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Multi-Tenancy in an APM Context

Ivar Sagemo

No topic in IT today is hotter than cloud computing. And I find it interesting how the rapid adoption of cloud platforms has led to a reinvention of how many IT applications and services work at a fairly deep level — certainly including those in my own area of APM.

Multi-tenancy, for instance, is a concept that has really come into vogue with the advent of public cloud platforms. A public cloud is by definition a shared architecture. This means an indefinite number of users (tenants) may be utilizing it at any given time. For all of those customers, the cloud provider wants to offer key services such as authentication, resource tracking, information management, policy creation, etc. It's only a question of what the most efficient way to accomplish this might be.

The most obvious idea would be to create a new instance of each service for each client. In this scenario, if the cloud has a thousand current clients, it also has a thousand iterations of a given service running simultaneously. Such an approach would be technically viable, but operationally wasteful — enormously complex, and therefore relatively slow and awkward to manage.

Multi-tenancy takes a different approach altogether. Instead of deploying new instances on a one-to-one basis with customers, the cloud host only needs to deploy one instance of a core application in total. That one instance, thanks to its sophisticated design, can then scale to support as many cloud customers as are necessary, logically sandboxing their data so as to keep them all completely separate from each other (even though the cloud architecture is in fact shared).

From the perspective of the cloud host, this approach is substantially superior. It is operationally much simpler to install, integrate, and manage one instance instead of many. And from the perspective of the cloud customer, the benefits are just as impressive. A customer who is interested in APM (Application Performance Management) capabilities, for example, can get them without ever having to worry about buying, deploying, or managing an actual APM solution. All that's required is contracting with a cloud provider who offers them.

Imagine an organization that manages a fleet of cruise ships. Each ship offers its own logical services, based on its own information; for each ship, separate APM considerations apply. Such an organization might solve that problem by purchasing, rolling out, and continually managing an APM solution in-house, but after all, IT infrastructure and IT service management isn't this organization's core strength; cruise ship management is. After all, APM on a moving target is tricky.

Now imagine that this organization discovers APM capabilities can be obtained from a trusted cloud provider, and that those capabilities will scale naturally to any number of ships. This may well prove the more attractive option of the two.

Setup time per server: roughly five minutes to install an agent. And because the cloud provider bills on a utility basis, the organization will only be charged in proportion to actual service usage. All the benefits of modern APM are thus achieved, yet the costs and complexity involved are relatively low.

Naturally, this does put a bit more burden on the APM solution developer! Re-coding an application to support multi-tenancy in cloud architecture is not a trivial feat of software engineering.

But for developers willing to put in the time, the benefits generated in the marketplace are clearly worth the effort:

• A broader range of service/software models, including both traditional and SaaS models, from which customers can easily choose to meet their needs

• A more direct focus on the core mission and less worry about IT infrastructure and overhead

• And for cloud hosts, simplified management, reduced costs and complexity, and a faster response to changing business conditions

For developer and organizations alike it’s a WIN-WIN situation.

Ivar Sagemo is CEO of AIMS Innovation.

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Multi-Tenancy in an APM Context

Ivar Sagemo

No topic in IT today is hotter than cloud computing. And I find it interesting how the rapid adoption of cloud platforms has led to a reinvention of how many IT applications and services work at a fairly deep level — certainly including those in my own area of APM.

Multi-tenancy, for instance, is a concept that has really come into vogue with the advent of public cloud platforms. A public cloud is by definition a shared architecture. This means an indefinite number of users (tenants) may be utilizing it at any given time. For all of those customers, the cloud provider wants to offer key services such as authentication, resource tracking, information management, policy creation, etc. It's only a question of what the most efficient way to accomplish this might be.

The most obvious idea would be to create a new instance of each service for each client. In this scenario, if the cloud has a thousand current clients, it also has a thousand iterations of a given service running simultaneously. Such an approach would be technically viable, but operationally wasteful — enormously complex, and therefore relatively slow and awkward to manage.

Multi-tenancy takes a different approach altogether. Instead of deploying new instances on a one-to-one basis with customers, the cloud host only needs to deploy one instance of a core application in total. That one instance, thanks to its sophisticated design, can then scale to support as many cloud customers as are necessary, logically sandboxing their data so as to keep them all completely separate from each other (even though the cloud architecture is in fact shared).

From the perspective of the cloud host, this approach is substantially superior. It is operationally much simpler to install, integrate, and manage one instance instead of many. And from the perspective of the cloud customer, the benefits are just as impressive. A customer who is interested in APM (Application Performance Management) capabilities, for example, can get them without ever having to worry about buying, deploying, or managing an actual APM solution. All that's required is contracting with a cloud provider who offers them.

Imagine an organization that manages a fleet of cruise ships. Each ship offers its own logical services, based on its own information; for each ship, separate APM considerations apply. Such an organization might solve that problem by purchasing, rolling out, and continually managing an APM solution in-house, but after all, IT infrastructure and IT service management isn't this organization's core strength; cruise ship management is. After all, APM on a moving target is tricky.

Now imagine that this organization discovers APM capabilities can be obtained from a trusted cloud provider, and that those capabilities will scale naturally to any number of ships. This may well prove the more attractive option of the two.

Setup time per server: roughly five minutes to install an agent. And because the cloud provider bills on a utility basis, the organization will only be charged in proportion to actual service usage. All the benefits of modern APM are thus achieved, yet the costs and complexity involved are relatively low.

Naturally, this does put a bit more burden on the APM solution developer! Re-coding an application to support multi-tenancy in cloud architecture is not a trivial feat of software engineering.

But for developers willing to put in the time, the benefits generated in the marketplace are clearly worth the effort:

• A broader range of service/software models, including both traditional and SaaS models, from which customers can easily choose to meet their needs

• A more direct focus on the core mission and less worry about IT infrastructure and overhead

• And for cloud hosts, simplified management, reduced costs and complexity, and a faster response to changing business conditions

For developer and organizations alike it’s a WIN-WIN situation.

Ivar Sagemo is CEO of AIMS Innovation.

Hot Topics

The Latest

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 ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.