the use of resources and improve the overall efficiency of the cloud environment.
    • Improving collaboration and communication: GitOps enables teams to collaborate and communicate more effectively, reducing the risk of conflicts and delays. With Git, changes can be easily tracked, discussed and approved, improving the speed and quality of deployments.
    • Enforcing governance and compliance: GitOps provides a centralized and auditable record of all changes to the environment, making it easier to enforce governance and compliance requirements. This helps to improve the security and reliability of the cloud environment, reducing the risk of breaches and other security incidents.

    APM

    Cloud application performance management (cloud APM) is the practice of monitoring, analyzing and optimizing the performance of cloud-based applications. The goal is to ensure that these applications are available, responsive and performing optimally to meet the needs of users.

    These solutions provide a range of tools and capabilities for monitoring and optimizing cloud-based applications, including:

    • Performance monitoring: Cloud APM solutions monitor application performance in real-time, providing visibility into performance metrics such as response time, resource utilization and error rates.
    • Root cause analysis: These solutions can help identify the root cause of performance issues by providing detailed performance data and tracing information.
    • Capacity planning: Cloud APM solutions can provide detailed performance data and analytics to help organizations plan and optimize their application deployments.
    • Performance optimization: These solutions can provide recommendations for performance optimization, such as right-sizing cloud resources, reducing resource utilization and improving application design.
    • Continuous performance monitoring: These solutions can continuously monitor application performance, providing real-time visibility and alerting to help organizations quickly identify and resolve performance issues.

    Conclusion

    In conclusion, cloud optimization is critical for organizations looking to maximize the value and minimize the costs of their cloud deployments. By using technologies such as cloud cost management, autoscaling, API gateways, GitOps platforms and APM, organizations can improve availability, performance and efficiency while reducing costs and ensuring that they are always prepared to meet changing business needs. 

    These technologies provide the tools and capabilities necessary to monitor, analyze and optimize cloud-based applications, helping organizations to ensure that their deployments are always delivering the best possible value. By embracing optimization, organizations can stay ahead of the curve and ensure that their cloud deployments are always aligned with their business goals and objectives.

  • Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration

    Optimizing Cloud Costs for DevOps With AI-Assisted Orchestration

    Kubernetes continues to grow as the leading container orchestrator for high-performance, cloud-native DevOps, DevSecOps and site reliability engineering (SRE) use cases. While Kubernetes offers excellent orchestration capabilities, it does not, by itself, assure cloud computing costs are optimized.

    The current state of cloud expenditures for many organizations indicates a lot of waste. So much so that there is a trend to replatform from the cloud to private infrastructures to save money—escalating cloud costs are too high and the deep level of real-time optimizations are too hard and too slow.

    According to an article from VentureBeat, Cloud computing: Cost at scale, “Large enterprises have started exploring cloud repatriation. What is worse, the forecasted trends are that “cloud spending could go up as high as 80% of the cost of the revenue for software companies. Moreover, the $100 billion market value among 50 software companies is lost due to the impact of the cloud on their margins.”

    An article by CAST AI indicated 60% of cloud costs could be eliminated by being smarter about the use of cloud resources and augment Kubernetes with AI tooling, no matter the size of the deployments. Given the large number of human and automated users associated with DevOps, DevSecOps and SRE use cases for enterprise cloud resources, the savings potential can equate to tens of thousands of dollars per month.

    However, typical optimization methods are not fast enough nor smart enough to process the resource changes quickly enough to achieve these optimizations at speed. An AI-assisted Kubernetes orchestration is needed to achieve this.

    Kubernetes Landscape

    According to 2021 State of Cloud Native Development Report, Kubernetes is used by 31% of all backend developers. Currently, 5.6 million developers use Kubernetes. The edge computing category has experienced rapid growth in the adoption of Kubernetes and has the highest usage rates of both containers and Kubernetes at 63%.

    A report from the Cloud Native Computing Foundation (CNCF) indicated that spending on Kubernetes, the primary means of orchestrating containerized applications, was up year-over-year from 2020 to 2021. Of those surveyed, 67% reported an increase of 20% or more over the last 12 months, and 10% spent more than $1 million per month on their deployments. Fewer than 25% accurately predicted how much they’d spend to within 5% of the actual cost. And over 20% could not predict their cloud bill. That’s no way to run an operation.

    Cloud Cost Use Cases for DevOps, DevSecOps and SRE

    DevOps, DevSecOps and SRE have many use cases for orchestrating cloud computing. The list of computing tasks below is only a partial set of computing resources that need to be orchestrated for every value stream.

