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LogDNA Introduces Logging Usage Quotas

LogDNA announced Usage Quotas—a new feature that allows users to set daily or monthly limits on the volume of logs stored and gives them more granular control over their data.

These enhancements are part of an increased effort to provide LogDNA users with the tools they need to control costs while ensuring that they have access to the data that’s important to them.

“As the adoption of microservices and containers has grown over the past decade, so has the volume of logs that people produce and consume,” said Tucker Callaway, CEO of LogDNA. “Development teams want to see all of their logs but budget owners need a way to predict and control the amount that they’re spending. Usage Quotas help admins better manage their cost while still giving users the tools they need to succeed.”

The new feature allows users to set a daily or monthly hard limit on the volume of logs stored to provide complete control over spend. For an added level of flexibility, admins can also set soft daily or monthly quotas. This creates throttling logic that will ensure mission-critical log data will continue to flow through LogDNA as usage volume approaches the limit threshold, and can even be configured so it continues even after that limit is reached. This allows teams to aim for a daily or monthly quota, and maintain flexibility to go over that limit while controlling overage spending.

No matter how you configure your usage limits, LogDNA will notify team members anytime a quota is hit, or when an exclusion rule is triggered due to reaching a daily or monthly quota. These alerts can be configured within LogDNA and include integrations to email and Slack. Logs that are not retained are not counted toward the user’s bill at the end of the month but are displayed in Live Tail and will trigger relevant Alerts.

LogDNA Usage Quotas are now available for LogDNA Enterprise customers.

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LogDNA Introduces Logging Usage Quotas

LogDNA announced Usage Quotas—a new feature that allows users to set daily or monthly limits on the volume of logs stored and gives them more granular control over their data.

These enhancements are part of an increased effort to provide LogDNA users with the tools they need to control costs while ensuring that they have access to the data that’s important to them.

“As the adoption of microservices and containers has grown over the past decade, so has the volume of logs that people produce and consume,” said Tucker Callaway, CEO of LogDNA. “Development teams want to see all of their logs but budget owners need a way to predict and control the amount that they’re spending. Usage Quotas help admins better manage their cost while still giving users the tools they need to succeed.”

The new feature allows users to set a daily or monthly hard limit on the volume of logs stored to provide complete control over spend. For an added level of flexibility, admins can also set soft daily or monthly quotas. This creates throttling logic that will ensure mission-critical log data will continue to flow through LogDNA as usage volume approaches the limit threshold, and can even be configured so it continues even after that limit is reached. This allows teams to aim for a daily or monthly quota, and maintain flexibility to go over that limit while controlling overage spending.

No matter how you configure your usage limits, LogDNA will notify team members anytime a quota is hit, or when an exclusion rule is triggered due to reaching a daily or monthly quota. These alerts can be configured within LogDNA and include integrations to email and Slack. Logs that are not retained are not counted toward the user’s bill at the end of the month but are displayed in Live Tail and will trigger relevant Alerts.

LogDNA Usage Quotas are now available for LogDNA Enterprise customers.

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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 ...

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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 ...

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