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[post_content] => To get the most out of your AI initiatives and infuse intelligence into your core business processes, you must first establish an IT environment that is “intelligent” itself. In other words, intelligence breeds intelligence. Without establishing a modernized foundation that is automated, adaptable and anticipatory, your AI initiatives will struggle to scale, adapt, or deliver the ROI you hoped. Just as a successful business strategy hinge on the people, process, and technology, your enterprise IT ecosystem requires embedded intelligence across its key pillars. These pillars are known as infrastructure, operations, development, applications, and governance. Let’s explore these five pillars in greater detail and reimagine how AI can elevate each one. 1. AI-Driven Infrastructure Modernization The first step is to ensure that your on-prem infrastructure is intelligence ready. This requires hardware prerequisites such as GPUs, latest and compatible hardware, deployment of containers and kubernetes, modern DevOps base layer, and more. Once your infrastructure readiness is achieved, you can introduce AI into your operational and automation tasks that were traditionally done manually or using disparate scripts. These tasks include things such as patching, resource planning, storage management, capacity planning and forecasting. When done in a proactive manner these efforts streamline the right-sizing of virtual machines, making optimization faster, smarter, and more efficient. Businesses must move from a reactive mindset of manual responses to a proactive anticipatory strategy that only AI can deliver. The ability to readily identify usage patterns will enable forward-thinking resource and capacity planning that will then unlock other advanced capabilities:
Order new infrastructure resources ahead of time so you don't run into capacity shortages that could disrupt operations.
Track compute resource performance to identify underutilized or overwhelmed systems and suggest optimizations for better cost management.
Fully leverage historical trends and usage patterns to achieve smarter long-term planning and budgeting.
2. AI-Driven Operations Alerts are constantly coming into your NOC or SOC informing you that servers are down or unusual login activity is occurring. How do you keep up with it all? The primary objective of AI enabled operations is to find the underlying cause by correlating all the things that are happening within your enterprise and connecting the dots. Root cause analysis becomes possible only when you can see how different events relate to each other. For instance, a single authentication alert may not mean much on its own, but when you connect it to another event ID, a pattern appears. In other cases, an alert may point to the wrong culprit as a server being down may be a failed network switch upstream. Having a current and accurate CMDB (Configuration Management Database) is critical for generating correlations, and determining other CIs (Configuration Items) that can potentially be affected. Automatic proactive notifications can be set to trigger when abnormal patterns occur, so that the operations teams get notified and trigger the appropriate and timely response. That may sound simple on the one hand and daunting on the other, but AI makes this possible by removing the guesswork from incident response. This means that your support teams can react faster and more consistently, which directly strengthens both your system's resilience and overall security. Another emerging feature of AI Ops is the utilization of chatbots, which allows teams to ask questions in normal conversation format and get real answers concerning things such as ticket status or procedural steps around specific workflows. 3. AI-Enhanced Developer Productivity Many organizations have gained great efficiency for their development teams by using IDE integrations and internal large language models. AI solutions like GitHub Copilot and internal large language models now assist in code validation and linting, processes that could eat up hours of manual work. Developers use AI capabilities embedded into their IDE consoles to gain efficiencies with the programming tasks. The payoff of these automation efforts is almost immediate, as bugs get caught earlier in the process, code quality improves, readability increases, and teams spend far less time on tedious manual reviews. What used to require multiple rounds of human oversight now happens automatically, freeing developers to focus on solving bigger problems. 4. AI Integrated Applications Whether it is commercial off-the-shelf applications or applications customized for your own organization, AI can take them to the next level. Imagine an application making API calls to the client's on-premises LLM to solicit AI-generated suggestions for infrastructure optimization that can then be presented to end users as action items. Consider another instance in which an application continuously monitors infrastructure usage patterns, automatically suggesting practical steps like storage deletion, re-tiering, or resource reallocation. AI-enhanced applications are doing far more than just providing data. They deliver context-aware insights that adapt to your specific environment and business needs. That means faster response times, reduced operational overhead and greater strategic use of resources. 5. AI for Governance, Security, and Trust One of the keys for security today is the ability to identify unanticipated activity. An example could be a privileged user that logs in from a new geographic location, device, or time window. AI detectors can easily identify these abnormalities and initiate alerts that then require validation by the security team. If these events cannot be validated, security policies should then kick in automatically to protect against these events. Essentially, the system learns what "normal" looks like for each user and environment, then acts decisively when patterns break. This level of intelligent governance creates a powerful defense mechanism that stops unauthorized activity in its tracks while maintaining operational flow for legitimate users. When governance is implemented effectively, it can stop any activity that is not part of the pattern, improving business resiliency and trust. AI-driven governance is especially valuable in industries with strict compliance requirements, like healthcare and finance, where monitoring and enforcing policy adherence is critical. Keyva as Another Pillar At Keyva, we focus on the infrastructure and operational backbone that powers your business. While your core business processes may require industry-specific expertise, we ensure your IT infrastructure is smart, responsive, and ready to support whatever your organization builds on top of it. We have proven specialties when it comes to transforming all five pillars of your IT ecosystem and we invite your technology and business leaders to discover how our expertise can help modernize your entire IT landscape to unlock greater agility, performance, and resilience for your enterprise. [table id=3 /] [post_title] => The Five Pillars of Intelligent Enterprise IT Modernization
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https://youtu.be/5Uob3eBvm4A
Keyva CTO Anuj Tuli discusses how an acquisition of a leading data integration platform help enhance our offerings in the DevOps and cloud automation space.