    • Communication—emails, chats
    • Experimentation, planning and research-related computing tasks
    • Configuration tasks associated with infrastructure-as-a-service, platform-as-a-service, software-as-a-service and serverless.
    • Version and artifact repositories transactions
    • Application and microservice transactions
    • API transactions
    • DevOps administration tasks
    • Developer pre-commit builds and testing
    • Continuous integration commits, builds, scans, tests, and tools
    • Continuous delivery—pre-prod tests and deployment development
    • Continuous deployment automation and deployment strategies
    • Production operation, backups, rollbacks, production testing
    • Replication of hybrid cloud and multi-cloud instances
    • Software repositories, data lake transactions
    • Monitoring, logging, synthetic monitoring
    • Tracing, metrics, collectors, analytics and dashboards
    • Release and value steam management
    • Anti-fragility—Chaos engineering, disaster recovery, fire drills, security red/blue/purple teams
    • Diagnostics, issue reporting and tracking
    • On-call support and retrospectives
    • Documentation and training (computer-based) transactions

    Sources of Waste and Potential Savings

    According to the State of Kubernetes Report: Overprovisioning in Real-Life Containerized Applications, 37% of cloud compute capacity is not used. The primary reasons behind cloud waste are:

    • Lack of visibility into cloud usage and costs
    • Overprovisioning
    • Leaving cloud resources idle
    • Fragmentation of usage between teams and departments

    One of the major drivers of wasted cloud spend is the inability to scale back once application demand drops. By eliminating this sort of overprovisioning, organizations stand to reduce their monthly cloud spend by almost 50%. By adding spot instances to the mix, organizations could cut their cloud spend by 60% on average.

    Complexities Impacting Ability to Optimize

    According to the article Cloud Cost Management for DevOps, “When properly done, cloud cost management can truly save organizations significant amounts of money. But, there is a learning curve and traditional approaches cannot keep pace with the rapidly changing requirements for DevOps, DevSecOps and SRE use cases.”

    Trial and error are needed to identify optimization challenges that DevOps team members can anticipate. Before you can even begin planning around cost optimization, you need an accurate picture of your current infrastructure and spending landscape. Cost attribution cannot be done by simply opening your AWS bill. Cost data from monthly bills is not granular and cannot attribute costs to projects and teams. Furthermore, there are additional expenses that are included in your bill that are not tied to your cost of goods sold, such as research and development expenses.

    Accurately allocating costs requires accurate and disciplined tagging infrastructure. The act of tagging infrastructure requires a fair amount of manual work for each piece of infrastructure in each sub-account. Enforcing tag hygiene can be a challenge of its own.

    Opportunity for an AI-Assisted Kubernetes Orchestrator

    Overcoming the complexity of optimizing cloud compute resource orchestration for the many uses cases for DevOps, DevSecOps and SRE requires a smarter, more responsive approach than traditional approaches that require manual planning and implementation. There is an opportunity to save an average of 60% of cloud spend by using an AI-assisted Kubernetes orchestrator.

    What This Means

    While Kubernetes offers excellent orchestration capabilities, it does not, by itself, assure cloud computing costs are optimized for the many use cases required for DevOps, DevSecOps and SRE. An article by CAST AI indicated 60% of cloud costs could be eliminated by being smarter about the use of cloud resources, no matter the size of the deployments. Given the large number of human and automated users associated with DevOps, DevSecOps and SRE use cases for cloud resources in enterprises, the savings potential can equate to tens of thousands of dollars per month.

    An AI-assisted Kubernetes orchestration capability is needed to process cloud resource changes quickly enough to achieve optimizations at the speed needed for DevOps, DevSecOps and SRE use cases.

    This article explained the landscape of requirements for an AI-assisted Kubernetes orchestrator to optimize cloud costs for DevOps, DevSecOps and SRE use cases. Future articles will discuss a solution for such an orchestrator and detail specific applications of the AI-assisted Kubernetes orchestrator for DevOps, DevSecOps and SRE use cases.

  • EU Data Privacy: Try Again | SkyPilot: Choose Cheapest Cloud | WFH: Better Meetings

    EU Data Privacy: Try Again | SkyPilot: Choose Cheapest Cloud | WFH: Better Meetings

    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: EU-U.S. Data Privacy Framework reemerges, Berkeley brings cloud selection box, and proof that remote work is good work. (more…)

  • Datadog Extends Reach of Integrated DevOps Platform

    Datadog Extends Reach of Integrated DevOps Platform

    At its Dash 2022 conference, Datadog announced today it is extending the reach of its namesake cloud-delivered monitoring and observability platform to address continuous testing, application security and cost management.

