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Image Optimization Best Practices for Device Breakpoints - Part 1

Ari Weil

An effective breakpoint strategy helps deliver sharp, properly sized images, which are some of the most compelling pieces of content on a web page. Lack of such a strategy can lead to jagged images or ones that take too long to render due to excessive size, potentially reducing the overall effectiveness of web pages — and driving down the quality of the user experience.

Creation of derivative images at properly determined breakpoints is critical but often challenging for web developers and designers to achieve. Generating the right number of variants — and at the correct widths and spacings — for their users' critical devices can provide a great balance between byte savings and cache dilution.

While it's true that having too few breakpoints can improve offload, it can also deliver many unused bytes and place an unnecessarily high rendering burden on the device and browser. At the same time, when there are too many breakpoints, it may result in the opposite effect. Finding the right balance mandates some time and resource commitment, but the benefits are truly worth the exercise.

In this 2-part blog, we will explore just how significant image breakpoints are to businesses, and some important device-related factors to consider in image breakpoint decisions — from screen resolution market share and user base, to device characteristics to pixel density — to deliver the optimally-sized web image every time.

The Byte Loss Breakdown

Getting image breakpoints right is especially important at larger image widths. To illustrate, let's look at this example in which a web page is displaying two images that need to be resized by the browser 50 pixels in both height and width:


As you can see, delivering a 200 X 200 pixel (px) image for a 150 X 150 px use case (50 extra pixels in width and height) results in 70,000 wasted bytes required in memory for the browser to display the image than if the image were delivered at 150 X 150 px.

When looking at the larger image example, delivering a 600 X 600 px image instead of a 550 X 550 px use case (still 50 extra pixels in height and width) resulted in 230,000 wasted bytes, or 3.3-times more wasted bytes. This illustrates why it's recommended to have more breakpoints at larger image sizes (in this case, images larger than 700 px in width).

Another example to consider involves displaying images on a typical smartphone, in this case, the Samsung Galaxy S5. This model has a screen resolution of 1080 X 1920 px. Take, for example, a server sending an image of 1200 X 2000 px dimensions that must be resized to 1080 X 1920 px. This would result in more than 1,305,600 bytes — that's 1.3 MB — waste, not to mention a significant degradation in user experience.

Read Image Optimization Best Practices for Device Breakpoints - Part 2, covering 4 tips for getting image breakpoints right.

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Image Optimization Best Practices for Device Breakpoints - Part 1

Ari Weil

An effective breakpoint strategy helps deliver sharp, properly sized images, which are some of the most compelling pieces of content on a web page. Lack of such a strategy can lead to jagged images or ones that take too long to render due to excessive size, potentially reducing the overall effectiveness of web pages — and driving down the quality of the user experience.

Creation of derivative images at properly determined breakpoints is critical but often challenging for web developers and designers to achieve. Generating the right number of variants — and at the correct widths and spacings — for their users' critical devices can provide a great balance between byte savings and cache dilution.

While it's true that having too few breakpoints can improve offload, it can also deliver many unused bytes and place an unnecessarily high rendering burden on the device and browser. At the same time, when there are too many breakpoints, it may result in the opposite effect. Finding the right balance mandates some time and resource commitment, but the benefits are truly worth the exercise.

In this 2-part blog, we will explore just how significant image breakpoints are to businesses, and some important device-related factors to consider in image breakpoint decisions — from screen resolution market share and user base, to device characteristics to pixel density — to deliver the optimally-sized web image every time.

The Byte Loss Breakdown

Getting image breakpoints right is especially important at larger image widths. To illustrate, let's look at this example in which a web page is displaying two images that need to be resized by the browser 50 pixels in both height and width:


As you can see, delivering a 200 X 200 pixel (px) image for a 150 X 150 px use case (50 extra pixels in width and height) results in 70,000 wasted bytes required in memory for the browser to display the image than if the image were delivered at 150 X 150 px.

When looking at the larger image example, delivering a 600 X 600 px image instead of a 550 X 550 px use case (still 50 extra pixels in height and width) resulted in 230,000 wasted bytes, or 3.3-times more wasted bytes. This illustrates why it's recommended to have more breakpoints at larger image sizes (in this case, images larger than 700 px in width).

Another example to consider involves displaying images on a typical smartphone, in this case, the Samsung Galaxy S5. This model has a screen resolution of 1080 X 1920 px. Take, for example, a server sending an image of 1200 X 2000 px dimensions that must be resized to 1080 X 1920 px. This would result in more than 1,305,600 bytes — that's 1.3 MB — waste, not to mention a significant degradation in user experience.

Read Image Optimization Best Practices for Device Breakpoints - Part 2, covering 4 tips for getting image breakpoints right.

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

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 ...