Logentries announced a new integration with Geckoboard that brings valuable log data visualizations and analytics to the centralized Geckoboard business dashboard.
The Logentries service can also now export real-time log data visualizations and metrics dashboards to other third-party operations dashboards, and teams can access their critical log data from anywhere, at any time.
Logentries Shareable Dashboards enable users to build a real-time metrics dashboard within Logentries and easily export via JSON format to an external tool such as Geckoboard. Geckoboard provides a centralized location for monitoring all business metrics, pulling from various sources, including Logentries. With the new integration, Development, Operations, and Business Analytics teams can look at KPIs generated from log data alongside other important metrics from across their business, including website performance, product usage and sales and marketing transactions, which can also be captured using log data.
“Our customers are using log data to monitor all aspects of their businesses, from Development, to Operations, to product usage metrics, and they need to easily share critical business dashboards across their teams, and across their organizations,” said Trevor Parsons, Co-founder and Chief Scientist, Logentries. “We are excited to partner with Geckoboard to offer an out-of-the-box approach to adding valuable log data-generated metrics to users’ existing dashboard views.”
Logentries enables users to conduct a query using regular expression or simple search terms on individual or groups of logs to identify key business metrics and trends on any information captured in your logs (such as average sale value, server load, webpage response time etc.). This information is now available as an http endpoint from Logentries that can be instantly consumed by an external tool such as Geckoboard via JSON message.
“At Geckoboard we believe everybody should have access to the data they need when they need it,” said Rob Hudson, CTO at Geckoboard. “Partners like Logentries offer deep insight into data that integrate with our API to help customers achieve that faster.”
The Logentries service features a unique pre-processing engine that collects and analyzes log files in real-time to offer immediate alerting, visualizations, and tailing of the data. There is no complex query language required, making searching the data easy and intuitive with click-through navigation – Logentries points out the important data points so you don’t have to go looking for them.
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...