    In addition, Datadog has made available in beta a Data Streams Monitoring tool that makes it simpler to identify upstream issues that are likely to impact DevOps workflows. Also in beta is a Dynamic Instrumentation tool that makes it possible to employ Datadog agent software to collect data on the fly from specific source code to investigate performance issues. Datadog is also making available in beta a Resource Catalog to make it easier to navigate cloud resources that IT teams are employing and has added a Datadog Workflows tool to create blueprints to for automating repeatable processes.

    Datadog is also expanding the reach of its Watchdog telemetry analysis service infused with machine learning algorithms to add beta support for containers. There is also now a beta of an Event Management tool that DevOps teams can employ to more easily investigate a stream of related alerts using the Watchdog service.

    The Datadog Continuous Testing service, now generally available, provides DevOps teams with a workbench for generating and maintaining tests that can be integrated within continuous integration (CI) workflows. Datadog has also added an Intelligent Test Runner tool, in beta, to enable DevOps teams to only run tests that need to run when a change is made by analyzing code execution paths to increase developer productivity.

    Renaud Boutet, senior vice president of product at Datadog, said the goal is to make it possible for DevOps teams to create and run tests from within the Datadog user interface in parallel using a set of no-code tools without having to employ scripts. The capability will make it possible to automatically generate tests using data collected by the Datadog platform as part of an infinite DevOps workflow loop, he added.

    At the same time, Datadog is making generally available a cloud security management service that combines its existing cloud security posture management (CSPM) service and the cloud workload security (CWS) service to provide an integrated cloud-native application platform (CNAP) at no additional cost. In addition, Datadog is making it possible to block access to application components that are under attack or might be attacked in real-time based on the known vulnerabilities detected using tools that are currently in beta.

    That approach reduces the total cost of cybersecurity by providing IT teams with a single service through which they can manage application security using a platform Datadog gained last year with the acquisition of Sqreen.

    Finally, Datadog is making generally available a cloud cost management tool that leverages its monitoring capabilities to enable organizations to automatically attribute spend to applications, services and individual teams. Changes to spending patterns that impact costs are surfaced alongside the same tools IT teams are already using to monitor their cloud computing environment. That unified visibility increases awareness of costs in a way that encourages DevOps teams to optimize the utilization of multiple classes of cloud resources.

    The services being added are extensions of a Datadog platform that employs a single set of agents it provides. The Datadog platform also supports the open source OpenTelemetry agent to deliver a wide range of services. That approach eliminates the need for IT teams to deploy multiple agents each time they add an additional monitoring, security, testing or cost management tool.

    Datadog has been making a case for an integrated platform that reduces the level of integration effort DevOps teams would otherwise need to expend when deploying separate point products to address monitoring, observability, security and cost management issues. That approach makes it easier for DevOps teams to collaborate and reduces the total cost of DevOps. In contrast, each time a DevOps teams adds a point product to address a specific issue, they need to deploy and maintain a separate set of agents.

    In the longer term, it’s not clear how problematic that agent issue will be once open source OpenTelemetry agent software is more widely employed. However, it may still be years before open source software provides the same capabilities that are available today using proprietary agent software.

    It may take some time before every application is as fully instrumented as it should be, but as monitoring and observability platforms continue to evolve, it is possible to more proactively manage application environments.

  • More Choices, More Problems: The Double-Edged Sword of Multi-Cloud

    More Choices, More Problems: The Double-Edged Sword of Multi-Cloud

    Multi-cloud environments can be a double-edged sword. On the one hand, there are clear benefits to using whatever cloud or tool is best for your particular needs, regardless of vendor – in theory. In practice, however, the more choices you have, the more complex and expensive your environment becomes. In fact, Andreessen Horowitz postulates that multi-cloud architectures are oftentimes costing enterprises more than if they had stayed with on-premises systems.

    Enterprises need a multi-cloud environment to compete in such a rapidly expanding digital economy. For the majority of companies, completely repatriating to on-prem isn’t an option–the train has already left that station for most. But what do you do when the costs of staying competitive become more and more prohibitive, ironically making you less competitive?

    Hybrid Clouds Became Multi-Cloud

    When hybrid cloud–a mix of public cloud services and on-prem systems–became table stakes for enterprises, companies only needed one public cloud service provider. Most put all of their eggs in the AWS basket, splitting their workloads between AWS public cloud services and on-premises systems.

    This setup provided more agility and flexibility to enterprises and lowered costs by reducing dependence on expensive, cumbersome on-prem hardware. A lot of capex was replaced by opex; with cloud services, you only pay for what you use as you use it without the physical infrastructure and maintenance costs.

