Skip to main content

The Case for a New Monitoring Architecture for Next Generation Hybrid Applications

Ted Wilson

Organizations are increasingly delivering critical business applications via the cloud, both public, private, and hybrid. Unfortunately, monitoring of these distributed applications and services is frequently an after-thought.

Support teams often find it difficult to be proactive in identifying and preventing problems before they occur. And since the new cloud environments are far more dynamic and complex than before, traditional monitoring architectures cannot keep pace. What is needed is a monitor that can adapt and still provide the end-to-end visibility into these applications and services, enabling support teams to be more proactive in identifying and addressing issues quickly.


Many of the organizations we work with are actively deploying service workloads and components in heterogeneous environments, often across multiple clouds and on-premise data centers. To address the challenges that emerge when these applications include deployment in the cloud, a new architecture was developed. Let's look at what this means.

The Promise of Cloud

For years, the benefits of moving applications to the cloud have been widely touted and evidence has grown that huge savings could be achieved. It is a big win to be able to deploy an app onto a single shared platform and make updates available to all users simultaneously. Users don't need to buy their own hardware or download new versions of software to get the latest features. The widespread adoption of "containerization" using Docker and Kubernetes, along with commercial "Platform as a Service" offerings, has made it even easier to move applications into the cloud.

For SL, moving its flagship product to the cloud was quite painless. The highly modular and component-oriented architecture made it a natural for deployment into Docker containers and for hosting in the cloud. (See Deploying in Docker Containers).

If only it were that easy for everyone ...

The Problem with Cloud

Given the clear benefits, why has real adoption of cloud by many organizations been so painfully sluggish? For financial institutions, eCommerce operations, logistics providers, defense companies, and many others, moving from on-premise hardware to the cloud has not been easy. Slowly but surely, the transition is happening. But, for many of these applications, data security is such a concern that large parts remain deployed in on-premise hardware kept securely behind a firewall ... and cloud is not an option.

Most monitoring systems require the use of agents that must be installed on every physical and virtual server or router, potentially introducing performance and maintenance challenges. These agents then forward all performance data to central monitoring systems, increasing network traffic. In the case of pure SaaS-based monitors, proprietary monitoring data must be sent to and stored in the cloud. This approach can introduce some real concerns, including:

■ data security

■ lack of control

■ increased monitoring costs

Hybrid: The Best of Both Worlds

The answer is found in the middle ground between these extremes. The optimal solution involves an appropriate combination of cloud and on-premise components that directly address the challenges inherent in the problem domain.

The use of the term "Hybrid Cloud" is not new. Usually, it refers to an application deployment scheme in which some application services are deployed in public cloud (Amazon, Azure, Google, etc), and others are deployed in private cloud (VMware, for example).

"Next Generation Hybrid Cloud" takes things to a new level. In such a system, all components are equally able to be deployed on-premise OR in cloud - and there is complete interoperability between all components in the system. Some services may be hosted in a multi-tenant cloud app, while others, such as sensitive data storage components, can be deployed behind a user's firewall. It is a matter of convenience or preference that dictates what goes where, not limitations of the architecture.

The problem is how best to monitor the health and performance of complex applications deployed across multiple environments using disparate middleware technologies. It boils down to what monitoring services can be shared among all users, and what needs to be completely private and secure within an organization. In many cases there are restrictions around who has access to key systems and what can be monitored. Often, it is simply not possible to migrate ALL of the components of a system-wide monitoring system to the cloud, for security reasons.

Let's explore how this modern architecture provides the most advanced, flexible, and effective solution for monitoring large-scale highly distributed applications.

Next Generation Hybrid SaaS Monitoring

Next generation hybrid SaaS monitoring architectures use distributed data collectors deployed on-premise and in public and private clouds. Data collectors store performance data in a distributed fashion behind the users' firewallsensuring security, maintaining control of their monitoring data, and minimizing SaaS costs. No sensitive monitoring data are stored in the cloud. In-memory caching is used to maximize performance and persisted to databases as needed.

With this hybrid approach, users interact with their monitoring application via a web browser in their secure environments. Displays are served up from a cloud service and the monitoring data populate the displays, on the user's desktop or mobile device, from behind the firewall. Performance data never leave the user's secure environments.


Take Advantage of Managed Services

With a hybrid architecture like this, users can access managed services hosted in the cloud and don't have to install and maintain software to provide this capability. Updates are provided by the vendor and seamless to the user. Examples of these managed services might include:

1. custom display design and reporting tools

2. work team display design collaboration

3. custom display publishing tools

Benefits of a Hybrid SaaS Monitoring Platform

In summary, there are several benefits to this approach.

1. Centralized monitoring and visibility of applications, services, and workloads across all on-premise and cloud platforms.

2. Improved data security and control of the user's monitoring data.

3. Users can connect to both private and public data in one platform where it can be “blended” together.

4. Access to high-value managed services such as custom display development, collaboration, and dashboard publishing.

5. Reduced SaaS platform costs since monitoring data are never hosted in the cloud.

6. No need to upgrade to new versions since product updates are transparent and users are always working with the latest version.

