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How Do I Assess the Quality of My External IT Supplier?

Coen Meerbeek

Given the extent to which companies are contracting out their IT organization to other parties, outsourcing appears to be making a comeback. But migrating your IT infrastructure and management to the cloud or another party remains a hot topic.

In the outsourcing procedure you lay down your criteria for the quality to be delivered by the other party. We have to do this, because otherwise the supplier will rest on his laurels, which is the last thing we want. So, we've got our criteria, but who's going to monitor them and how transparent are the figures?

An interesting subject to take a look at today.

As I mentioned above, the concept of outsourcing has changed a fair bit since the emergence of the cloud. We now have different levels of outsourcing.

1. The IT infrastructure runs at the supplier's premises but you remain responsible for the architecture, implementation and management. You also have the applications managed by your own people. (IaaS)

2. The IT infrastructure runs at the supplier's premises and the supplier is responsible for the architecture, implementation and management. You have the applications managed by your own people. (PaaS)

3. Both the IT infrastructure and the applications are the responsibility of the supplier. It makes no difference how everything has been designed. (SaaS)

The monitoring process is different for each of the three variants. What I regard as being most important here is:

■ To have all of the variants assessed by an independent party or by your own organisation. Under no circumstances should this be done by the supplier.

■ All of the assessments are carried out from the end-user's perspective. The best measure of the supplier's quality is the performance and availability that you as the buyer receive.

We can use the following solutions for the three variants. I've based this on pure SLA monitoring but also on the four data sets; wire, machine, agent and synthetic.

Variant 1 – Infrastructure-as-a-Service

The supplier is solely responsible for providing the hardware. You are responsible for its content. You will usually be responsible for everything from the virtualization level onwards.

In that case, the best way to monitor the supplier's service is to place the monitoring process at virtualization level. Splunk, for instance, has a superb VMWare app that provides you with all the information you need. I can well imagine that the supplier will not in all cases be willing to allow this. It will mean that he has to be transparent about the service he provides and it might be possible to pass on the monitoring data that he generates to your own Splunk implementation in order to draw up the right reports yourself.

Monitoring from infrastructure level is entirely your responsibility, so you can decide for yourself which tool to choose to cover four data sets. A synthetic solution remains desirable but cannot be used in respect of the supplier because he'll always say that there are other links between what he provides and the end-user and that he is not responsible for them.

Variant 2 – Platform-as-a-Service

In this variant, the supplier is responsible up to OS level. From that point onwards you are the owner who implements and manages the applications. In some cases the OS is also shielded and you are only able to implement the applications.

Monitoring the supplier's service directly affects the performance and availability of your applications, and for that reason it's advisable to implement a synthetic monitor. You can supplement this with a process that monitors the availability of the OS if you are able to influence this. In this case a simple Ping monitor will suffice.

You can synthetically add agent or machine data in order to cover all of the data sets. Wire data is an attractive option in the application area, but not at infrastructure level. To do this you will need to know a lot about how the infrastructure is set up, and that's precisely what you wanted to outsource.

Variant 3 – Software-as-a-Service

The supplier arranges everything. You are only the user of the application that you want to buy. The supplier will usually have published his own SLA, but how transparent is it?

The best bet here is to chose a synthetic solution yourself and have it assessed independently. Use these figures to check the quality of the service and to confront the supplier with results other than what have been agreed.

To conclude ...

Outsourcing is based on trust but to many companies IT is a matter of life or death: it's importance is inestimable. You want to avoid a situation where finger-pointing starts between you and the supplier if faults occur. Make sure that you give careful thought to monitoring quality also during the implementation of the outsourcing.

How have you experienced this as a user? How do you monitor your outsourcing contracts, and what were your experiences with an outsourcing party? I'd like to hear your experiences - I can learn from them too.

Coen Meerbeek is an Online Performance Consultant at Blue Factory Internet.

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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 ...

