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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

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...