Skip to main content

4 Strategies to Slash Observability Costs

Dotan Horovits
Logz.io

In a world where software systems rule the digital landscape, there's a lurking terror that goes bump in the code. It's called "observability," and you may not be prepared to pay its price.

Observability is essential for maintaining the performance and reliability of digital creations. Like a boat in shark-infested waters, it's a lifeline of modern software. But beware, for the cost of attaining the power of observability can quickly spiral out of control, like a monster lurking in the depths, waiting to strike when you least expect it.


In the darkest corners of the tech world, we hear the chilling cries of organizations, tormented by the relentless rise of the cost of observability. Every dollar spent on technology is scrutinized and dissected. Meanwhile, nefarious vendors take advantage of the desperate need for observability, charging terrifying fees to transport data to their unholy platforms, data that holds virtually no value.

But fear not, for there is a different path, a path to cost-effective observability. Join me as we venture into this cryptic world with practical tips to help you vanquish observability costs, without compromising your monitoring and troubleshooting prowess.

Tip #1: Optimize Your Data

In this haunted realm, one of the most pervasive villains is excessive and irrelevant data. Many organizations unwittingly ship massive volumes of metrics and logs, most of which are mere phantoms, holding no value. To banish this data demon, you must identify and capture only the meaningful data that affects your business. By filtering out the unnecessary, you can significantly reduce the cost of storage and processing, focusing your energies on what truly matters.

Thanks to the "mysteries" of machine learning, we can now unlock the secrets of multi-layer data optimization and distinguish between living and undead data. With the right tools, we can now visualize the metrics and logs that are truly alive, and ignore those that wander the land of the dead. Armed with this knowledge, you can make informed decisions, leading to substantial cost savings.

Tip #2: Manage Data Retention

Not all data deserves to ascend to the observability plane. Some data must be preserved, while others can be released into the ether. By managing data retention wisely, you can reduce storage costs without sacrificing your ability to troubleshoot and comply with the dark arts of regulations.

The key is to segregate your data based on specific use cases and retention requirements. Each use case should have its own set of retention policies, ensuring that the critical data lingers for the required duration while less important data meets its demise sooner. In this way, you can flexibly optimize costs, aligning data retention with its value and importance to your organization.

Tip #3: The Alchemy of Logs to Metrics

Sending logs is like sending a message to the beyond, but the true value lies in the insights that rise from the darkness. Many organizations find themselves drowning in the deluge of logs, trapped in a nightmarish maelstrom of data overload. However, by converting logs into meaningful metrics, you can refine data analysis, visualization, and alerting, all while reducing costs. No longer will you be haunted by the specter of high storage costs, for you can define parameters to generate metrics that unveil system performance, success rates, failure rates, and more.

Tip #4: Leverage Sub-Accounts for Cost Control

In the sprawling mansion of observability, managing costs can be as daunting as a haunted maze. One of the best ways to conquer the labyrinth is to provide cost accountability and autonomy to different teams within your organization.

By allocating specific budgets to sub-accounts, you can impose cost limits on each team while allowing them to manage their observability needs. This approach ensures teams are responsible for their spending and, if done correctly, casts a protective spell to ensure teams only see the data they need for their tasks, reducing compliance risks. Sub-accounts bring balance between autonomy and cost control, like a ghostly guide through the labyrinth of resource utilization and budget management.

End the Nightmare: Cost-Efficient Observability Can Be Your Reality

Observability, though essential, need not be a horror story of costs spiraling out of control. By heeding these practical tips, you can wrestle control from the observability cost beast, all while maintaining your monitoring and troubleshooting prowess.

Focus on meaningful data by utilizing data optimization techniques; convert logs into metrics; master the dark arts of storage and data retention policies; and wield sub-accounts for cost control — These are the keys to achieving cost-efficient observability, without compromising your critical monitoring processes.

So be brave enough to face the shadows and seek the observability you desire at a price that won't cost you an arm and a leg. When you do, the horror of observability costs shall haunt you no more!

