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Middleware Unveils New Version of its Observability Platform

Middleware announced a new iteration of its full-stack cloud observability platform that helps developers monitor applications and infrastructure in real-time, boost operational efficiency, and minimize downtime.

The latest version provides complete control over telemetry data and costs, new monitoring capabilities, faster root cause analysis, and broader integration support.

"Developers no longer want outdated debugging systems. They seek faster, cost-effective, automated solutions, for monitoring distributed architectures with scalable and easy-to-learn real-time observability capabilities. Initially, our goal was to drive adoption by providing cost-effective, end-to-end observability. Now, we're making it easier for users to instrument services and start quickly, without a steep learning curve. Guided by customer insights, we're enhancing the UX to transform their journey," said Laduram Vishnoi, Founder and CEO, Middleware.

What's improved within Middleware:

- New UI/UX: Middleware now features a completely redesigned frontend, with over 300 refreshed screens, 100 customizable dashboards, new alerts on custom metrics, and a unified date picker.

- Auto Instrumentation: Middleware now supports auto instrumentation for applications written in Python, Node.js, Java, .NET, and Golang using the OpenTelemetry operator. This allows for distributed tracing with zero code changes and simplifies the instrumentation process.

- Datadog Agent Support: Developers can now send logs, metrics, and traces from Datadog agents directly to Middleware. This eliminates the need for new agents or configurations, making the transition to Middleware effortless.

- Status Page: Users can now create status pages and publish them to their websites. It allows for synthetic checks and incident management, providing a public-facing status page that displays the availability of services/products. Users can send notifications via Slack and email, keeping stakeholders informed during outages.

- Product Performance Monitoring within Real User Monitoring (RUM): Middleware's RUM now tracks core web vitals (FCP, LCP, FID, CLS) and extends to native iOS and Android apps, delivering insights on performance metrics and user interactions across mobile platforms.

- New Integrations: Middleware now supports AWS integration for collecting logs, metrics, and events from ECS, EC2, S3, RDS, Firehose, Lambda, and EBS, simplifying AWS monitoring with less complex configurations. Middleware can also ingest logs from Elastic Logstash, thereby replacing Elastic Search and Open Search.

- Log Patterns: This feature allows users to quickly identify logs with similar patterns, facilitating faster root cause analysis.

"We are committed to making our cloud-native observability platform more powerful, intuitive, and cost-effective by using OpenTelemetry, cloud object storage, and fast analytical query processing. We're introducing ingestion controls and pipelines for precise user control over telemetry data sent to Middleware. We'll also leverage AI to detect telemetry anomalies, suggest alerts, and enable querying using natural language," said Tejas Kokje, Head of Engineering at Middleware.

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 ...

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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.

Middleware Unveils New Version of its Observability Platform

Middleware announced a new iteration of its full-stack cloud observability platform that helps developers monitor applications and infrastructure in real-time, boost operational efficiency, and minimize downtime.

The latest version provides complete control over telemetry data and costs, new monitoring capabilities, faster root cause analysis, and broader integration support.

"Developers no longer want outdated debugging systems. They seek faster, cost-effective, automated solutions, for monitoring distributed architectures with scalable and easy-to-learn real-time observability capabilities. Initially, our goal was to drive adoption by providing cost-effective, end-to-end observability. Now, we're making it easier for users to instrument services and start quickly, without a steep learning curve. Guided by customer insights, we're enhancing the UX to transform their journey," said Laduram Vishnoi, Founder and CEO, Middleware.

What's improved within Middleware:

- New UI/UX: Middleware now features a completely redesigned frontend, with over 300 refreshed screens, 100 customizable dashboards, new alerts on custom metrics, and a unified date picker.

- Auto Instrumentation: Middleware now supports auto instrumentation for applications written in Python, Node.js, Java, .NET, and Golang using the OpenTelemetry operator. This allows for distributed tracing with zero code changes and simplifies the instrumentation process.

- Datadog Agent Support: Developers can now send logs, metrics, and traces from Datadog agents directly to Middleware. This eliminates the need for new agents or configurations, making the transition to Middleware effortless.

- Status Page: Users can now create status pages and publish them to their websites. It allows for synthetic checks and incident management, providing a public-facing status page that displays the availability of services/products. Users can send notifications via Slack and email, keeping stakeholders informed during outages.

- Product Performance Monitoring within Real User Monitoring (RUM): Middleware's RUM now tracks core web vitals (FCP, LCP, FID, CLS) and extends to native iOS and Android apps, delivering insights on performance metrics and user interactions across mobile platforms.

- New Integrations: Middleware now supports AWS integration for collecting logs, metrics, and events from ECS, EC2, S3, RDS, Firehose, Lambda, and EBS, simplifying AWS monitoring with less complex configurations. Middleware can also ingest logs from Elastic Logstash, thereby replacing Elastic Search and Open Search.

- Log Patterns: This feature allows users to quickly identify logs with similar patterns, facilitating faster root cause analysis.

"We are committed to making our cloud-native observability platform more powerful, intuitive, and cost-effective by using OpenTelemetry, cloud object storage, and fast analytical query processing. We're introducing ingestion controls and pipelines for precise user control over telemetry data sent to Middleware. We'll also leverage AI to detect telemetry anomalies, suggest alerts, and enable querying using natural language," said Tejas Kokje, Head of Engineering at Middleware.

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.