Blue Medora announced BindPlane for Microsoft’s Azure Log Analytics, allowing unified monitoring across the growing number of multi-cloud environments.
Blue Medora’s BindPlane for Azure Log Analytics, now in early access preview through the Azure Marketplace, streams health and performance data from the top 10 AWS services and delivers them in dashboards to the Azure Log Analytics service. It provides a single, self-maintaining connection for 100+ enterprise technologies. BindPlane collects more granular metrics than traditional integrations and enhances them with rich-relational context about the entire IT stack – part of a proprietary innovation referred to as dimensional data.
BindPlane gives Microsoft customers another option to manage their IT apps as efficiently and easily as possible, including those apps running outside Azure on AWS.
“Achieving visibility across cloud platforms and applications has traditionally led to cloud tool sprawl and high management overhead, plus the fractured visibility leads to poor end user experience as issues can’t be quickly identified and understood,” explains Nathan Owen, CEO and founder of Blue Medora. “Blue Medora BindPlane helps extend visibility of Microsoft Azure Log Analytics beyond Azure to other leading public cloud providers, enabling a unified, in-context view of cloud behavior and performance for the rapidly increasing cross section of Microsoft customers running multi-cloud workloads.”
“We’ve seen through our recent multi-cloud report that customers are choosing a cloud platform best suited to a particular application, but it can be hard to make an informed decision without a single view to effectively measure and monitor performance,” comments Edwin Yuen, senior analyst at research firm ESG. “BindPlane for Microsoft Azure Log Analytics offers an important way to see across all cloud apps, and in turn help guide decisions around the ideal cloud platform for a given application.”
BindPlane provides seamless monitoring integration between Azure Log Analytics and public cloud resource endpoints. The offering currently includes 10 AWS services, but will soon also include Google Cloud Platform and IBM Softlayer.
Key benefits include:
- Lower cloud costs: Customers can use their existing Azure Log Analytics solution to match cloud performance and price needs for individual applications.
- Saved time: Improves productivity of IT operations and DevOps teams by offloading expensive and non-core monitoring integration investments. Log Analytics users don’t need to invest time configuring, deploying and maintaining new AWS integrations.
- Reduced risk: Unified monitoring enables faster troubleshooting and eliminates management risks introduced by cloud sprawl.
- Seamless integration: Monitor AWS resources with Azure Log Analytics in a single pane of glass
- Services currently included: EC2, Elastic Container Service (ECS), RDS, S3, Redshift, DynamoDB, Lambda, Elastic Beanstalk, Elasticsearch, and Kinesis
- Dimensional data: Deep monitoring data based on expertise and enterprise knowledge base, imbued with relational visibility across the full IT stack. Extensive metrics, more than what’s available in Amazon CloudWatch.
- Operational dashboards: 10 pre-built custom dashboard templates deliver dimensional data in operational context for relational visibility.
The Latest
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
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 ...