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LogDNA Unveils Spike Protection

LogDNA unveiled Spike Protection to give companies more control over fluctuations in their data and spend.

With these new capabilities, development and operations teams have the freedom to rapidly deploy applications with guardrails in place to know if their ingestion spikes as a result.

In cloud-native and microservices environments, developers have an increasingly difficult time managing the spikes of log data, which often leads to surprise overage costs. Legacy vendors present storage as the solution, but this requires a substantial investment and often increases complexity, creating additional cycles for debugging. LogDNA Spike Protection gives DevOps teams the necessary tools to understand and manage increases through Index Rate Alerting and Usage Quotas. This provides additional insight into anomalous data spikes, making it faster to pinpoint the root cause so that admins can choose to store or exclude contributing logs.

“LogDNA Spike Protection gives developers greater control over the flow of log data to ensure that teams get the insights they need, while also giving them the ability to better control spend,” said Tucker Callaway, CEO, LogDNA. “Budget owners gain peace of mind knowing they are in control of their costs and developers maintain access to the data they need to accelerate release velocity and improve application reliability.”

The Spike Protection bundle includes:

- Index Rate Alerting—The latest in LogDNA’s set of tools to enable engineers with controls, Index Rate Alerting notifies users when log data exceeds a certain threshold by setting maximum threshold alerts or alerts based on deviations from historical data. LogDNA monitors index rates from the past 30 days to understand what is ‘normal’ for an organization, and will trigger an alert when spikes occur. Index Rate Alerting also provides insights into which sources have seen anomalous indexing increases—such as new software releases or unexpected increases in application usage—making it easier to pinpoint the root cause of data spikes. LogDNA’s usage dashboard page also provides access to this and all data associated with all the apps and sources in the organization.

- Usage Quotas—Launched in March 2021, Usage Quotas allows developers to set daily or monthly limits on the volume of logs stored and gives them more granular control over their data. A hard quota lets teams set specific thresholds to stop retaining logs and a soft quota lets them throttle the amount of logs being retained as they approach the hard threshold, and even allows users to go over if the data is considered mission critical.

The LogDNA platform also delivers robust capabilities to help developers manage the increasing complexity in their cloud-native and microservices environments. In addition to Spike Protection, LogDNA announced the release of its Agent 3.2 for Kubernetes and OpenShift, which introduces the configuration of log inclusion/exclusion rules, along with log redaction, using regex patterns. These enhancements give developers more control over what data leaves their system, and what data is ingested by LogDNA. Powerful Exclusion Rules let developers manage log volume by storing what’s important and excluding what’s not. Automatic Archiving lets LogDNA users forward logs to an AWS S3 bucket or any other object storage for compliance or later review. Role-Based Access Control lets teams limit access to sensitive logs and potentially destructive actions.

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LogDNA Unveils Spike Protection

LogDNA unveiled Spike Protection to give companies more control over fluctuations in their data and spend.

With these new capabilities, development and operations teams have the freedom to rapidly deploy applications with guardrails in place to know if their ingestion spikes as a result.

In cloud-native and microservices environments, developers have an increasingly difficult time managing the spikes of log data, which often leads to surprise overage costs. Legacy vendors present storage as the solution, but this requires a substantial investment and often increases complexity, creating additional cycles for debugging. LogDNA Spike Protection gives DevOps teams the necessary tools to understand and manage increases through Index Rate Alerting and Usage Quotas. This provides additional insight into anomalous data spikes, making it faster to pinpoint the root cause so that admins can choose to store or exclude contributing logs.

“LogDNA Spike Protection gives developers greater control over the flow of log data to ensure that teams get the insights they need, while also giving them the ability to better control spend,” said Tucker Callaway, CEO, LogDNA. “Budget owners gain peace of mind knowing they are in control of their costs and developers maintain access to the data they need to accelerate release velocity and improve application reliability.”

The Spike Protection bundle includes:

- Index Rate Alerting—The latest in LogDNA’s set of tools to enable engineers with controls, Index Rate Alerting notifies users when log data exceeds a certain threshold by setting maximum threshold alerts or alerts based on deviations from historical data. LogDNA monitors index rates from the past 30 days to understand what is ‘normal’ for an organization, and will trigger an alert when spikes occur. Index Rate Alerting also provides insights into which sources have seen anomalous indexing increases—such as new software releases or unexpected increases in application usage—making it easier to pinpoint the root cause of data spikes. LogDNA’s usage dashboard page also provides access to this and all data associated with all the apps and sources in the organization.

- Usage Quotas—Launched in March 2021, Usage Quotas allows developers to set daily or monthly limits on the volume of logs stored and gives them more granular control over their data. A hard quota lets teams set specific thresholds to stop retaining logs and a soft quota lets them throttle the amount of logs being retained as they approach the hard threshold, and even allows users to go over if the data is considered mission critical.

The LogDNA platform also delivers robust capabilities to help developers manage the increasing complexity in their cloud-native and microservices environments. In addition to Spike Protection, LogDNA announced the release of its Agent 3.2 for Kubernetes and OpenShift, which introduces the configuration of log inclusion/exclusion rules, along with log redaction, using regex patterns. These enhancements give developers more control over what data leaves their system, and what data is ingested by LogDNA. Powerful Exclusion Rules let developers manage log volume by storing what’s important and excluding what’s not. Automatic Archiving lets LogDNA users forward logs to an AWS S3 bucket or any other object storage for compliance or later review. Role-Based Access Control lets teams limit access to sensitive logs and potentially destructive actions.

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 ...