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Mastering Observability: Navigating Costs and Complexity with eBPF Innovation

Aviv Zohari
groundcover

A colleague of mine recently embarked on a journey to explore the capabilities of a well-known legacy observability platform within his Kubernetes environment. He dedicated a week to familiarize himself with the platform, primarily testing out the different features for traces, logs, and infrastructure monitoring. However, his focus shifted when a critical feature needed an early release, diverting his attention away from the observability tool. Unfortunately, without any prior notification or warning, there was no rate limitation to the platform logs collection mechanism. One line of YAML configuration file meant all logs were collected, ingested and stored — with no mention of the projected cost.

Fast forward to the following week, a member of the billing department barged into his office, demanding an explanation for an astronomical observability bill totaling $33,000 for a single month, a staggering contrast to the anticipated $1,700.

This series of events left my work buddy struggling with the size of his mistake, and me questioning whether it really was entirely his fault.

The Complex Landscape of Observability Pricing

Navigating observability pricing models can be compared to solving a perplexing puzzle which includes financial variables and contractual intricacies. Predicting all potential costs in advance becomes an elusive endeavor, exemplified by a recent eye-popping $65 million observability bill.

Avoiding miscalculations as the one that happened to my friend requires continuous monitoring of the monitoring solution. This practice slows down day-to-day operations and long-term growth efforts.

The Challenge of Affordability in Observability

The escalating costs associated with observability represent a vast challenge which is confronting many organizations currently. Particularly in the age of cloud computing, IT leaders and even top executives have come to realize the imperative of reining in their infrastructure budgets, which often spiral out of control.

The proliferation of microservices and distributed architectures has ushered in a flood of data that demands observability. Traditionally, more data translates into higher expenses, accompanied by substantial resource consumption, leading not only to increased costs but also inefficiencies.

Regrettably, most observability tools employ pricing models that defy prediction. While applications generate large amounts of log data, instead of an advantage, this abundance has become a cause for concern. In response, best practices now advocate monitoring "only what you need" or limiting the retention period for collected data to a minimum. This raises two questions: how can you know in advance what you will need, and will limiting the retention period to a minimum make it impossible to correlate with out-of-range historical data.

Enter eBPF: A Game-Changer

eBPF (extended Berkeley Packet Filter) has recently emerged as a revolutionary technology that has significantly impacted the Linux kernell. eBPF operates at specific hook points within the kernel, extracting data with minimal overhead, safeguarding the application's resources from excessive consumption. It observes every packet entering or exiting the host, mapping them to processes or containers running on the host, thereby offering granular insights into network traffic.

Moreover, eBPF-powered agents operate independently of the primary application being monitored, ensuring minimal impact on microservice resources.

The combination of visibility depth and stability has made eBPF a groundbreaking technology for cybersecurity companies, and is predicted to have the same effect on observability, for exactly the same reasons.

Hassle-Free Observability

Observability should empower engineers, not bury them in a load of unexpected overheads, data volume surges, and huge subscription bills. The goal of observability platforms should be to guarantee complete protection against such surprises, offering immunity against sudden spikes in data volume and shielding engineers from unfortunate encounters with the billing department.

In conclusion, the journey to achieving efficient and cost-effective observability is full of challenges, but with the right tools and strategies, IT and DevOps leaders can help their organizations emerge from financial uncertainty and empower their engineers to become true observability heroes.

Aviv Zohari is the Founding Engineer of groundcover

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Mastering Observability: Navigating Costs and Complexity with eBPF Innovation

Aviv Zohari
groundcover

A colleague of mine recently embarked on a journey to explore the capabilities of a well-known legacy observability platform within his Kubernetes environment. He dedicated a week to familiarize himself with the platform, primarily testing out the different features for traces, logs, and infrastructure monitoring. However, his focus shifted when a critical feature needed an early release, diverting his attention away from the observability tool. Unfortunately, without any prior notification or warning, there was no rate limitation to the platform logs collection mechanism. One line of YAML configuration file meant all logs were collected, ingested and stored — with no mention of the projected cost.

Fast forward to the following week, a member of the billing department barged into his office, demanding an explanation for an astronomical observability bill totaling $33,000 for a single month, a staggering contrast to the anticipated $1,700.

This series of events left my work buddy struggling with the size of his mistake, and me questioning whether it really was entirely his fault.

The Complex Landscape of Observability Pricing

Navigating observability pricing models can be compared to solving a perplexing puzzle which includes financial variables and contractual intricacies. Predicting all potential costs in advance becomes an elusive endeavor, exemplified by a recent eye-popping $65 million observability bill.

Avoiding miscalculations as the one that happened to my friend requires continuous monitoring of the monitoring solution. This practice slows down day-to-day operations and long-term growth efforts.

The Challenge of Affordability in Observability

The escalating costs associated with observability represent a vast challenge which is confronting many organizations currently. Particularly in the age of cloud computing, IT leaders and even top executives have come to realize the imperative of reining in their infrastructure budgets, which often spiral out of control.

The proliferation of microservices and distributed architectures has ushered in a flood of data that demands observability. Traditionally, more data translates into higher expenses, accompanied by substantial resource consumption, leading not only to increased costs but also inefficiencies.

Regrettably, most observability tools employ pricing models that defy prediction. While applications generate large amounts of log data, instead of an advantage, this abundance has become a cause for concern. In response, best practices now advocate monitoring "only what you need" or limiting the retention period for collected data to a minimum. This raises two questions: how can you know in advance what you will need, and will limiting the retention period to a minimum make it impossible to correlate with out-of-range historical data.

Enter eBPF: A Game-Changer

eBPF (extended Berkeley Packet Filter) has recently emerged as a revolutionary technology that has significantly impacted the Linux kernell. eBPF operates at specific hook points within the kernel, extracting data with minimal overhead, safeguarding the application's resources from excessive consumption. It observes every packet entering or exiting the host, mapping them to processes or containers running on the host, thereby offering granular insights into network traffic.

Moreover, eBPF-powered agents operate independently of the primary application being monitored, ensuring minimal impact on microservice resources.

The combination of visibility depth and stability has made eBPF a groundbreaking technology for cybersecurity companies, and is predicted to have the same effect on observability, for exactly the same reasons.

Hassle-Free Observability

Observability should empower engineers, not bury them in a load of unexpected overheads, data volume surges, and huge subscription bills. The goal of observability platforms should be to guarantee complete protection against such surprises, offering immunity against sudden spikes in data volume and shielding engineers from unfortunate encounters with the billing department.

In conclusion, the journey to achieving efficient and cost-effective observability is full of challenges, but with the right tools and strategies, IT and DevOps leaders can help their organizations emerge from financial uncertainty and empower their engineers to become true observability heroes.

Aviv Zohari is the Founding Engineer of groundcover

Hot Topics

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...