In today’s hybrid cloud landscape, organizations are eager to embrace cloud-native technologies like Kubernetes and with the right approach, they can do so with confidence. By addressing internal skill gaps, clarifying roadmaps, and aligning modernization strategies with business goals, teams can set the stage for meaningful progress.
[post_title] => Kubernetes Adoption Accelerator
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[post_content] => It was just over twenty years ago that enterprises talked about server sprawl and the sustainability of maintaining so many hardware servers on prem. That conversation was initiated by the dawn of virtualization thanks to innovative vendors like VMware whose ESX infrastructure began transforming the datacenter landscape. Suddenly, organizations could consolidate workloads, which significantly reduced the need to purchase and maintain endless racks of hardware. Suddenly, a new standard was born.
Virtualization: Streamlined, But Still Complex
While the days of racks filled with metal boxes are largely behind us, virtual machines still rely on a complex ecosystem of underlying hardware, software, and systems that require perpetual licensing. From hypervisors and storage systems to network configurations and management tools, each component plays its own key role in keeping your applications and processes running, so that your business can operate and thrive. While the initial investment can be substantial, it is only the beginning as ongoing costs continue throughout the system's lifecycle. All that “stuff” needs to be supported, maintained and upgraded after every lifecycle. This is why we calculate the Total Cost of Ownership to reveal the complete financial impact of virtualization beyond upfront expenses. Without accounting for long-term investments in infrastructure, operations, and personnel, organizations cannot accurately assess a solution's true value.
Factoring TCO is Highly Complicated
As organizations embrace hybrid architectures, arriving at the Total Cost of Ownership (TCO) of your IT infrastructure investments is no longer just about accounting for hardware investments, support contracts, and upgrade cycles. Today’s hybrid environments introduce a host of new variables that make TCO a murky process.
Scalability Costs: Successful and competitive businesses want to grow, which means your IT infrastructure must scale with it. Whether expanding cloud capacity or upgrading on-prem resources, scalability comes with a price tag that must be accounted for.
Technical Debt: Every organization is guilty of quick fixes, legacy code, and rushed development decisions at some point. Eventually, those shortcomings accumulate into technical debt that can slow innovation and inflate future upgrade expenses.
Resource Utilization Imbalances: Hybrid environments often suffer from overutilized on-prem assets. Underutilization means your investments aren’t being optimized. This is typical with cloud environments where underutilization can result in surprise bills that wreak havoc on budgets.
Tool Sprawl: Managing a hybrid estate often leads to the adoption of multiple tools for monitoring, security, and orchestration which can result in overlapping functionality, duplicate licensing, and increased training and support costs.
There are also hidden costs you must consider. A big one is business downtime. When your business critical applications are down, you lose business. Don’t take shortcuts on resiliency.
The Challenge of Sustainability
While you may not have witnessed the dawn of server virtualization, you were most likely present during the COVID years. If so, you remember the sudden disruption in supply chains. The fragility of supply chains is important to keep in mind when aiming to extend the life of existing infrastructure because the longer you rely on aging infrastructure, the more dependent you become on the availability of compatible hardware. Sustainability in IT isn’t just about financial viability. It’s also about resilience and availability. Sustainability can also be threatened when the financial equation changes due to mergers or acquisitions, as demonstrated by Broadcom's recent acquisition of VMware.
