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Maximizing Resilience: Insights from the 2025 SRE Report

Leo Vasiliou
Catchpoint

As the digital landscape expands, the stakes for delivering reliable and seamless online experiences have never been higher. In the past year, site reliability engineering (SRE) has continued to evolve into a critical driver of operational success, shaping how organizations approach resilience, collaboration, and customer satisfaction.

The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them.

Slow Is the New Down

Performance is about more than just uptime; it's also about speed. This year's report reveals that 53% of organizations believe poor performance is as harmful as downtime, making user experience a critical reliability metric.

What This Means for You: Organizations must elevate their performance monitoring strategies to include experience level objectives (XLOs) for ensuring fast and seamless digital interactions. Proactive performance tuning and real-time observability can mitigate the impact of "slow" on end users.

Toil Levels Are Rising Despite AI

After years of decline, toil — the manual, repetitive tasks that consume engineering resources — has ticked upward. The median reported percentage of work spent on toil rose to 30% from 25% in 2024 causing us to hypothesize whether AI is filling our time with more — instead of less — operational workload.

Why It Matters: This hypothesis suggests that while AI is improving specific workflows, it hasn't eliminated the burden of toil. Teams should evaluate their AI implementations to ensure they target high-impact areas and actively reduce manual effort. As Laura de Vesine, one of this year's report contributors put it: AI is at best "a co-worker you can't trust." Even as AI tools become more integrated into workflows, human oversight and intervention remain critical to ensure these tools don't inadvertently add to the complexity of tasks.

Organizational Priorities Under Pressure

The tension between agility and stability persists. Over two-thirds of respondents reported feeling pressured to prioritize release schedules over reliability, highlighting the ongoing challenge of balancing speed with resilience.

Takeaway: Building a culture that values reliability alongside agility requires clear communication and alignment on priorities. Teams should integrate reliability metrics into performance evaluations and emphasize the long-term benefits of stable releases for both IT and the business.

Monitoring Tools: More Is More

The report found that most organizations use between 2-10 monitoring or observability tools, showing a "value over cost" mindset for effective oversight across complex technology stacks.

What This Means for You: While multiple tools can provide comprehensive coverage, they also introduce complexity. Organizations should focus on integrating these tools to provide unified visibility and actionable insights without overwhelming their teams.

AI Training Universally in High Demand, but Time-Constrained

As AI continues to shape the SRE landscape, 30% of respondents prioritized technical training on AI — a strong indicator of the desire to upskill. However, the top sentiment (37%) reflected caution, as teams balance enthusiasm for AI with practical implementation concerns.

Takeaway: Providing targeted, hands-on training programs can help bridge the knowledge gap and build confidence in AI's capabilities. Organizations should also set realistic expectations for AI adoption, ensuring a smooth transition into daily workflows.

Incidents Are a Certainty

Incident response remains a universal challenge, with 40% of respondents handling between 1 and 5 incidents in the last 30 days. Notably, incident management is a shared responsibility, with higher-level managers as involved as individual contributors.

Why This Matters: Teams should adopt a collaborative approach to incident response, leveraging diverse perspectives to address issues effectively. Implementing clear incident playbooks and blameless post-mortem practices can further enhance preparedness and learning.

Misalignment on Reliability Priorities

While the overall responses paint a positive picture of reliability practices, significant gaps emerge when analyzed by managerial responsibility. Misalignment on priorities and approaches remains a challenge.

Takeaway: Bridging this IT-to-business gap requires the acknowledgment of its existence. Ongoing dialogue, alignment across all levels of the organization, and regularly revisiting and communicating reliability goals can help ensure everyone is pulling in the same direction.

Ownership and Action in SRE

The report shows just how important it is to connect technical work with the bigger picture. It all comes down to teams knowing how their efforts make a real difference and taking thoughtful steps to grab the opportunities in front of them. This year's report sheds light on the ongoing challenges that need attention, like making reliability a part of release planning, giving teams the tools and training they need to tackle incidents smoothly, and getting everyone on the same page, from leadership to contributors.

When it comes to AI, the focus should be on using it in practical ways that actually make work easier rather than more complicated. Building resilience and reliability isn't just about technical know-how. It's about clear goals, teamwork, and always looking for ways to improve. Companies that see SRE as a way to drive real outcomes, rather than just a set of technical tasks, will be in a great spot to succeed as the digital world keeps getting more complex and fast-paced.

