
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"Developers no longer want outdated debugging systems.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
For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...
Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...
AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...
Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...
Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...
When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...
In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...
Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...
AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...
Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...