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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

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...