Sumo Logic announced comprehensive analytics capabilities for organizations embracing DevOps practices, microservices architectures and containers to build applications.
As application architectures evolve toward microservices, containers continue to gain traction for providing the ideal environment to build, deploy and operate these applications across distributed systems. The volume and complexity of data generated by these environments make monitoring and troubleshooting an enormous challenge for development and operations teams. The Sumo Logic Collector and Application for Docker allows DevOps teams to easily collect any data from the Docker infrastructure and the applications running within the container to quickly identify and resolve critical issues.
“While building applications has become easier over the years due to the rise in adoption of IaaS and PaaS, the increasingly common nature of large-scale distributed applications and the abstraction introduced by containerization is making runtime monitoring and getting visibility into these architectures very complex,” said Christian Beedgen, Co-Founder and CTO at Sumo Logic. “The Sumo Logic Collector for Docker addresses these challenges and easily collects logs and statistics from Docker containers, as well as ingests event and configuration information to help with Docker container lifecycle management.”
Sumo Logic provides full-stack visibility for microservices-based applications so DevOps teams can monitor complex service interactions and identify issues within each component of an application. The ability to scale on-demand, ingest any data source and apply machine-learning algorithms make Sumo Logic the ideal logging and monitoring solution for microservices-based applications.
Available today, the Sumo Logic Application for Docker provides:
- A native collection source for the entire Docker infrastructure
- Real-time monitoring of Docker infrastructure including stats, events and container logs
- Ability to troubleshoot issues and set alerts on abnormal container or application behavior
- Visualizations of key metrics and KPIs, including image usage, container actions and faults, as well as CPU/Memory/Network statistics
- Ability to easily create custom and aggregate KPIs and metrics using Sumo Logic’s powerful query language
- Advanced analytics powered by LogReduce, Anomaly Detection, Transaction Analytics, and Outlier Detection
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
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.