    Inevitably, the cost and efficiency benefits of the cloud drove companies to migrate more and larger workloads from their on-premises systems, increasing cloud costs over time. And the more workloads you had with a single vendor, the more beholden to that vendor you became. If AWS raised their prices, you had no alternative but to pay more.

    Soon, hybrid clouds grew into multi-clouds, with other public cloud providers such as Azure and GPC in the mix. Sometimes this was by choice–why be locked into one vendor and suite of tools when you could choose from multiple vendors that might offer better pricing or have some products that are better suited for your needs?

    More often, though, it was involuntary–developers spinning up their own clouds and resources for their own purposes without consulting IT, creating shadow IT. Or companies acquiring other organizations whose cloud choices varied. Either way, the enterprise is using multiple clouds whether they planned to or not.

    Regardless of how it happened, once more clouds were introduced, complexity multiplied.

    The Multi-Cloud Hydra

    Too many organizations underestimate the complexity they’re introducing with each additional cloud. Operating systems, networking, cost monitoring and tracking, workload methodologies and data aggregation are all different for each vendor. How many people in your organization know what all these differences are and how to navigate between them? How do you effectively manage, govern and monitor usage, costs and security and compliance issues when each public cloud does it differently? You have to manually collect data from each cloud and aggregate it in spreadsheets for analysis. How accurate can that be, how long does it take and at what cost in terms of people and time?

    Furthermore, how will you be able to accurately pinpoint waste and inefficiency? If you’re chasing down engineers to turn off resources or having to choose between one manual cost-efficiency action versus another, the waste will add up and your budget will be inaccurate.

    As if that’s not chaotic and complicated enough, development teams spread out across the enterprise adopt different tools and use them for their own purposes, without any alignment or coordination. The same tool is purchased multiple times. Best practices between teams aren’t shared. Everything needs to be custom-coded, creating layers of “spaghetti code” that blur visibility and are ripe for misconfigurations, which results in security vulnerabilities. And let’s not forget shadow IT, which IT departments don’t even know about, let alone monitor.

    You started your journey with a unified cloud strategy, but now you’re struggling with an ever-growing number of different clouds and strategies for different groups. Each group prefers different tools, clouds, methods and processes, and this has left you stuck with siloed teams, unending tool sprawl, infinite lines of custom code, worsening security risks and no clear visibility. And the meter keeps spinning, costing you millions.

    So, what do you do now?

    Taming the Chaos

    To survive and thrive in the new cloud order, enterprises can’t just write off exploding cloud costs as “the cost of innovation,” or otherwise justify their cloud chaos as par for the course. Too many are doing that now, and it won’t end well, as we’re likely headed into recession and growing uncertainty and turbulence in markets.

    First and foremost, taming cloud chaos and getting multi-cloud costs under control requires visibility and interoperability. You must be able to view, monitor, manage and analyze all clouds from one place; manually collecting that data with spreadsheets won’t do anymore.

    Development teams can no longer be siloed off from each other. There should be a single repository where all teams can access and utilize all tools, scripts, and previously coded integrations. When everything is viewable and available in a single place, no one has to buy the same tool twice or reinvent the wheel when using it. Once a tool has been integrated, any other team that wants to use that tool can simply re-use the integration, configuration, and policies that have already been created and vetted. If multiple groups are doing the same thing, an approved automation process can be created and shared.

    True cost optimization also requires organizational awareness and shared responsibility. If people don’t know they’re wasting resources, how will they be able to stop? Enterprises need a system that detects waste and cost overruns and notifies stakeholders accordingly.

    For example, a development environment may be up and running even though it’s no longer being used. An automatic alert could be sent to the appropriate team leader, reminding them to shut it down. A developer provisioning cloud resources might not choose the most cost-effective option, but the alert system would flag their activity and notify them of a cheaper alternative. Cloud resources that are left on overnight or on weekends would also be detected and shut down. Commitments would be conducted automatically every day and no longer require individuals to remember to do them manually.

    There’s just no going back to the traditional whack-a-mole style of cloud management and cost control. Enterprises that understand this and create a holistic framework for cloud management capabilities based on simplicity, visibility, unity and awareness will be in a much better position to navigate the choppy economic waters to come.

  • Exploring the Emerging Role of FinOps

    Exploring the Emerging Role of FinOps

    Cloud overspending is becoming a top-of-mind budget item for the modern CFO. To direct resource utilization in the cloud, some organizations are turning to increased automation and more diligent cloud procurement processes. As part of this momentum, new roles are emerging to oversee cloud financial operations.

    Amid rising cloud costs, FinOps is emerging as a helpful discipline. It is not always a specific role but more often a culture that blends engineers, finance and business areas to participate in dynamic cloud cost management. But getting all these players on the same page isn’t always easy — new findings say influencing developers to actually make optimizations is a top challenge.

