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3 Steps to Avoid Service Level Disagreements

John Lucania

You ask a friend to "check" on your dog while you're away. Obliging, your friend goes to your house, rings the doorbell to listen for a bark and then returns to their car. However, when you made the request you really wanted your friend to go into the house for a bit, make sure there were no issues and immediately notify you if something was wrong. A perfect case of a poorly negotiated SLA!

What Are SLAs and Why Do We Have Them?

A Service Level Agreement is a contractual agreement between a service provider and a customer regarding the level of service that will be provided. SLAs are beneficial for both parties – they define what is being purchased and also the roles and responsibilities to remediate any issues. A well-constructed SLA strengthens the customer relationship by bridging the gap between the vendor services and customer expectations. With software services, websites and applications becoming increasingly complex, negotiating and adhering to SLAs is more important than ever.

What Do SLAs Typically Cover?

It is very important to keep the SLA simple, measurable and realistic. SLAs typically cover:

■ Description of overall services

■ Service performance metrics

■ Financial aspects of service delivery

■ Responsibilities of service provider and customer

■ Disaster recovery process

■ Review process and frequency of review

■ Termination of agreement process

The specific performance metrics that manage the compliance of service delivery are called Service Level Objectives (SLOs). In the context of web services, SLOs would cover availability, uptime and response time for the service; probably accessibility by geography and problem resolution metrics such as mean time to answer and/or mean time to repair.

Is a service really available if the customer cannot use it? A well-constructed SLA should include a unit of measurement that defines availability to align with the customer's critical business process, and not just the availability of the servers URL/URI or log in process.

Using our doorbell analogy in web services context, a poorly negotiated SLA will ring the doorbell equivalent of looking for the 200 OK from the server. The 200 code, like the dog's bark, will just tell you that someone is home and not the actual condition i.e. health of the service. Checking a website or authenticating without validating the business process you rely on, exposes you to downtime without financial leverage.

Step One: Measure What You Have

What can you, the service provider, do to get most out of SLAs? Let's say you are providing a marketing automation system to an enterprise that will run its global web activities over your system. You have promised them 95% availability and suitable performance from the USA east and west coasts, UK, Germany and India.

Before you commit to an exact performance target, hopefully you have measured what you have now. You need to baseline the performance of your service in order to understand what you can offer. No sense promising 95% availability in India if your system typically only is available 80% of the time in India. However, when it comes to SLAs, under committing can lead to lost business opportunities and lost revenue. You can use your SLA as a competitive advantage, only if you know what you can and cannot deliver. Baselining performance will help you commit not too much, not too little but just right!

Using a synthetic performance monitoring tool, you can baseline your services. Ex. Let's say you want to measure performance of a user log in activity from UK during business hours. You can record this multi-step user transaction and use that script to create a monitor. Next, you can create an SLA for that monitor by setting desired response time and availability objective. A quality synthetic tool will not only see if the service is up and running but also measures the response times and functional correctness from its global monitoring nodes; assuring SLA compliance by comparing the actual performance with SLA objectives.

By observing your monitors in real time , as well as from the SLA summary, you get the realistic and complete picture of your performance.

Step Two: Include What Applies to Your Customer, Exclude the Rest

If your agreement states that you will provide a certain level of service for east coast, west coast, UK, Germany and India, don't provide the data regarding the Netherlands and Africa. You also need to account for operational time for you, clearly mention the descriptions of your maintenance windows and/or upgrades. When building the service-level-agreement, keep in mind the operating periods as well as both ongoing and one-time events.

Customers are getting used to the multi tenancy nature of service providers. So be open to SLA negotiations, however calculate the cost associated with customization and make sure it aligns with your aggregate business interest in that customer. Many times the customer can also be found in over/under demanding situations. Baselining customer's performance requirements will lead to more realistic SLAs and a win-win situation for both parties.

Step Three: Monitor Aggressively

In order to make realistic availability and performance goals and keep them, you have to take enough measurements so that a single failure doesn't skew the overall results.

I want to talk a little bit about the law of large numbers: which is a principle of probability and statistics. The law of large numbers states that as a sample size grows, its mean will get closer and closer to the average of the whole population.

This is an important context for monitoring and setting SLAs. If you run an availability test from 5 locations once an hour, one time, and one of those tests fails. Your availability is down to 80 percent. If you run tests from 10 locations every 5 minutes for an hour that is 50 tests – and if 1 fails then your availability is now 98%! Less aggressive monitoring leaves you vulnerable to an SLA violation for a brief outage.

In conclusion, service level agreements are valuable for you and your customers. These three steps will help you look at SLAs as an opportunity than a restriction.

■ Make the right agreement based on baseline performance

■ Measure the correct things with the correct frequency

■ Take enough measurements to smooth out variability

John Lucania is Senior Sales Engineer at SmartBear Software.

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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 ...

