Skip to main content

Management Tools for MSPs Could Come in Handy in the Private Cloud

The emergence of private and public cloud computing technologies is causing organizations to revisit their IT performance management strategies and evaluate if the solutions that they currently have in place can be as effective in these new environments.

Many of them are already finding that the tools in which they had in place for many years do not have all of the necessary capabilities to support cloud deployments, and are looking for new features, delivery methods and pricing models to meet their new objectives. On the other hand, vendors from different areas of IT performance management are focusing a major part of their product development efforts on making their solutions more “cloud friendly”. When it comes to Business Service Management, some vendors might find that some capabilities that have been developed in the past might come in handy when managing private cloud environments. As deployments of private cloud services become more prevalent in the enterprise, some of these vendors could experience the market actually “coming to them”.

Even though private clouds cannot deliver all of the benefits that organizations generally associate with the term “cloud computing”, they still enable organizations to achieve the same significant improvements, especially in the areas such as the flexibility of management and alignment of infrastructure management with business needs. From a management perspective, deployments of private cloud services are driving IT organizations to act like internal service providers. For that reason, many of the capabilities that BSM vendors built to make their solutions more appealing to the managed service provider market could be very valuable for the management of private cloud environments.

Historically, while some vendors saw the managed service provider market as a major opportunity for growth, some others were very turned off by long sales cycles and more complicated requirements, and decided to focus on other markets in which they have a better chance to win. Vendors that had focused on this market segment and built capabilities that would allow them to deal with some of the key challenges of providing BSM as a managed service are now finding it much easier to enter the private cloud management market and position themselves for success. A good example of this type of company is ScienceLogic. Nimsoft has also been looking to replicate their success in the MSP market and use their capabilities in the private cloud environments. Additionally, AccelOps recently announced a new version of their solutions that include some enhancements designed specifically to better address the needs of the MSP market.

It should be noted that vendors cannot take just any solution that was designed to work for MSPs and apply it to private clouds. In order for these technologies to benefit the users of private cloud services, they need to include a set of additional functionalities that will make them effective in managing virtualized and dynamic infrastructure. However, the capabilities such as multi-tenancy, SLA management or the ability to calculate service chargebacks have been essential for using BSM solutions in the MSP market and they are equally as important in managing private cloud environments.

About Bojan Simic

Bojan Simic is the founder and Principal Analyst at TRAC Research, a market research and analyst firm that specializes in IT performance management. As an industry analyst, Bojan interviewed more than 2,000 IT and business professionals from end-user organizations and published more than 50 research reports. Bojan's coverage area at TRAC Research includes application and network monitoring, WAN management and acceleration, cloud and virtualization management, BSM and managed services.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

Management Tools for MSPs Could Come in Handy in the Private Cloud

The emergence of private and public cloud computing technologies is causing organizations to revisit their IT performance management strategies and evaluate if the solutions that they currently have in place can be as effective in these new environments.

Many of them are already finding that the tools in which they had in place for many years do not have all of the necessary capabilities to support cloud deployments, and are looking for new features, delivery methods and pricing models to meet their new objectives. On the other hand, vendors from different areas of IT performance management are focusing a major part of their product development efforts on making their solutions more “cloud friendly”. When it comes to Business Service Management, some vendors might find that some capabilities that have been developed in the past might come in handy when managing private cloud environments. As deployments of private cloud services become more prevalent in the enterprise, some of these vendors could experience the market actually “coming to them”.

Even though private clouds cannot deliver all of the benefits that organizations generally associate with the term “cloud computing”, they still enable organizations to achieve the same significant improvements, especially in the areas such as the flexibility of management and alignment of infrastructure management with business needs. From a management perspective, deployments of private cloud services are driving IT organizations to act like internal service providers. For that reason, many of the capabilities that BSM vendors built to make their solutions more appealing to the managed service provider market could be very valuable for the management of private cloud environments.

Historically, while some vendors saw the managed service provider market as a major opportunity for growth, some others were very turned off by long sales cycles and more complicated requirements, and decided to focus on other markets in which they have a better chance to win. Vendors that had focused on this market segment and built capabilities that would allow them to deal with some of the key challenges of providing BSM as a managed service are now finding it much easier to enter the private cloud management market and position themselves for success. A good example of this type of company is ScienceLogic. Nimsoft has also been looking to replicate their success in the MSP market and use their capabilities in the private cloud environments. Additionally, AccelOps recently announced a new version of their solutions that include some enhancements designed specifically to better address the needs of the MSP market.

It should be noted that vendors cannot take just any solution that was designed to work for MSPs and apply it to private clouds. In order for these technologies to benefit the users of private cloud services, they need to include a set of additional functionalities that will make them effective in managing virtualized and dynamic infrastructure. However, the capabilities such as multi-tenancy, SLA management or the ability to calculate service chargebacks have been essential for using BSM solutions in the MSP market and they are equally as important in managing private cloud environments.

About Bojan Simic

Bojan Simic is the founder and Principal Analyst at TRAC Research, a market research and analyst firm that specializes in IT performance management. As an industry analyst, Bojan interviewed more than 2,000 IT and business professionals from end-user organizations and published more than 50 research reports. Bojan's coverage area at TRAC Research includes application and network monitoring, WAN management and acceleration, cloud and virtualization management, BSM and managed services.

Hot Topics

The Latest

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...