Leo Vasiliou is Director of Product Marketing at Catchpoint

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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Maximizing Resilience: Insights from the 2025 SRE Report

Leo Vasiliou
Catchpoint

As the digital landscape expands, the stakes for delivering reliable and seamless online experiences have never been higher. In the past year, site reliability engineering (SRE) has continued to evolve into a critical driver of operational success, shaping how organizations approach resilience, collaboration, and customer satisfaction.

The 2025 Catchpoint SRE Report dives into the forces transforming the SRE landscape, exploring both the challenges and opportunities ahead. Let's break down the key findings and what they mean for SRE professionals and the businesses relying on them.

Slow Is the New Down

Performance is about more than just uptime; it's also about speed. This year's report reveals that 53% of organizations believe poor performance is as harmful as downtime, making user experience a critical reliability metric.

What This Means for You: Organizations must elevate their performance monitoring strategies to include experience level objectives (XLOs) for ensuring fast and seamless digital interactions. Proactive performance tuning and real-time observability can mitigate the impact of "slow" on end users.

Toil Levels Are Rising Despite AI

After years of decline, toil — the manual, repetitive tasks that consume engineering resources — has ticked upward. The median reported percentage of work spent on toil rose to 30% from 25% in 2024 causing us to hypothesize whether AI is filling our time with more — instead of less — operational workload.

Why It Matters: This hypothesis suggests that while AI is improving specific workflows, it hasn't eliminated the burden of toil. Teams should evaluate their AI implementations to ensure they target high-impact areas and actively reduce manual effort. As Laura de Vesine, one of this year's report contributors put it: AI is at best "a co-worker you can't trust." Even as AI tools become more integrated into workflows, human oversight and intervention remain critical to ensure these tools don't inadvertently add to the complexity of tasks.

Organizational Priorities Under Pressure

The tension between agility and stability persists. Over two-thirds of respondents reported feeling pressured to prioritize release schedules over reliability, highlighting the ongoing challenge of balancing speed with resilience.

Takeaway: Building a culture that values reliability alongside agility requires clear communication and alignment on priorities. Teams should integrate reliability metrics into performance evaluations and emphasize the long-term benefits of stable releases for both IT and the business.

Monitoring Tools: More Is More

The report found that most organizations use between 2-10 monitoring or observability tools, showing a "value over cost" mindset for effective oversight across complex technology stacks.

What This Means for You: While multiple tools can provide comprehensive coverage, they also introduce complexity. Organizations should focus on integrating these tools to provide unified visibility and actionable insights without overwhelming their teams.

AI Training Universally in High Demand, but Time-Constrained

As AI continues to shape the SRE landscape, 30% of respondents prioritized technical training on AI — a strong indicator of the desire to upskill. However, the top sentiment (37%) reflected caution, as teams balance enthusiasm for AI with practical implementation concerns.

Takeaway: Providing targeted, hands-on training programs can help bridge the knowledge gap and build confidence in AI's capabilities. Organizations should also set realistic expectations for AI adoption, ensuring a smooth transition into daily workflows.

Incidents Are a Certainty

Incident response remains a universal challenge, with 40% of respondents handling between 1 and 5 incidents in the last 30 days. Notably, incident management is a shared responsibility, with higher-level managers as involved as individual contributors.

Why This Matters: Teams should adopt a collaborative approach to incident response, leveraging diverse perspectives to address issues effectively. Implementing clear incident playbooks and blameless post-mortem practices can further enhance preparedness and learning.

Misalignment on Reliability Priorities

While the overall responses paint a positive picture of reliability practices, significant gaps emerge when analyzed by managerial responsibility. Misalignment on priorities and approaches remains a challenge.

Takeaway: Bridging this IT-to-business gap requires the acknowledgment of its existence. Ongoing dialogue, alignment across all levels of the organization, and regularly revisiting and communicating reliability goals can help ensure everyone is pulling in the same direction.

Ownership and Action in SRE

The report shows just how important it is to connect technical work with the bigger picture. It all comes down to teams knowing how their efforts make a real difference and taking thoughtful steps to grab the opportunities in front of them. This year's report sheds light on the ongoing challenges that need attention, like making reliability a part of release planning, giving teams the tools and training they need to tackle incidents smoothly, and getting everyone on the same page, from leadership to contributors.

When it comes to AI, the focus should be on using it in practical ways that actually make work easier rather than more complicated. Building resilience and reliability isn't just about technical know-how. It's about clear goals, teamwork, and always looking for ways to improve. Companies that see SRE as a way to drive real outcomes, rather than just a set of technical tasks, will be in a great spot to succeed as the digital world keeps getting more complex and fast-paced.

Leo Vasiliou is Director of Product Marketing at Catchpoint

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...