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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...

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

Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...