Dotan Horovits is Principal Developer Advocate at Logz.io

Hot Topics

The Latest

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 ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

4 Strategies to Slash Observability Costs

Dotan Horovits
Logz.io

In a world where software systems rule the digital landscape, there's a lurking terror that goes bump in the code. It's called "observability," and you may not be prepared to pay its price.

Observability is essential for maintaining the performance and reliability of digital creations. Like a boat in shark-infested waters, it's a lifeline of modern software. But beware, for the cost of attaining the power of observability can quickly spiral out of control, like a monster lurking in the depths, waiting to strike when you least expect it.


In the darkest corners of the tech world, we hear the chilling cries of organizations, tormented by the relentless rise of the cost of observability. Every dollar spent on technology is scrutinized and dissected. Meanwhile, nefarious vendors take advantage of the desperate need for observability, charging terrifying fees to transport data to their unholy platforms, data that holds virtually no value.

But fear not, for there is a different path, a path to cost-effective observability. Join me as we venture into this cryptic world with practical tips to help you vanquish observability costs, without compromising your monitoring and troubleshooting prowess.

Tip #1: Optimize Your Data

In this haunted realm, one of the most pervasive villains is excessive and irrelevant data. Many organizations unwittingly ship massive volumes of metrics and logs, most of which are mere phantoms, holding no value. To banish this data demon, you must identify and capture only the meaningful data that affects your business. By filtering out the unnecessary, you can significantly reduce the cost of storage and processing, focusing your energies on what truly matters.

Thanks to the "mysteries" of machine learning, we can now unlock the secrets of multi-layer data optimization and distinguish between living and undead data. With the right tools, we can now visualize the metrics and logs that are truly alive, and ignore those that wander the land of the dead. Armed with this knowledge, you can make informed decisions, leading to substantial cost savings.

Tip #2: Manage Data Retention

Not all data deserves to ascend to the observability plane. Some data must be preserved, while others can be released into the ether. By managing data retention wisely, you can reduce storage costs without sacrificing your ability to troubleshoot and comply with the dark arts of regulations.

The key is to segregate your data based on specific use cases and retention requirements. Each use case should have its own set of retention policies, ensuring that the critical data lingers for the required duration while less important data meets its demise sooner. In this way, you can flexibly optimize costs, aligning data retention with its value and importance to your organization.

Tip #3: The Alchemy of Logs to Metrics

Sending logs is like sending a message to the beyond, but the true value lies in the insights that rise from the darkness. Many organizations find themselves drowning in the deluge of logs, trapped in a nightmarish maelstrom of data overload. However, by converting logs into meaningful metrics, you can refine data analysis, visualization, and alerting, all while reducing costs. No longer will you be haunted by the specter of high storage costs, for you can define parameters to generate metrics that unveil system performance, success rates, failure rates, and more.

Tip #4: Leverage Sub-Accounts for Cost Control

In the sprawling mansion of observability, managing costs can be as daunting as a haunted maze. One of the best ways to conquer the labyrinth is to provide cost accountability and autonomy to different teams within your organization.

By allocating specific budgets to sub-accounts, you can impose cost limits on each team while allowing them to manage their observability needs. This approach ensures teams are responsible for their spending and, if done correctly, casts a protective spell to ensure teams only see the data they need for their tasks, reducing compliance risks. Sub-accounts bring balance between autonomy and cost control, like a ghostly guide through the labyrinth of resource utilization and budget management.

End the Nightmare: Cost-Efficient Observability Can Be Your Reality

Observability, though essential, need not be a horror story of costs spiraling out of control. By heeding these practical tips, you can wrestle control from the observability cost beast, all while maintaining your monitoring and troubleshooting prowess.

Focus on meaningful data by utilizing data optimization techniques; convert logs into metrics; master the dark arts of storage and data retention policies; and wield sub-accounts for cost control — These are the keys to achieving cost-efficient observability, without compromising your critical monitoring processes.

So be brave enough to face the shadows and seek the observability you desire at a price that won't cost you an arm and a leg. When you do, the horror of observability costs shall haunt you no more!

Dotan Horovits is Principal Developer Advocate at Logz.io

Hot Topics

The Latest

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 ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.