Netuitive announced details about its upcoming major release, Netuitive 6.0, which features the industry’s first open API to a predictive analytics platform.
The innovations in this release improve usability and eliminate vendor lock-in by enabling extensible data integration and correlation capabilities required to achieve end-to-end application performance management (APM).
One of the biggest challenges for APM is how to quickly correlate and extract value from the vast and unmanageable amounts of data (e.g., business activity, customer experience, applications, and infrastructure) collected from a plethora of specialized monitoring tools.
“The APM market is confronted with a big data problem. As our customers have progressed in the deployment of more agents to monitor their real-time performance at the business, application and infrastructure levels, they have been confronted with a deluge of data that is humanly impossible to correlate and interpret with traditional monitoring tools,” said Nicola Sanna, CEO of Netuitive. “Netuitive 6.0 provides a leap in extensibility by providing the tools and analytics necessary to turn data from new data sources into actionable insight.”
Netuitive 6.0 delivers rapid extension capabilities to integrate with and correlate any third party data source, allowing power users and partners to meet their unique data analysis needs at their own pace.
Netuitive 6.0 provides meaningful visibility into the health of applications, automatically isolates problems across silos and domains, and delivers proactive notifications of impending performance issues.
Orbitz is a Netuitive customer who has tested Integration Studio. “Our company is rapidly growing our big data collections. With new types of data arriving weekly, the new Studio feature in the 6.0 release will let us adapt quicker, easier and better,” said Brian Devlin, Operations Engineer for Orbitz.
Netuitive 6.0 innovates in three key areas:
- Integration Studio for Unlimited Extensibility
- Most Complete View into Java Application Ecosystem
- Improved User Interface and New Algorithms for Optimized APM Diagnostics
Integration Studio
Netuitive has developed an SDK empowering users to:
- Integrate and correlate data streams from any monitoring source
- Edit the settings via UI-based configuration wizards
- Create best practices templates for integrations with new and custom data sources
On this new analytics platform, data is collected and normalized in Netuitive’s Performance Management Database (PMDB), analyzed by Netuitive’s Behavior Learning technology, with actionable outputs delivered based on the analysis.
Central to this capability are APIs enabling integration with configuration management databases, incident management, and programmatic administration of users security and policies.
Integration Studio complements Netuitive’s Open approach and broad portfolio of standard integrations with leading monitoring tools from more than 30 companies including leaders such as CA, BMC, HP, IBM, VMware, Microsoft, Compuware, EMC, NetApp, Oracle, and many others. These standard integrations embed best practices for a rapid implementation and immediate time to value.
Most Complete View Into Java Application Ecosystem
Netuitive has developed new models for complex, distributed Java applications. By introducing new managed elements types such as JVM and Web Application clusters, and then applying Behavior Learning technology to application performance, Netuitive 6.0 provides much greater visibility into Java applications running in large distributed environments while preserving relationships that power diagnostics.
The software also correlates application and infrastructure performance data together, facilitating performance troubleshooting across enterprise silos. This new modeling capability is available in the standard product with built-in best practices to deliver a rapid time to value.
Improved User Interface and New Algorithms Optimized for APM Diagnostics
Netuitive’s innovative APM graphical user interface provides real time, multi-tier visibility into application performance management across silos and domains. It takes a problem-oriented event-management approach, leveraging critical knowledge bases, to automate and categorize problems.
It then provides prescriptive advice making Netuitive’s composite alerting and health index more actionable and intuitive to understand. New algorithms have been developed to make Netuitive’s unique Trusted Alarms and health index more intuitive and easy to interpret at a glance.
Ultimately, the new features simplify troubleshooting workflow and the detection of anomalies that can lead to service degradation and outages. The combination is a game-changing leap forward in the way that analytics delivers value to IT operations.
“A major challenge of large-scale multi-vendor APM implementations is the analytics - whether integrated or provided by independent vendors. Feeding the analytics tools with relevant business and IT data sources is required to automatically build relationships, key performance indicators and service models,” said Jonah Kowall, Research Director for Gartner.
“Solutions that leverage behavior learning engines can help automate the correlation of these disparate data sources. When tooling is provided customers may extend the product to almost any data source, which helps the analytics solution to become engrained across an entire organization.”
Eight of the world’s 10 largest banks and several global service providers have deployed Netuitive’s self-learning predictive analytics software that sits on top of their traditional monitoring tools, providing a holistic view of performance across business, application, and infrastructure silos.
Hot Topic
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
