
LogicMonitor directly addressed a longtime industry-wide pain point related to the high total cost of ownership for logs with the announcement of unlimited log data retention, now available for LM Logs, the company’s SaaS-based log management solution.
With unlimited and hot log data retention in LM Logs, operations teams have instant access to all their log data with real-time anomaly detection, along with an industry-leading low total cost of ownership, saving both time and money. These capabilities go far beyond offerings from traditional vendors whose log storage options result in expensive fees, complex operations, and limited storage capabilities. With LM Logs, now logs data can be available whenever customers need it through hot storage, with no need to wait for log rehydration, leading to faster troubleshooting and RCA (root cause analysis). Having log data easily accessible in the same place as metrics and traces makes achieving unified observability and gaining visibility into and predictability across the technologies that modern organizations depend on to deliver extraordinary employee and customer experiences easier than ever before.
“We listened to customers who have had to choose between addressing complex compliance and forensics challenges that require longer log retention and managing costs, and are pleased to offer a groundbreaking new logs cost structure,” said Tej Redkar, Chief Product Officer at LogicMonitor. “LM Logs is a key part of our unified observability platform, and with unlimited log retention, we’ve made it possible for customers to have the best of both worlds: unlimited, fast access to log data, better contextualization between infrastructure monitoring and logs, and lower more predictable costs to better manage IT budgets. Customers can drive down costs, and address compliance concerns, while freeing up to 40% of non-value adding engineering time with log anomalies that automatically surface with LogicMonitor’s machine learning technology.”
LM Logs provides tailored data retention options, ranging from 30 days to one year to unlimited, for companies of all sizes across all industries. With this announcement LogicMonitor has addressed a key pain point – budget predictability, with competitive pricing models designed to take the mystery out of the unknown, and often hidden, charges associated with cloud access and rehydration.
Features include:
- Unlimited log retention makes more data available for troubleshooting, Business Intelligence, forensics, and regulatory needs.
- 30 day, one year and unlimited data retention options, allowing flexibility to choose which storage option works best for unique business and compliance needs.
- Unified logs and metrics give customers full visibility into their entire IT ecosystem with context and correlation, resulting in dramatically reduced MTTR and simplified IT workflows.
- Patented algorithms intelligently analyze millions of log events and automatically highlight anomalous logs to help ITOps, CloudOps, and DevOps teams save time and uncover issues before they result in business impact.
“Fully-featured solutions that address log storage and analysis are the required foundation of customer observability and operation management hygiene, making it possible for ITOps and DevOps teams to successfully monitor complex, multi-cloud, and hybrid enterprise infrastructures at a global scale,” said Roy Illsley, Chief Analyst, IT Ecosystem & Operations, Omdia. “With audit compliance requirements increasing along with added forensic pressures from cyber incidents, IT and audit teams need to look beyond the most recent log activity to analyze longer-term log data. Until now, customers have faced slow data access from cold storage and an array of unpredictable and sometimes hidden cloud-related costs. Modern, best-in-class solutions must address these factors.”
LM Log with unlimited log retention is immediately available.
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 ...