
IBM announced the integration of real time observability capabilities of IBM Instana with resource optimization capabilities of IBM Turbonomic.
This integration enables companies to run their critical applications smoothly 24x7 in a cost-effective way, empowering their IT operations to proactively act before problems affect their customers and their baseline.
The integration between Instana and Turbonomic is bi-directional, enabling the exchange of contextual data. Turbonomic uses the Instana REST API to collect information about the environment that Instana is observing such as applications, services, endpoints, virtual machines (VMs), containers, Java VMs, database servers, Kubernetes entities—for example, namespaces, deployments, StatefulSets, and others. The Turbonomic Instana probe is the component that uses the Instana API (Instana API token must be provided). The Turbonomic kubeturbo probe is also required for Turbonomic to correlate the discovered Instana application resources to the Kubernetes infrastructure.
Instana uses the Turbonomic REST API to collect actions related to the environment that Instana is observing. The Instana Turbonomic sensor is the component that uses the Turbonomic API (Turbonomic API token must be provided). Instana collects and stores actions that Turbonomic has executed and actions that Turbonomic currently recommends.
Even though Turbonomic will not create actions for Instana applications, services, or endpoints, it must discover Instana applications so that it can obtain the infrastructure supporting the applications, and then, based on its analysis, create actions for them.
The integration provides the following benefits:
- Reduce cloud waste
- Reduce performance risk
- Reduce MTBF and MTTR
The Latest
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
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 ...