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Catchpoint Blog

SRE Report: AI optimism and the economics of effort SRE Report: Why fast is what users trust SRE Report 2026: What surprised us, what didn't, and why the gaps matter most The SRE Report 2026: Defensible Ns Why Synthetic Tracing Delivers Better Data, Not Just More Data A New Chapter: LogicMonitor + Catchpoint – A Personal Note from Mehdi Mezmo + Catchpoint deliver observability SREs can rely on The four pillars holding up your digital business, and what happens when they crumble When payments pause: lessons from a global payments outage Observability 2025 Decoded: What the DZone Report Means for SLO-Driven Ops The next evolution of WebPageTest has arrived, and it’s a game-changer The Monitoring Blind Spot That Could Cost You Black Friday Powering Mexico’s Digital Future: Expanded Internet Observability with Catchpoint The Next Chapter of WebPageTest: Your New Experience Starts Soon SRE Report Retrospectives — Have AIOps Predictions Held Up? When BGP becomes UX: The inside story of a SaaS routing decision gone wrong (or right) Session Replay explained: A guide to seeing digital experience through your user’s eyes Making the invisible visible: Are your cloud firewalls and DDoS protection really working? Why it’s time to move beyond APM: Monitoring from the user’s perspective When metrics mislead: Inside the 2025 Retail Web Performance Benchmark The vendor trap: why your next outage won’t be your fault—but will be your problem LLMs don’t stand still: How to monitor and trust the models powering your AI Semantic Caching: What We Measured, Why It Matters The Annual SRE Survey Is Open—We Want to Hear from You Observability isn’t about the tool. It’s about the truth Invisible dependencies, visible impact: Lessons from the Google Cloud outage Real-time detection of BGP blackholing and prefix hijacks Leading analyst firm reveals the real cost of internet disruptions The Power of Over 3000 Intelligent Observability Agents Monitoring in the Age of Complexity: 5 Assumptions CIOs Need to Rethink
Why Intelligent Traffic Steering is Critical for Performa...
2025-03-31 · via Catchpoint Blog

in this blog post

In today’s world of globally distributed applications, user experience is everything. Whether your platform runs across multiple cloud providers or uses a Multi CDN with numerous points of presence (PoPs), efficiently routing user traffic can make or break performance. That's where intelligent traffic steering becomes not just a nice-to-have, but a must-have.

This diagram illustrates the complex web of connections between a modern core banking system and its extended digital ecosystem. It includes cloud-based services, APIs, third-party integrations, CDN layers, internal networks, and diverse user access points. Components are connected via ISPs and DNS lookups, highlighting potential performance bottlenecks and visibility gaps across digital services.

End-to-end ecosystem of a core banking system

At our recent joint webinar with IBM NS1, we explored how Catchpoint's real-time Internet Performance Monitoring (IPM) data integrates with NS1's powerful traffic steering capabilities to solve a critical problem: ensuring traffic is routed not just to the nearest server, but to the best-performing one.

The challenge: static routing in a dynamic world

Traditionally, DNS-based routing strategies like round-robin or proximity-based decisions have been used to direct user traffic. However, these methods don't account for real-time performance metrics. A nearby server might be under heavy load or facing regional ISP issues, yet static routing would still direct users there, resulting in higher latency and degraded experience.

This issue is magnified in multi-cloud and multi-CDN environments, where routing inefficiencies can also drive up cloud and infrastructure costs. Without real-time visibility, you’re essentially flying blind.

The maps on the left compare AWS and Oracle response times across different global regions, highlighting areas where performance varies significantly. Green indicates faster response times, while red signals slower performance. The lower maps offer a city-level breakdown. The chart on the right shows traffic distribution before dynamic routing, with traffic nearly evenly split: 52% to AWS and 48% to Oracle.

Regional performance differences and traffic distribution between AWS and Oracle cloud environments

The image above provides a great example of this challenge in a multi-cloud environment using AWS and Oracle. The global response time maps show noticeable regional performance differences between the two providers. Yet, because traffic is being routed using a static round-robin method (seen in the 52% AWS / 48% Oracle split), users in underperforming regions are still being directed to slower endpoints. For instance, the Oracle path shows significant latency in North America and Oceania, while AWS performs better there. Still, the lack of performance-aware steering results in an inefficient and inconsistent user experience.