3 Steps to Avoid Service Level Disagreements

John Lucania

You ask a friend to "check" on your dog while you're away. Obliging, your friend goes to your house, rings the doorbell to listen for a bark and then returns to their car. However, when you made the request you really wanted your friend to go into the house for a bit, make sure there were no issues and immediately notify you if something was wrong. A perfect case of a poorly negotiated SLA!

What Are SLAs and Why Do We Have Them?

A Service Level Agreement is a contractual agreement between a service provider and a customer regarding the level of service that will be provided. SLAs are beneficial for both parties – they define what is being purchased and also the roles and responsibilities to remediate any issues. A well-constructed SLA strengthens the customer relationship by bridging the gap between the vendor services and customer expectations. With software services, websites and applications becoming increasingly complex, negotiating and adhering to SLAs is more important than ever.

What Do SLAs Typically Cover?

It is very important to keep the SLA simple, measurable and realistic. SLAs typically cover:

■ Description of overall services

■ Service performance metrics

■ Financial aspects of service delivery

■ Responsibilities of service provider and customer

■ Disaster recovery process

■ Review process and frequency of review

■ Termination of agreement process

The specific performance metrics that manage the compliance of service delivery are called Service Level Objectives (SLOs). In the context of web services, SLOs would cover availability, uptime and response time for the service; probably accessibility by geography and problem resolution metrics such as mean time to answer and/or mean time to repair.

Is a service really available if the customer cannot use it? A well-constructed SLA should include a unit of measurement that defines availability to align with the customer's critical business process, and not just the availability of the servers URL/URI or log in process.

Using our doorbell analogy in web services context, a poorly negotiated SLA will ring the doorbell equivalent of looking for the 200 OK from the server. The 200 code, like the dog's bark, will just tell you that someone is home and not the actual condition i.e. health of the service. Checking a website or authenticating without validating the business process you rely on, exposes you to downtime without financial leverage.

Step One: Measure What You Have

What can you, the service provider, do to get most out of SLAs? Let's say you are providing a marketing automation system to an enterprise that will run its global web activities over your system. You have promised them 95% availability and suitable performance from the USA east and west coasts, UK, Germany and India.

Before you commit to an exact performance target, hopefully you have measured what you have now. You need to baseline the performance of your service in order to understand what you can offer. No sense promising 95% availability in India if your system typically only is available 80% of the time in India. However, when it comes to SLAs, under committing can lead to lost business opportunities and lost revenue. You can use your SLA as a competitive advantage, only if you know what you can and cannot deliver. Baselining performance will help you commit not too much, not too little but just right!

Using a synthetic performance monitoring tool, you can baseline your services. Ex. Let's say you want to measure performance of a user log in activity from UK during business hours. You can record this multi-step user transaction and use that script to create a monitor. Next, you can create an SLA for that monitor by setting desired response time and availability objective. A quality synthetic tool will not only see if the service is up and running but also measures the response times and functional correctness from its global monitoring nodes; assuring SLA compliance by comparing the actual performance with SLA objectives.

By observing your monitors in real time , as well as from the SLA summary, you get the realistic and complete picture of your performance.

Step Two: Include What Applies to Your Customer, Exclude the Rest

If your agreement states that you will provide a certain level of service for east coast, west coast, UK, Germany and India, don't provide the data regarding the Netherlands and Africa. You also need to account for operational time for you, clearly mention the descriptions of your maintenance windows and/or upgrades. When building the service-level-agreement, keep in mind the operating periods as well as both ongoing and one-time events.

Customers are getting used to the multi tenancy nature of service providers. So be open to SLA negotiations, however calculate the cost associated with customization and make sure it aligns with your aggregate business interest in that customer. Many times the customer can also be found in over/under demanding situations. Baselining customer's performance requirements will lead to more realistic SLAs and a win-win situation for both parties.

Step Three: Monitor Aggressively

In order to make realistic availability and performance goals and keep them, you have to take enough measurements so that a single failure doesn't skew the overall results.

I want to talk a little bit about the law of large numbers: which is a principle of probability and statistics. The law of large numbers states that as a sample size grows, its mean will get closer and closer to the average of the whole population.

This is an important context for monitoring and setting SLAs. If you run an availability test from 5 locations once an hour, one time, and one of those tests fails. Your availability is down to 80 percent. If you run tests from 10 locations every 5 minutes for an hour that is 50 tests – and if 1 fails then your availability is now 98%! Less aggressive monitoring leaves you vulnerable to an SLA violation for a brief outage.

In conclusion, service level agreements are valuable for you and your customers. These three steps will help you look at SLAs as an opportunity than a restriction.

■ Make the right agreement based on baseline performance

■ Measure the correct things with the correct frequency

■ Take enough measurements to smooth out variability

John Lucania is Senior Sales Engineer at SmartBear Software.

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 ...