IBM Turbonomic

What is IBM Turbonomic?

IBM Turbonomic is an IT resource optimization tool - it analyzes your VMs, containers, and cloud, then tells you (or automatically does): “This VM has 16 GB RAM but uses 2 GB - reduce to 4 GB” or “This Kubernetes pod keeps restarting because it has too little CPU”.

Problem it solves:

• Over-provisioning - VM with 32 GB RAM uses 4 GB. You’re paying for 28 GB you don’t use
• Under-provisioning - Pod has 500m CPU limit but needs 800m. Application is slow
• Guessing - “Give it 16 GB just in case” instead of data-driven decisions

What does Turbonomic do?

• Collects resource utilization data (CPU, RAM, storage, network)
• Analyzes patterns and generates recommendations
• Can automatically execute actions (resize, migrate, scale)

Bear in mind that a system making changes to production on its own needs the same control framework as any privileged automation. We develop this below.

How does it work?

1. Data Collection

Turbonomic connects to your infrastructure:

[vCenter] ----\
[AWS Account] -------+---- [Turbonomic] ---- Analysis
[Azure] -------------+          ↓
[Kubernetes] --------/    Recommendations/Actions

Integrations:

• vSAN, NSX
• AWS, Azure, GCP
• Kubernetes, OpenShift, Rancher
• Hyper-V, Nutanix
• Storage (Pure, NetApp, Dell)

2. Analysis and Recommendations

Turbonomic analyzes each resource and generates actions:

Example recommendations:

Resource	Problem	Action
VM prod-db-01	32 GB RAM, uses 8 GB	Reduce to 12 GB
Pod order-svc	CPU limit 500m, throttling 40%	Increase to 800m
EC2 i-abc123	Type m5.xlarge, uses 20% CPU	Change to m5.large
Azure VM	Runs 9-17, idle at night	Shut down after hours

3. Automation (Optional)

You can set the automation level:

• Recommend - only shows what to do
• Manual - shows + creates ticket/workflow
• Automated - executes automatically (with policies)

Automation example:

• Non-prod VM: automatic resize when utilization < 20%
• Prod VM: only recommendation, requires approval
• Kubernetes non-critical: automatic scaling
• Kubernetes critical: manual action

Where do you save?

On-premises

• VM right-sizing - smaller VMs = more VMs per host = fewer hosts to buy
• Storage tiering - archival data on cheaper storage
• Consolidation - better utilization of existing hosts

Typical result: 20-40% reduction in required resources.

Cloud (AWS/Azure/GCP)

• Instance right-sizing - m5.xlarge → m5.large = 50% cheaper
• Reserved Instances - Turbonomic tells you what’s worth reserving
• Savings Plans - commitment optimization
• Spot instances - identify workloads that can use spot
• Shutdown - dev/test after hours

Typical result: 25-40% cloud bill reduction.

Kubernetes

• Pod right-sizing - requests/limits matched to actual usage
• Node optimization - better pod packing on nodes
• Cluster efficiency - fewer nodes with same performance

Typical result: 30-50% cluster cost reduction.

Turbonomic vs Competition

Feature	Turbonomic	CloudHealth	Spot.io
On-prem virtualization	Yes	No	No
Kubernetes	Yes	Partial	Yes
Multi-cloud	Yes	Yes	Yes
On-prem	Yes	No	No
Automation	Full	Limited	Full
Performance guarantee	Yes	No	Partial

When Turbonomic?

• You have hybrid (on-prem + cloud) - one tool for everything
• You need automation, not just reports
• You want to optimize performance, not just costs
• Kubernetes + traditional infrastructure

Who is it for?

Turbonomic makes sense when:

• You spend >500K PLN annually on infrastructure/cloud
• You have on-prem and/or Kubernetes
• You don’t know if your VMs/pods are properly sized
• FinOps/ITOps wants data for decisions

Turbonomic does NOT make sense when:

• Small infrastructure (<20 VMs)
• Only cloud without on-prem - native tools may suffice
• No resources to act on recommendations

How much does it cost?

Licensing model per managed unit (VM, pod, cloud instance):

Size	Units	Approximate annual price
Small	100	~€19K
Medium	500	~€70K
Large	2000+	Custom

ROI: Typically 3-6 months. Savings > license cost.

Specifications

Deployment	SaaS or on-premises
Integrations	AWS, Azure, GCP, Kubernetes
Automation	Recommend, Manual, Automated
API	REST API, Terraform provider
Reporting	Dashboards, export, BI integration

Automated resource decisions need guardrails

A system that resizes machines and moves workloads on its own performs actions with production impact. That calls for the same control framework as any other privileged automation.

• Privilege scope. The account used to make changes has access to the virtualization or cloud layer. Its compromise means the ability to shut down and relocate workloads.
• Headroom and resilience. Optimisation removes spare capacity. Decide deliberately how much headroom remains for sudden load scenarios — including a volumetric denial-of-service attack.
• Change log. An automated configuration change nobody approved must leave a trace that distinguishes system action from attacker action.
• Recommendation mode for critical systems. For regulated environments it often makes sense to leave the decision with a human, even at the cost of some savings.

We align automation privilege scope with regulatory requirements as part of NIS2 compliance.

FAQ

Where do the savings come from? Average from deployments. Mainly right-sizing (VMs have 2-3x more resources than needed) + cloud optimization.

Will Turbonomic shut down production VMs? Not automatically. You define policies - what can be automatic, what requires approval.

How quickly do you see savings? First recommendations: hours. Actual savings after executing actions: days/weeks.

Does it integrate with ServiceNow? Yes. Can create tickets with recommendations, CMDB integration.

What about Kubernetes? Full support. Analyzes pod requests/limits, recommends changes, can automatically modify deployments.

Does it replace monitoring (Prometheus, Datadog)? No. Turbonomic uses monitoring data for optimization. Complementary tools.

What’s the difference between SaaS vs on-prem? SaaS - faster start, less management. On-prem - for companies that don’t want data in cloud.

Does it support Reserved Instances? Yes. Analyzes utilization and tells you which instances are worth reserving.

Does nFlo implement Turbonomic? Yes. Integration with environment, policy configuration, training for FinOps/ITOps teams.