The Solution: Adaptive traffic steering with Catchpoint and IBM NS1

By combining Catchpoint’s extensive Global Agent Network—spanning 2,880+ intelligent agents and millions of connected devices—with IBM NS1’s real-time DNS traffic steering capabilities, organizations can shift from reactive to proactive, performance-driven routing.

 This graphic illustrates the scale and distribution of Catchpoint’s monitoring infrastructure, showcasing the world’s largest independent agent network. With 2,986 intelligent agents deployed across 106 countries and 348 cities.

Catchpoint’s Global Agent Network

Check out the video below for an overview of how Catchpoint and IBM NS1 work together to enable real-time, performance-aware traffic steering.  

Catchpoint IPM continuously collects metrics such as DNS resolution time, connect time, SSL, wait time, and total response time using synthetic tests from backbone, last mile, cloud, and wireless networks. This real-time telemetry is then fed into NS1's filter chain logic.

NS1 applies this data through its intelligent filter chains, which support decisions based on geolocation, ASN, availability, and actual performance. The diagram below illustrates how the end user’s DNS request is routed through NS1, where IPM-powered performance metrics guide the decision-making engine. This ensures traffic is not just sent to the closest server, but the best-performing server at that moment.

This diagram shows the flow of a DNS request and how real-time performance metrics power intelligent traffic steering with NS1 and Catchpoint.

How real time metrics power intelligent traffic steering with NS1 and Catchpoint

This feedback loop enables true adaptive routing, drastically reducing latency and improving reliability without waiting for end-user impact to trigger change.  

Feeding Catchpoint Data into IBM NS1 Connect

Catchpoint enables real-time performance telemetry to be pushed directly into NS1 IBM Connect using Data Webhooks. This integration empowers traffic steering logic to be based on real, actionable insights rather than static assumptions.

Metrics such as DNS time, connect time, SSL handshake duration, wait time (time to first byte), TTFB, and overall response time are just the start. Users can also feed in custom data sources like CDN-specific server timing metrics or application-specific performance KPIs. This flexibility ensures that the traffic steering logic is tailor-fit to business and technical requirements whether optimizing for speed, reliability, or cost.

This dashboard shows how real user metrics from Catchpoint are fed into IBM NS1 Connect to compare performance across different cloud providers—in this case, AWS and Oracle. The graph visualizes real-time response times over a 6-hour window, highlighting latency variations that inform traffic steering decisions.

Using Catchpoint IPM data in IBM NS1 Connect to compare multi-cloud performance

All this data is consumed by NS1 Pulsar's filter chain engine, which uses it to dynamically apply routing rules. These filters can be chained together based on geography, ASN, latency, availability, or any of the performance metrics provided by Catchpoint IPM.

NS1 Pulsar filter chain using Catchpoint data to route traffic, with dashboards showing traffic distribution across cloud providers and ASNs.

NS1 Pulsar filter chain powered by Catchpoint IPM data

As a result, routing decisions aren't just smart they're precisely aligned with your most up-to-date performance landscape.

Real-world impact: Better performance and lower costs

In our joint implementation example, traffic was initially split evenly between AWS and Oracle cloud environments. After enabling dynamic routing with IPM-fed data, 77% of the traffic shifted to the better-performing cloud region, significantly reducing wait times and improving end-user experience.

This side-by-side comparison shows traffic distribution across AWS and Oracle before and after implementing performance-based traffic steering. Before steering, traffic was nearly evenly split (52% AWS, 48% Oracle). After steering, 77% of traffic shifted to AWS, resulting in more efficient regional routing and improved user experience.

Impact of traffic steering on cloud traffic distribution

The results speak for themselves:

  • 86.57% reduction in wait time on AWS
  • 85.30% reduction in wait time on Oracle

This graph compares the reduction in wait time (GM Wait in ms) for AWS and Oracle environments after implementing intelligent traffic steering. AWS saw an 86.57% reduction, while Oracle experienced an 85.30% reduction, demonstrating the effectiveness of routing decisions informed by real-time performance data.

