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3 Key Findings from the Cyber Monday Web Performance Index

Sven Hammar

Online retailers stand to make a lot of money on Cyber Monday as long as their infrastructure can keep up with customers. If your company's site goes offline or substantially slows down, you're going to lose sales. And even top ecommerce sites experience performance or stability issues at peak loads, like Cyber Monday, according to Apica's Cyber Monday Web Performance Index.

The Cyber Monday Web Performance Index is built to gauge how well top sites in the market are performing. The purpose is to measure and rank the top-performing retail websites by examining elements like Document Object Model completion time, render time, minimum load time, and maximum load time. This year's index included the Internet Retailer Hot 100, which features a range of the most popular eCommerce sites on the Internet. Cyber Monday is, of course, an ideal day to test as these sites are pushed harder than at any other time of year, creating the right load testing conditions.

Key findings of the Cyber Monday Web Performance Index include:

1. Top 10 eCommerce Websites Are Healthy, But the Rest Are Lagging Behind

The top 10 rated websites are in excellent shape and only show cracks when the servers get overloaded with traffic. However, the servers are able to stay online and continue to function quickly enough to keep visitors on the site. Upwards of two-thirds of visitors will switch to a competitor's site if your company's site is too slow, so this factor is crucially important.

One study found top-rated sites like the Apple Store and Microsoft Store manage minimum load times (two seconds or less) under low-load conditions, but both experienced 10-second load times when visitors pushed the infrastructure under peak demand. Visitors may stick through modest increases, but they are likely to leave when load times explode to Keurig's 38-second, QVC's 131-second, and Avon's 147-second maximum load times.

2. Scaling and Stability Are a Major Issue Across the Industry

Hosting infrastructure that isn't built to scale can produce a very poor "stability" score, meaning there is a very substantial difference in the minimum and maximum load times. Even sites with optimized designs can crumble under demand if the infrastructure can't scale properly.

While the Apple Store's overall performance was fantastic, the site suffered a bit in stability metrics: 1.7-second load times fell to 10 seconds during the busiest periods. Costco's site scaled better, with consistent performance ranging between 1.8-2.9 seconds. Site operators can minimize load time increases with proper load testing and performance monitoring practices. The testing data helps businesses plan scaling infrastructure to grow with demand spikes, while avoiding cases where businesses overspend on more power than needed. 

3. Deception Through Progressive Page Loading Is the Key to Speed

Developers can work with the system by loading the page's basic functionalities as quickly as possible, and its auxiliary components afterward. This way, even if the page takes eight seconds to load, the visitor thinks it only took two.

Costco manages this well. The site's pages are very image-heavy, making it impossible to optimally compress the images and send them to a visitor's system in under three seconds. Instead, the page completes the DOM in just 2.1 seconds, allowing the user to start interacting with the page while the system loads off-screen content faster than the visitor can access it.

Read: 3 Ways to Improve Your Website for Cyber Monday

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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 ...

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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.

3 Key Findings from the Cyber Monday Web Performance Index

Sven Hammar

Online retailers stand to make a lot of money on Cyber Monday as long as their infrastructure can keep up with customers. If your company's site goes offline or substantially slows down, you're going to lose sales. And even top ecommerce sites experience performance or stability issues at peak loads, like Cyber Monday, according to Apica's Cyber Monday Web Performance Index.

The Cyber Monday Web Performance Index is built to gauge how well top sites in the market are performing. The purpose is to measure and rank the top-performing retail websites by examining elements like Document Object Model completion time, render time, minimum load time, and maximum load time. This year's index included the Internet Retailer Hot 100, which features a range of the most popular eCommerce sites on the Internet. Cyber Monday is, of course, an ideal day to test as these sites are pushed harder than at any other time of year, creating the right load testing conditions.

Key findings of the Cyber Monday Web Performance Index include:

1. Top 10 eCommerce Websites Are Healthy, But the Rest Are Lagging Behind

The top 10 rated websites are in excellent shape and only show cracks when the servers get overloaded with traffic. However, the servers are able to stay online and continue to function quickly enough to keep visitors on the site. Upwards of two-thirds of visitors will switch to a competitor's site if your company's site is too slow, so this factor is crucially important.

One study found top-rated sites like the Apple Store and Microsoft Store manage minimum load times (two seconds or less) under low-load conditions, but both experienced 10-second load times when visitors pushed the infrastructure under peak demand. Visitors may stick through modest increases, but they are likely to leave when load times explode to Keurig's 38-second, QVC's 131-second, and Avon's 147-second maximum load times.

2. Scaling and Stability Are a Major Issue Across the Industry

Hosting infrastructure that isn't built to scale can produce a very poor "stability" score, meaning there is a very substantial difference in the minimum and maximum load times. Even sites with optimized designs can crumble under demand if the infrastructure can't scale properly.

While the Apple Store's overall performance was fantastic, the site suffered a bit in stability metrics: 1.7-second load times fell to 10 seconds during the busiest periods. Costco's site scaled better, with consistent performance ranging between 1.8-2.9 seconds. Site operators can minimize load time increases with proper load testing and performance monitoring practices. The testing data helps businesses plan scaling infrastructure to grow with demand spikes, while avoiding cases where businesses overspend on more power than needed. 

3. Deception Through Progressive Page Loading Is the Key to Speed

Developers can work with the system by loading the page's basic functionalities as quickly as possible, and its auxiliary components afterward. This way, even if the page takes eight seconds to load, the visitor thinks it only took two.

Costco manages this well. The site's pages are very image-heavy, making it impossible to optimally compress the images and send them to a visitor's system in under three seconds. Instead, the page completes the DOM in just 2.1 seconds, allowing the user to start interacting with the page while the system loads off-screen content faster than the visitor can access it.

Read: 3 Ways to Improve Your Website for Cyber Monday

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.