Skip to main content

LogDNA Introduces Variable Retention

LogDNA announced early access for Variable Retention.

This new capability allows companies to better control spend by storing different types or sources of logs for different lengths of time.

Modern infrastructure and applications generate massive amounts of data—sometimes petabytes worth of log data in a single day. Many teams need access to this data to gain critical insights into their services, but logging can get expensive, fast, and companies are forced to make difficult trade-offs that reduce observability and heighten risk. As a result, teams may not have the granular log data needed to form a complete picture during an incident or troubleshooting workflow. Variable Retention gives users the flexibility to save logs within the platform only for the amount of time that they're relevant, ensuring teams get access to the data they need, when they need it, while keeping costs in check.

“Different organizational and business functions need varying amounts and periods of data, but cost concerns drive sacrifices on what to keep and force dollar-driven decisions that diminish the value of all that data,” said Tucker Callaway, CEO, LogDNA. “Variable Retention gives LogDNA users control, removing friction that impacts how autonomous teams use log and other machine data to be more efficient and secure. Now, teams don’t have to choose between a reasonable logging bill and comprehensive observability data.”

Within the LogDNA user interface, customers select a subset of logs to store for different retention periods based on their needs—for instance, 30 days for security logs but only seven days for quality assurance and testing logs. When these rules are in place, users can monitor how their logging volume is distributed across different tiers in their usage dashboard to ensure the appropriate logs are being put in their respective retention tiers.

This new capability is one of many built by LogDNA to give users more control over how teams can route and store their log data. Earlier this year, the company released Spike Protection to give organizations more control over fluctuations of log data. And the recently announced LogDNA Streaming enables enterprises to ingest all of their log data to a single platform, and then route it for any enterprise use case.

Available retention tiers are three days, seven days, 14 days, and 30 days. LogDNA customers on an enterprise plan can sign up for the private beta program.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

LogDNA Introduces Variable Retention

LogDNA announced early access for Variable Retention.

This new capability allows companies to better control spend by storing different types or sources of logs for different lengths of time.

Modern infrastructure and applications generate massive amounts of data—sometimes petabytes worth of log data in a single day. Many teams need access to this data to gain critical insights into their services, but logging can get expensive, fast, and companies are forced to make difficult trade-offs that reduce observability and heighten risk. As a result, teams may not have the granular log data needed to form a complete picture during an incident or troubleshooting workflow. Variable Retention gives users the flexibility to save logs within the platform only for the amount of time that they're relevant, ensuring teams get access to the data they need, when they need it, while keeping costs in check.

“Different organizational and business functions need varying amounts and periods of data, but cost concerns drive sacrifices on what to keep and force dollar-driven decisions that diminish the value of all that data,” said Tucker Callaway, CEO, LogDNA. “Variable Retention gives LogDNA users control, removing friction that impacts how autonomous teams use log and other machine data to be more efficient and secure. Now, teams don’t have to choose between a reasonable logging bill and comprehensive observability data.”

Within the LogDNA user interface, customers select a subset of logs to store for different retention periods based on their needs—for instance, 30 days for security logs but only seven days for quality assurance and testing logs. When these rules are in place, users can monitor how their logging volume is distributed across different tiers in their usage dashboard to ensure the appropriate logs are being put in their respective retention tiers.

This new capability is one of many built by LogDNA to give users more control over how teams can route and store their log data. Earlier this year, the company released Spike Protection to give organizations more control over fluctuations of log data. And the recently announced LogDNA Streaming enables enterprises to ingest all of their log data to a single platform, and then route it for any enterprise use case.

Available retention tiers are three days, seven days, 14 days, and 30 days. LogDNA customers on an enterprise plan can sign up for the private beta program.

The Latest

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 ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...