    The FinOps Foundation recently released The State of FinOps 2022 report, a deep exploration into the role and the state of FinOps adoption across 1,000 companies worldwide. Below, I’ll highlight the key insights from the report to understand current trends and consider how organizations might implement similar tactics to better operationalize their cloud impact.

    Who Is Practicing FinOps?

    What type of person is involved in FinOps? Well, folks come from diverse backgrounds, spanning cloud financial management, IT or DevOps or consultants. Many are also new to the practice in general—the average career tenure is roughly three years. FinOps tends to be a well-paying field—45% of practitioners report salaries of over $100,000 annually.

    FinOps also tends to be associated with larger enterprises—45% of respondents came from organizations with 10,000 or more employees. In terms of industry, it is most common in financial services and IT companies, although studies show the practice is proliferating in many other sectors too. Geographically, FinOps is most adopted in primarily adopted in North America and Europe.

    Team Dynamics

    The average team size is five people, although many feel they’re a “one-person show.” Interestingly, more mature adoption tends to involve a larger team, upwards of 20+ members for established teams. There’s also a notable increase in FinOps within organizations managing between $50 million and $500 million of cloud costs.

    FinOps must interface with many levels of the company, sharing projections with both development and executive leadership. Most FinOps teams report directly to the CTO or other C-suite members. This makes sense, as an executive directive was found to be the most common way to promote a FinOps culture, followed by organic and grassroots adoption.

    Technology and Tools

    In general, FinOps tends to be achieved through cloud awareness and dedicated tooling. The most mature practitioners are “all-in on cloud.” The largest subgroup (24%) runs 1,000 to 5,000 computing instances in the public cloud, hosting technology like virtual machines, containers and serverless functions. Simply put, the average FinOps practitioner has a lot on their plate.

    In terms of tooling, there’s not one cloud cost management suite to rule them all. Teams use an average of 3.7 tools. A variance of tooling here is likely due to rising hybrid multi-cloud architectures and the need to manage costs separately within each cloud environment.

    As such, FinOps tooling includes a mixture of native cloud provider tools and other middleware. Surprisingly, after AWS Cost Explorer, the second most common tool is a homegrown tool. After that, Cloudablity, CloudHealth and Azure Cost Management rank as popular primary tools.

    Top Challenge: Collaboration

    The top barrier has to do with interdepartmental collaboration. Getting engineers to take action on cost optimization was the biggest challenge, cited by 30% of respondents. Other common challenges included accurately forecasting cloud spending (29%), convincing the organization to adopt FinOps (22%), enabling automation (20%) and reducing waste or unused resources (19%).

    Developers in the flow likely don’t want to be burdened by new cost optimization rules, so FinOps may encounter an uphill battle trying to affect low-level change. It will probably take a combination of shift-left IDE-based cost projection tooling and a decree from upper management to jumpstart this change.

    Besides getting everyone on board, FinOps is often tasked with chargeback models—48% of respondents report using chargeback to report cloud costs, the report found. Yet, correlating cloud costs to a single department or team is challenging with so much shared infrastructure. As such, shared costs remain a challenge; so much so that many practitioners don’t even split them up.

    There is also the need to resolve unexpected anomalies and increases in cloud costs, and it can be hard to distinguish between the two. As a result, 53% of respondents said it takes days for their FinOps teams to respond to unexpected cost increases. In the future, finding ways to shorten this timeframe will help save the company money.

    Next Goal: Getting Engineers to Take Action

    The vast majority (94%) of organizations are wasting money in the cloud, according to the 2022 HashiCorp State of Cloud Strategy Survey. Idle or overprovisioned resources can unnecessarily increase the cloud budget, making the need for FinOps evident. Investing in cloud optimization is also another tactic to meet sustainability pledges.

    FinOps is an emerging role that can help forecast spending and reduce waste. But the practice does require time and resources to reach an ROI. For example, a mature team only has a 5% deviation comparing forecasted spend and actual spend, whereas a less mature team can have as high as a 20% deviation. To improve, teams will require automation for evaluating and communicating cloud projections and more established anomaly management best practices.

    But above all, organizations must grapple with the number-one problem: Getting engineers to take action. Perhaps this is due to the fact that most respondents share cost data with technical teams monthly. Instead, more frequent cost transparency would match the pace of agile development and might empower more engineers.

    The State of FinOps 2022 surveyed 1,056 respondents on their FinOps roles. The yearly study is conducted by The FinOps Foundation, a Linux Foundation program. For more details, you can read the entire report here.