Wait time improvements after enabling performance-based traffic steering

Wait time improvements after enabling performance-based traffic steering

These improvements not only enhance digital experience but also minimize the frustration and churn that come with sluggish performance.

Beyond latency improvements, traffic steering also drives operational efficiency. By understanding which cloud or PoP performs best in specific regions, businesses can:

  • Scale down underperforming infrastructure
  • Optimize cloud costs by avoiding overprovisioning
  • Improve regional performance by steering traffic intelligently

This is the power of proactive, performance-aware traffic steering.

Proactive, not reactive

Most traditional monitoring tools rely on real user monitoring (RUM) data, which only becomes useful after users have already experienced performance issues. Catchpoint IPM flips this model by using synthetic testing for proactive insights. This means traffic can be rerouted before users are impacted, creating a continuous feedback loop that enhances reliability and experience.

Next up: the setup guide

In an upcoming post, we’ll walk through the step-by-step implementation of intelligent traffic steering—from configuring Catchpoint tests to feeding metrics into NS1 and setting up intelligent filter chains. Watch this space.  

Learn More

In today’s world of globally distributed applications, user experience is everything. Whether your platform runs across multiple cloud providers or uses a Multi CDN with numerous points of presence (PoPs), efficiently routing user traffic can make or break performance. That's where intelligent traffic steering becomes not just a nice-to-have, but a must-have.

This diagram illustrates the complex web of connections between a modern core banking system and its extended digital ecosystem. It includes cloud-based services, APIs, third-party integrations, CDN layers, internal networks, and diverse user access points. Components are connected via ISPs and DNS lookups, highlighting potential performance bottlenecks and visibility gaps across digital services.

End-to-end ecosystem of a core banking system

At our recent joint webinar with IBM NS1, we explored how Catchpoint's real-time Internet Performance Monitoring (IPM) data integrates with NS1's powerful traffic steering capabilities to solve a critical problem: ensuring traffic is routed not just to the nearest server, but to the best-performing one.

The challenge: static routing in a dynamic world

Traditionally, DNS-based routing strategies like round-robin or proximity-based decisions have been used to direct user traffic. However, these methods don't account for real-time performance metrics. A nearby server might be under heavy load or facing regional ISP issues, yet static routing would still direct users there, resulting in higher latency and degraded experience.

This issue is magnified in multi-cloud and multi-CDN environments, where routing inefficiencies can also drive up cloud and infrastructure costs. Without real-time visibility, you’re essentially flying blind.

The maps on the left compare AWS and Oracle response times across different global regions, highlighting areas where performance varies significantly. Green indicates faster response times, while red signals slower performance. The lower maps offer a city-level breakdown. The chart on the right shows traffic distribution before dynamic routing, with traffic nearly evenly split: 52% to AWS and 48% to Oracle.

Regional performance differences and traffic distribution between AWS and Oracle cloud environments

The image above provides a great example of this challenge in a multi-cloud environment using AWS and Oracle. The global response time maps show noticeable regional performance differences between the two providers. Yet, because traffic is being routed using a static round-robin method (seen in the 52% AWS / 48% Oracle split), users in underperforming regions are still being directed to slower endpoints. For instance, the Oracle path shows significant latency in North America and Oceania, while AWS performs better there. Still, the lack of performance-aware steering results in an inefficient and inconsistent user experience.

The Solution: Adaptive traffic steering with Catchpoint and IBM NS1

By combining Catchpoint’s extensive Global Agent Network—spanning 2,880+ intelligent agents and millions of connected devices—with IBM NS1’s real-time DNS traffic steering capabilities, organizations can shift from reactive to proactive, performance-driven routing.

 This graphic illustrates the scale and distribution of Catchpoint’s monitoring infrastructure, showcasing the world’s largest independent agent network. With 2,986 intelligent agents deployed across 106 countries and 348 cities.

Catchpoint’s Global Agent Network

Check out the video below for an overview of how Catchpoint and IBM NS1 work together to enable real-time, performance-aware traffic steering.  

Catchpoint IPM continuously collects metrics such as DNS resolution time, connect time, SSL, wait time, and total response time using synthetic tests from backbone, last mile, cloud, and wireless networks. This real-time telemetry is then fed into NS1's filter chain logic.