Hot Topics

The Latest

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

The Case for a New Monitoring Architecture for Next Generation Hybrid Applications

Ted Wilson

Organizations are increasingly delivering critical business applications via the cloud, both public, private, and hybrid. Unfortunately, monitoring of these distributed applications and services is frequently an after-thought.

Support teams often find it difficult to be proactive in identifying and preventing problems before they occur. And since the new cloud environments are far more dynamic and complex than before, traditional monitoring architectures cannot keep pace. What is needed is a monitor that can adapt and still provide the end-to-end visibility into these applications and services, enabling support teams to be more proactive in identifying and addressing issues quickly.


Many of the organizations we work with are actively deploying service workloads and components in heterogeneous environments, often across multiple clouds and on-premise data centers. To address the challenges that emerge when these applications include deployment in the cloud, a new architecture was developed. Let's look at what this means.

The Promise of Cloud

For years, the benefits of moving applications to the cloud have been widely touted and evidence has grown that huge savings could be achieved. It is a big win to be able to deploy an app onto a single shared platform and make updates available to all users simultaneously. Users don't need to buy their own hardware or download new versions of software to get the latest features. The widespread adoption of "containerization" using Docker and Kubernetes, along with commercial "Platform as a Service" offerings, has made it even easier to move applications into the cloud.

For SL, moving its flagship product to the cloud was quite painless. The highly modular and component-oriented architecture made it a natural for deployment into Docker containers and for hosting in the cloud. (See Deploying in Docker Containers).

If only it were that easy for everyone ...

The Problem with Cloud

Given the clear benefits, why has real adoption of cloud by many organizations been so painfully sluggish? For financial institutions, eCommerce operations, logistics providers, defense companies, and many others, moving from on-premise hardware to the cloud has not been easy. Slowly but surely, the transition is happening. But, for many of these applications, data security is such a concern that large parts remain deployed in on-premise hardware kept securely behind a firewall ... and cloud is not an option.

Most monitoring systems require the use of agents that must be installed on every physical and virtual server or router, potentially introducing performance and maintenance challenges. These agents then forward all performance data to central monitoring systems, increasing network traffic. In the case of pure SaaS-based monitors, proprietary monitoring data must be sent to and stored in the cloud. This approach can introduce some real concerns, including:

■ data security

■ lack of control

■ increased monitoring costs

Hybrid: The Best of Both Worlds

The answer is found in the middle ground between these extremes. The optimal solution involves an appropriate combination of cloud and on-premise components that directly address the challenges inherent in the problem domain.

The use of the term "Hybrid Cloud" is not new. Usually, it refers to an application deployment scheme in which some application services are deployed in public cloud (Amazon, Azure, Google, etc), and others are deployed in private cloud (VMware, for example).

"Next Generation Hybrid Cloud" takes things to a new level. In such a system, all components are equally able to be deployed on-premise OR in cloud - and there is complete interoperability between all components in the system. Some services may be hosted in a multi-tenant cloud app, while others, such as sensitive data storage components, can be deployed behind a user's firewall. It is a matter of convenience or preference that dictates what goes where, not limitations of the architecture.

The problem is how best to monitor the health and performance of complex applications deployed across multiple environments using disparate middleware technologies. It boils down to what monitoring services can be shared among all users, and what needs to be completely private and secure within an organization. In many cases there are restrictions around who has access to key systems and what can be monitored. Often, it is simply not possible to migrate ALL of the components of a system-wide monitoring system to the cloud, for security reasons.

Let's explore how this modern architecture provides the most advanced, flexible, and effective solution for monitoring large-scale highly distributed applications.

Next Generation Hybrid SaaS Monitoring

Next generation hybrid SaaS monitoring architectures use distributed data collectors deployed on-premise and in public and private clouds. Data collectors store performance data in a distributed fashion behind the users' firewallsensuring security, maintaining control of their monitoring data, and minimizing SaaS costs. No sensitive monitoring data are stored in the cloud. In-memory caching is used to maximize performance and persisted to databases as needed.

With this hybrid approach, users interact with their monitoring application via a web browser in their secure environments. Displays are served up from a cloud service and the monitoring data populate the displays, on the user's desktop or mobile device, from behind the firewall. Performance data never leave the user's secure environments.


Take Advantage of Managed Services

With a hybrid architecture like this, users can access managed services hosted in the cloud and don't have to install and maintain software to provide this capability. Updates are provided by the vendor and seamless to the user. Examples of these managed services might include:

1. custom display design and reporting tools

2. work team display design collaboration

3. custom display publishing tools

Benefits of a Hybrid SaaS Monitoring Platform

In summary, there are several benefits to this approach.

1. Centralized monitoring and visibility of applications, services, and workloads across all on-premise and cloud platforms.

2. Improved data security and control of the user's monitoring data.

3. Users can connect to both private and public data in one platform where it can be “blended” together.

4. Access to high-value managed services such as custom display development, collaboration, and dashboard publishing.

5. Reduced SaaS platform costs since monitoring data are never hosted in the cloud.

6. No need to upgrade to new versions since product updates are transparent and users are always working with the latest version.

Hot Topics

The Latest

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...