DuploCloud announced the launch of the DuploCloud Advanced Observability Suite (AOS), designed to deliver application insights.
DuploCloud has been delivering their self-hosted all-in-one DevSecOps automation and orchestration platform to hundreds of customers, and adding AOS is a natural extension.
DuploCloud's AOS gives customers full control over infrastructure cost via fine tuning the lower layers like cold storage, availability zones, deployment footprint and so on.
The solution is completely set up and customized during onboarding and includes a wide array of integrations that empower developers to optimize application performance, ensure security, and derive meaningful insights from vast amounts of data.
DuploCloud AOS Capabilities:
- Application Performance Monitoring (APM): Actionable telemetry to help developers focus on areas with the greatest impact. Identify bottlenecks, trace flaws, and ensure smooth application performance, keeping services aligned with key SLAs.
- Custom Metrics Collection: Define custom metrics based on your environment's application and infrastructure. These metrics make creating relevant KPIs and SLAs easier, mapping directly to timely business decisions.
- Advanced Troubleshooting with Traces and Logs: Correlating traces and logs across distributed systems drives the identification of root causes of errors and substandard performance. This unified approach enables tagging and tracing significant events or anomalies that might go unnoticed in traditional logging systems.
- End-to-End Observability: As AOS integrates traces, metrics, and logs, you gain comprehensive observability across the entire application stack, from front-end services to back-end databases allowing you to monitor full-stack performance and uncover system-wide issues.
- Real-Time Alerting and Automated Responses: Your metrics and data can be used by DuploCloud or other tools to automatically trigger responses to threshold breaches, anomalies, or downtimes, empowering you to take proactive action.
- Custom Dashboards: Easily create on-demand dashboards focused on specific pain points and meaningful metrics. Examples include Service Health, Request Tracing, App Performance, User Experience, Infrastructure, and System Health, all with numerous visualization options.
"Observability solutions have been available in the open source community for many years, but the key drawback has been the ability to manage the complex stack with in-house resources," said Venkat Thiruvengadam, founder and CEO of DuploCloud. "While SaaS-based observability solutions solved that problem, the pricing model is prohibitive. With the advent of Kubernetes and OpenTelemetry, the management problem of a self-hosted stack can be solved efficiently without paying the ‘SaaS Tax.'"
With DuploCloud's always-on support and no-code/low-code automation, organizations can accelerate time-to-market, reduce costs, and ensure their infrastructure adheres to key compliance standards, such as SOC 2, HIPAA, PCI, and others. Whether running on Kubernetes across multiple cloud providers or integrating with third-party tools, DuploCloud's platform enables seamless operation with maximum flexibility.
DuploCloud's Advanced Observability Suite is available today as an add-on to the company's DevOps Automation Platform.
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.