9 Key Performance Considerations for App Rollouts

Bruce Kosbab

For a successful application rollout, it is vital to assess the user experience appropriately and have an understanding of how the new app impacts your already deployed apps and infrastructure. This requires a great deal of preparation across various IT functions, from network to application teams. To put your team on the path to a successful rollout, take the time to consider the following points before the wide-scale launch:

1. LOCATION

Determine the topology. Where will users access the application and where will the app reside (there may be more than one hosting location)? Is the location intended to be on-premise or off-premise? How will the app behave from remote locations or on mobile devices? How will this impact performance?

2. DATA TRAFFIC

Examine the expected regular traffic load and profile. How does this new app rank in terms of bandwidth priority compared to other apps? How will the additional traffic generated by this new application impact existing app performance? What about overall impact on end-user Quality of Experience?

3. BASELINE

Establish a performance and capacity baseline for the existing infrastructure and applications at all locations. What potential problems could arise after deployment? How does the impact compare to your baseline?

4. PIPE CAPACITY

Determine the most likely path the traffic will take between the application and those user locations. Assess available capacity of both your internal and WAN network – is it sufficient to carry the additional load?

5. LATENCY

Define the new app’s sensitivity to latency. Think about the delay incurred to serve remote locations and mobile users. How will this impact your app deployment?

6. POTENTIAL BOTTLENECKS

Examine capacity and traffic considerations. Is the application using cloud services? Do you have the capability and capacity on the hosting site to allow traffic between the on-premise and off-premise application components?

7. MONITORING

Establish the critical nature of the app. Is this app important enough that you need to configure it for monitoring, either temporarily for the initial rollout or longer-term?

8. LIMITED ROLLOUT

Consider deploying the application to a limited number of test users in each site to get some preliminary testing done. Set expectations for how the application should perform and give users adequate time to acclimate and validate the new application as part of their workflow. How are users receiving the new application? What is the user experience like? Are there any issues that need to be resolved immediately?

9. QUALITY OF EXPERIENCE

After crossing all these hurdles, you can consider moving ahead with a full rollout. Continue to measure the user experience for the new and existing apps, and compare this to your pre-deployment baseline to determine early warning signs of potentially user-impacting behavior. How is the new app stacking up? With any luck the new application will prove to be a valuable addition to the company.

Bruce Kosbab is CTO of Fluke Networks.

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9 Key Performance Considerations for App Rollouts

Bruce Kosbab

For a successful application rollout, it is vital to assess the user experience appropriately and have an understanding of how the new app impacts your already deployed apps and infrastructure. This requires a great deal of preparation across various IT functions, from network to application teams. To put your team on the path to a successful rollout, take the time to consider the following points before the wide-scale launch:

1. LOCATION

Determine the topology. Where will users access the application and where will the app reside (there may be more than one hosting location)? Is the location intended to be on-premise or off-premise? How will the app behave from remote locations or on mobile devices? How will this impact performance?

2. DATA TRAFFIC

Examine the expected regular traffic load and profile. How does this new app rank in terms of bandwidth priority compared to other apps? How will the additional traffic generated by this new application impact existing app performance? What about overall impact on end-user Quality of Experience?

3. BASELINE

Establish a performance and capacity baseline for the existing infrastructure and applications at all locations. What potential problems could arise after deployment? How does the impact compare to your baseline?

4. PIPE CAPACITY

Determine the most likely path the traffic will take between the application and those user locations. Assess available capacity of both your internal and WAN network – is it sufficient to carry the additional load?

5. LATENCY

Define the new app’s sensitivity to latency. Think about the delay incurred to serve remote locations and mobile users. How will this impact your app deployment?

6. POTENTIAL BOTTLENECKS

Examine capacity and traffic considerations. Is the application using cloud services? Do you have the capability and capacity on the hosting site to allow traffic between the on-premise and off-premise application components?

7. MONITORING

Establish the critical nature of the app. Is this app important enough that you need to configure it for monitoring, either temporarily for the initial rollout or longer-term?

8. LIMITED ROLLOUT

Consider deploying the application to a limited number of test users in each site to get some preliminary testing done. Set expectations for how the application should perform and give users adequate time to acclimate and validate the new application as part of their workflow. How are users receiving the new application? What is the user experience like? Are there any issues that need to be resolved immediately?

9. QUALITY OF EXPERIENCE

After crossing all these hurdles, you can consider moving ahead with a full rollout. Continue to measure the user experience for the new and existing apps, and compare this to your pre-deployment baseline to determine early warning signs of potentially user-impacting behavior. How is the new app stacking up? With any luck the new application will prove to be a valuable addition to the company.

Bruce Kosbab is CTO of Fluke Networks.

Related Links

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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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

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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.

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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 ...

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