NS1 applies this data through its intelligent filter chains, which support decisions based on geolocation, ASN, availability, and actual performance. The diagram below illustrates how the end user’s DNS request is routed through NS1, where IPM-powered performance metrics guide the decision-making engine. This ensures traffic is not just sent to the closest server, but the best-performing server at that moment.

This diagram shows the flow of a DNS request and how real-time performance metrics power intelligent traffic steering with NS1 and Catchpoint.

How real time metrics power intelligent traffic steering with NS1 and Catchpoint

This feedback loop enables true adaptive routing, drastically reducing latency and improving reliability without waiting for end-user impact to trigger change.  

Feeding Catchpoint Data into IBM NS1 Connect

Catchpoint enables real-time performance telemetry to be pushed directly into NS1 IBM Connect using Data Webhooks. This integration empowers traffic steering logic to be based on real, actionable insights rather than static assumptions.

Metrics such as DNS time, connect time, SSL handshake duration, wait time (time to first byte), TTFB, and overall response time are just the start. Users can also feed in custom data sources like CDN-specific server timing metrics or application-specific performance KPIs. This flexibility ensures that the traffic steering logic is tailor-fit to business and technical requirements whether optimizing for speed, reliability, or cost.

This dashboard shows how real user metrics from Catchpoint are fed into IBM NS1 Connect to compare performance across different cloud providers—in this case, AWS and Oracle. The graph visualizes real-time response times over a 6-hour window, highlighting latency variations that inform traffic steering decisions.

Using Catchpoint IPM data in IBM NS1 Connect to compare multi-cloud performance

All this data is consumed by NS1 Pulsar's filter chain engine, which uses it to dynamically apply routing rules. These filters can be chained together based on geography, ASN, latency, availability, or any of the performance metrics provided by Catchpoint IPM.

NS1 Pulsar filter chain using Catchpoint data to route traffic, with dashboards showing traffic distribution across cloud providers and ASNs.

NS1 Pulsar filter chain powered by Catchpoint IPM data

As a result, routing decisions aren't just smart they're precisely aligned with your most up-to-date performance landscape.

Real-world impact: Better performance and lower costs

In our joint implementation example, traffic was initially split evenly between AWS and Oracle cloud environments. After enabling dynamic routing with IPM-fed data, 77% of the traffic shifted to the better-performing cloud region, significantly reducing wait times and improving end-user experience.

This side-by-side comparison shows traffic distribution across AWS and Oracle before and after implementing performance-based traffic steering. Before steering, traffic was nearly evenly split (52% AWS, 48% Oracle). After steering, 77% of traffic shifted to AWS, resulting in more efficient regional routing and improved user experience.

Impact of traffic steering on cloud traffic distribution

The results speak for themselves:

  • 86.57% reduction in wait time on AWS
  • 85.30% reduction in wait time on Oracle

This graph compares the reduction in wait time (GM Wait in ms) for AWS and Oracle environments after implementing intelligent traffic steering. AWS saw an 86.57% reduction, while Oracle experienced an 85.30% reduction, demonstrating the effectiveness of routing decisions informed by real-time performance data.

Wait time improvements after enabling performance-based traffic steering

Wait time improvements after enabling performance-based traffic steering

These improvements not only enhance digital experience but also minimize the frustration and churn that come with sluggish performance.

Beyond latency improvements, traffic steering also drives operational efficiency. By understanding which cloud or PoP performs best in specific regions, businesses can:

  • Scale down underperforming infrastructure
  • Optimize cloud costs by avoiding overprovisioning
  • Improve regional performance by steering traffic intelligently

This is the power of proactive, performance-aware traffic steering.

Proactive, not reactive

Most traditional monitoring tools rely on real user monitoring (RUM) data, which only becomes useful after users have already experienced performance issues. Catchpoint IPM flips this model by using synthetic testing for proactive insights. This means traffic can be rerouted before users are impacted, creating a continuous feedback loop that enhances reliability and experience.

Next up: the setup guide

In an upcoming post, we’ll walk through the step-by-step implementation of intelligent traffic steering—from configuring Catchpoint tests to feeding metrics into NS1 and setting up intelligent filter chains. Watch this space.  

Learn More