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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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How Do I Assess the Quality of My External IT Supplier?

Coen Meerbeek

Given the extent to which companies are contracting out their IT organization to other parties, outsourcing appears to be making a comeback. But migrating your IT infrastructure and management to the cloud or another party remains a hot topic.

In the outsourcing procedure you lay down your criteria for the quality to be delivered by the other party. We have to do this, because otherwise the supplier will rest on his laurels, which is the last thing we want. So, we've got our criteria, but who's going to monitor them and how transparent are the figures?

An interesting subject to take a look at today.

As I mentioned above, the concept of outsourcing has changed a fair bit since the emergence of the cloud. We now have different levels of outsourcing.

1. The IT infrastructure runs at the supplier's premises but you remain responsible for the architecture, implementation and management. You also have the applications managed by your own people. (IaaS)

2. The IT infrastructure runs at the supplier's premises and the supplier is responsible for the architecture, implementation and management. You have the applications managed by your own people. (PaaS)

3. Both the IT infrastructure and the applications are the responsibility of the supplier. It makes no difference how everything has been designed. (SaaS)

The monitoring process is different for each of the three variants. What I regard as being most important here is:

■ To have all of the variants assessed by an independent party or by your own organisation. Under no circumstances should this be done by the supplier.

■ All of the assessments are carried out from the end-user's perspective. The best measure of the supplier's quality is the performance and availability that you as the buyer receive.

We can use the following solutions for the three variants. I've based this on pure SLA monitoring but also on the four data sets; wire, machine, agent and synthetic.

Variant 1 – Infrastructure-as-a-Service

The supplier is solely responsible for providing the hardware. You are responsible for its content. You will usually be responsible for everything from the virtualization level onwards.

In that case, the best way to monitor the supplier's service is to place the monitoring process at virtualization level. Splunk, for instance, has a superb VMWare app that provides you with all the information you need. I can well imagine that the supplier will not in all cases be willing to allow this. It will mean that he has to be transparent about the service he provides and it might be possible to pass on the monitoring data that he generates to your own Splunk implementation in order to draw up the right reports yourself.

Monitoring from infrastructure level is entirely your responsibility, so you can decide for yourself which tool to choose to cover four data sets. A synthetic solution remains desirable but cannot be used in respect of the supplier because he'll always say that there are other links between what he provides and the end-user and that he is not responsible for them.

Variant 2 – Platform-as-a-Service

In this variant, the supplier is responsible up to OS level. From that point onwards you are the owner who implements and manages the applications. In some cases the OS is also shielded and you are only able to implement the applications.

Monitoring the supplier's service directly affects the performance and availability of your applications, and for that reason it's advisable to implement a synthetic monitor. You can supplement this with a process that monitors the availability of the OS if you are able to influence this. In this case a simple Ping monitor will suffice.

You can synthetically add agent or machine data in order to cover all of the data sets. Wire data is an attractive option in the application area, but not at infrastructure level. To do this you will need to know a lot about how the infrastructure is set up, and that's precisely what you wanted to outsource.

Variant 3 – Software-as-a-Service

The supplier arranges everything. You are only the user of the application that you want to buy. The supplier will usually have published his own SLA, but how transparent is it?

The best bet here is to chose a synthetic solution yourself and have it assessed independently. Use these figures to check the quality of the service and to confront the supplier with results other than what have been agreed.

To conclude ...

Outsourcing is based on trust but to many companies IT is a matter of life or death: it's importance is inestimable. You want to avoid a situation where finger-pointing starts between you and the supplier if faults occur. Make sure that you give careful thought to monitoring quality also during the implementation of the outsourcing.

How have you experienced this as a user? How do you monitor your outsourcing contracts, and what were your experiences with an outsourcing party? I'd like to hear your experiences - I can learn from them too.

Coen Meerbeek is an Online Performance Consultant at Blue Factory Internet.

Hot Topics

The Latest

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.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...