Containers Mean Lower TCO
Even more compelling, containers typically run on open-source platforms such as Kubernetes, eliminating hefty licensing fees associated with proprietary solutions. The lightweight approach of containers uses far less memory and storage, which means you can run more workloads on fewer servers. That translates to lower costs on many fronts including power, cooling and space. Kubernetes also natively automates tasks such as scaling, load balancing and failover, minimizing the need for manual intervention and operational overhead. As application demand grows, Kubernetes automatically deploys additional containers as needed, ensuring seamless scaling without a spike in labor costs. Containers also pair naturally with OPEX-friendly, consumption-based cloud models where you pay only for the resources you use, and scaling capacity up or down is simple and fast. By migrating away from costly virtualization licenses and legacy technical debt, organizations can redirect resources toward innovation and accelerate product releases.
Container Considerations
If the concept of containers is new to your organization, there are some things you need to consider before transitioning.
Your organization may have a skills gap, so factor in retraining costs or bringing new talent to lead the team.
While cloud hosting offers flexibility, extracting and analyzing cloud-hosted data can incur unexpected fees. Be sure to factor in data egress costs when planning workload analytics.
You may want to refactor your workloads by separating them into microservices to take advantage of containers.
Containers may eliminate licensing costs, but they don’t eliminate the need for robust cybersecurity. Container environments require dedicated security strategies, including image scanning, runtime protection, and access controls.
Successful adoption depends on support from across the organization, so communicate the benefits clearly.
Make Keyva Part of the Equation
Transitioning from a virtualized environment to a container platform is not a mere lift-and-shift. It is a paradigm shift, from how you monitor systems and patch applications, to how you develop, deploy, and manage workloads. Operational workflows, troubleshooting, and even the skills required for day-to-day tasks are fundamentally different. It isn’t a migration; it is a platform modernization effort. At Keyva, we’ve guided organizations through this shift for years. We help you identify the best workloads to containerize, select the optimal platforms, and ensure your transition delivers long-term value. Our hands-on approach includes training your team to maximize efficiency, control costs, and budget with confidence for your new environment. Let Keyva be your partner for a smooth, successful container transformation. [table id=3 /]
[post_title] => Total Cost of Ownership: Virtualization and Container Environments
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https://youtu.be/jG693ZS3Ezg
Keyva CTO Anuj Tuli discusses how organization are leveraging DevOps to enhance their operational efficiency.
Keyva CTO Anuj Tuli discusses how to navigate the complexities of cloud adoption and maximize the benefits for your organization.
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[post_content] => To get the most out of your AI initiatives and infuse intelligence into your core business processes, you must first establish an IT environment that is “intelligent” itself. In other words, intelligence breeds intelligence. Without establishing a modernized foundation that is automated, adaptable and anticipatory, your AI initiatives will struggle to scale, adapt, or deliver the ROI you hoped. Just as a successful business strategy hinge on the people, process, and technology, your enterprise IT ecosystem requires embedded intelligence across its key pillars. These pillars are known as infrastructure, operations, development, applications, and governance. Let’s explore these five pillars in greater detail and reimagine how AI can elevate each one. 1. AI-Driven Infrastructure Modernization The first step is to ensure that your on-prem infrastructure is intelligence ready. This requires hardware prerequisites such as GPUs, latest and compatible hardware, deployment of containers and kubernetes, modern DevOps base layer, and more. Once your infrastructure readiness is achieved, you can introduce AI into your operational and automation tasks that were traditionally done manually or using disparate scripts. These tasks include things such as patching, resource planning, storage management, capacity planning and forecasting. When done in a proactive manner these efforts streamline the right-sizing of virtual machines, making optimization faster, smarter, and more efficient. Businesses must move from a reactive mindset of manual responses to a proactive anticipatory strategy that only AI can deliver. The ability to readily identify usage patterns will enable forward-thinking resource and capacity planning that will then unlock other advanced capabilities:
Order new infrastructure resources ahead of time so you don't run into capacity shortages that could disrupt operations.
Track compute resource performance to identify underutilized or overwhelmed systems and suggest optimizations for better cost management.
Fully leverage historical trends and usage patterns to achieve smarter long-term planning and budgeting.
