Cisco says UK observability costs outstrip IT budgets

Cisco has published UK research showing that 65% of IT professionals say their organisation's observability costs exceeded its infrastructure or operational costs this year. The findings are based on a survey of 300 UK IT operations and engineering professionals.

The data points to growing pressure on technology teams as companies collect and analyse large volumes of telemetry data to monitor applications, networks and systems. Half of UK respondents said their organisation had faced unexpected costs linked to telemetry data, while 55% said significantly higher-than-expected bills put more strain on operations than a high baseline cost for ingesting and storing data.

Observability tools help IT teams track the health and performance of digital systems and investigate incidents. Cisco's figures suggest the cost of gathering and retaining that data has become a more prominent issue for organisations, particularly as they add more complex software and AI-based workloads.

In the UK sample, 38.3% said their organisation had reduced data coverage or retention because of rising telemetry costs. That suggests some businesses are limiting how much system data they keep or monitor to manage spending, even as those teams rely on visibility to diagnose faults and maintain service performance.

AI pressure

The report also links observability spending to wider adoption of AI agents. In the UK, 32% of respondents said they use AI agents all the time to diagnose root cause, compared with 20% globally. At the same time, 55% of UK respondents said they strongly worry about the unintended consequences of AI agents.

The global results, based on 2,050 respondents across 10 countries, show similar concerns over cost control. Globally, 55% of respondents said they cannot control AI costs, while 60% said unexpected billing spikes create more operational strain than high baseline costs. Another 57% said telemetry costs had overtaken the cost of the infrastructure being monitored.

Those figures suggest the challenge is not only the size of technology bills, but also their unpredictability. For IT operations teams, sudden increases in data or AI-related charges can be harder to absorb than consistently high costs because they complicate budgeting and incident response planning.

As organisations deploy AI agents in software development, operations and customer-facing processes, technology teams are being asked to monitor systems that can behave less predictably than conventional applications. That has increased demand for new forms of monitoring focused on AI model behaviour, response quality and operational impact.

Cisco's global findings indicate that many companies still lack clear ways to measure those systems. While 87% of respondents said monitoring AI is a priority, 54% said they do not have clear business outcomes tied to it. More than half, 56%, said they are unable to measure AI workload metrics such as large language model response quality.

Security links

The study also points to a closer relationship between observability and security teams as AI systems gain broader access across corporate technology environments. Globally, 81% said monitoring AI workloads had increased collaboration between observability and security teams, while 84% said data sharing between those groups had risen.

That overlap matters because faults in AI systems may not be limited to performance issues. They can also involve access controls, data handling and security risks, meaning troubleshooting increasingly requires input from several technical functions.

Jeetu Patel, President and Chief Product Officer at Cisco, said: "Most companies will soon have far more agents at work than people. Managing agents at scale requires reimagining both security and observability. Have our agents been compromised? Are they doing their job well? In the world of agentic AI, these questions are inseparable, and they're moving at machine speed. This report shows that observability and security data are converging, and that teams need to collaborate more than ever. Common data platforms that help us detect and respond to issues in real-time are the key to deploying agents we can trust at scale."

The report identifies a group of organisations it describes as leaders in observability practices. According to the research, these respondents were more likely to attribute AI costs accurately and to say returns from observability spending exceeded expectations. Among those organisations, 54% reported at least USD $5 million in annual business revenue linked to observability, compared with 28% of other respondents.

Kamal Hathi, Senior Vice President and General Manager, Splunk at Cisco, said: "For today's enterprises, the excitement of AI is clashing with the reality of the bill. We believe observability is the essential bridge to restoring predictability and confidence to the agentic AI era. By providing the necessary guardrails and incorporating observability into an AI tokenomics framework, leaders can transform AI from an unpredictable expense into a measurable and strategic long-term business investment."