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IBM Instana Integrates with IBM Turbonomic

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

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IBM Instana Integrates with IBM Turbonomic

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

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 ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...