2. AI-Driven Operations Alerts are constantly coming into your NOC or SOC informing you that servers are down or unusual login activity is occurring. How do you keep up with it all? The primary objective of AI enabled operations is to find the underlying cause by correlating all the things that are happening within your enterprise and connecting the dots. Root cause analysis becomes possible only when you can see how different events relate to each other. For instance, a single authentication alert may not mean much on its own, but when you connect it to another event ID, a pattern appears. In other cases, an alert may point to the wrong culprit as a server being down may be a failed network switch upstream. Having a current and accurate CMDB (Configuration Management Database) is critical for generating correlations, and determining other CIs (Configuration Items) that can potentially be affected. Automatic proactive notifications can be set to trigger when abnormal patterns occur, so that the operations teams get notified and trigger the appropriate and timely response. That may sound simple on the one hand and daunting on the other, but AI makes this possible by removing the guesswork from incident response. This means that your support teams can react faster and more consistently, which directly strengthens both your system's resilience and overall security. Another emerging feature of AI Ops is the utilization of chatbots, which allows teams to ask questions in normal conversation format and get real answers concerning things such as ticket status or procedural steps around specific workflows. 3. AI-Enhanced Developer Productivity Many organizations have gained great efficiency for their development teams by using IDE integrations and internal large language models. AI solutions like GitHub Copilot and internal large language models now assist in code validation and linting, processes that could eat up hours of manual work. Developers use AI capabilities embedded into their IDE consoles to gain efficiencies with the programming tasks. The payoff of these automation efforts is almost immediate, as bugs get caught earlier in the process, code quality improves, readability increases, and teams spend far less time on tedious manual reviews. What used to require multiple rounds of human oversight now happens automatically, freeing developers to focus on solving bigger problems. 4. AI Integrated Applications Whether it is commercial off-the-shelf applications or applications customized for your own organization, AI can take them to the next level. Imagine an application making API calls to the client's on-premises LLM to solicit AI-generated suggestions for infrastructure optimization that can then be presented to end users as action items. Consider another instance in which an application continuously monitors infrastructure usage patterns, automatically suggesting practical steps like storage deletion, re-tiering, or resource reallocation. AI-enhanced applications are doing far more than just providing data. They deliver context-aware insights that adapt to your specific environment and business needs. That means faster response times, reduced operational overhead and greater strategic use of resources. 5. AI for Governance, Security, and Trust One of the keys for security today is the ability to identify unanticipated activity. An example could be a privileged user that logs in from a new geographic location, device, or time window. AI detectors can easily identify these abnormalities and initiate alerts that then require validation by the security team. If these events cannot be validated, security policies should then kick in automatically to protect against these events. Essentially, the system learns what "normal" looks like for each user and environment, then acts decisively when patterns break. This level of intelligent governance creates a powerful defense mechanism that stops unauthorized activity in its tracks while maintaining operational flow for legitimate users. When governance is implemented effectively, it can stop any activity that is not part of the pattern, improving business resiliency and trust. AI-driven governance is especially valuable in industries with strict compliance requirements, like healthcare and finance, where monitoring and enforcing policy adherence is critical. Keyva as Another Pillar At Keyva, we focus on the infrastructure and operational backbone that powers your business. While your core business processes may require industry-specific expertise, we ensure your IT infrastructure is smart, responsive, and ready to support whatever your organization builds on top of it. We have proven specialties when it comes to transforming all five pillars of your IT ecosystem and we invite your technology and business leaders to discover how our expertise can help modernize your entire IT landscape to unlock greater agility, performance, and resilience for your enterprise. [table id=3 /] [post_title] => The Five Pillars of Intelligent Enterprise IT Modernization
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Discover how leading organizations are transforming their IT operations with the Keyva Seamless Data Pump™. This eBook details how you can automate complex integrations, achieve true single pane-of-glass visibility, and ...
To get the most out of your AI initiatives and infuse intelligence into your core business processes, you must first establish an IT environment that is “intelligent” itself. In other ...
Keyva CTO Anuj Tuli discusses how an acquisition of a leading data integration platform help enhance our offerings in the DevOps and cloud automation space.
In today’s hybrid cloud landscape, organizations are eager to embrace cloud-native technologies like Kubernetes and with the right approach, they can do so with confidence. By addressing internal skill gaps, ...
It was just over twenty years ago that enterprises talked about server sprawl and the sustainability of maintaining so many hardware servers on prem. That conversation was initiated by the ...
Our client faced a significant challenge in synchronizing Configuration Items (CIs) between BMC Discovery (ADDM) and ServiceNow without using an intermediary staging table.
