Skip to main content

Apptio Releases AI TCO & Usage and Hybrid IT TCO Impact

Apptio, an IBM company, has announced the availability of AI TCO & Usage and Hybrid IT TCO Impact, two features designed to empower enterprises to take control of their hybrid and multi-cloud strategy and to ultimately drive increased ROI from AI and hybrid IT investments.

With AI TCO & Usage, teams can actively monitor spend and performance across AI initiatives to help drive measurable business value. Hybrid IT TCO Impact provides finance teams with visibility into the financial impacts from migrating applications across hybrid environments. The new offerings join Apptio’s slate of SaaS solutions for Technology Business Management (TBM), which encompasses IT financial management (ITFM), FinOps, and strategic portfolio management (SPM), collectively designed to align organizational IT strategy to business value and outcomes.

To help address potential blind spots in cost visibility, IBM Apptio’s Hybrid IT TCO Impact provides comprehensive cost visibility, enabling companies to track application TCO and unit rates across environments, and adjust strategies to reduce costs and enhance efficiency amid constantly evolving migration needs.

“These solutions are built to fill critical gaps in enterprise TBM, to give organizations greater agility to stay on top of the constantly shifting cloud and AI landscapes,” said Eugene Khvostov, Chief Product Officer, Apptio. “AI TCO & Usage is designed to solve the AI ROI dilemma, and we developed Hybrid IT TCO Impact to support technology decision makers on their overall hybrid cloud strategy. Both solutions are specifically tailored to address some of the industry’s most pressing needs, including IT cost transparency and demonstrating value, and we’re excited to bring them to market.”

AI TCO & Usage enables organizations to track total cost and business impact across AI initiatives, helping them optimize spend, measure outcomes, and scale. With active visibility, users can make more informed investment decisions, enable solution adoption, and maximize the impact of AI across the organization. Additionally, users can:

  • Monitor the lifecycle of their AI investments, providing defensible transparency into ongoing AI costs and usage across AI models and AI solutions.
  • Proactively get ahead of AI sprawl by continuously monitoring AI TCO trends and anomalies, obtaining visibility into detailed cost drivers – cloud, vendor, labor – while surfacing AI usage and user adoption across business units.
  • Drive more informed AI scaling decisions by assessing unit economics and enable responsible AI consumption and show back across their organization.

Hybrid IT TCO Impact is a solution available for IBM Apptio Costing that helps enterprises demonstrate the financial impact of application migrations across their evolving Hybrid IT landscape, featuring:

  • Financial Visibility: Provides a dedicated financial view into application migrations, tracking cost shifts and ROI across the hybrid IT landscape. Informed by this data, organizations can balance costs across cloud and on-prem infrastructure.
  • Single-Pane Management: Unifies on-prem, private cloud, and public cloud footprints into a single view, delivering visibility into the hybrid IT estate.
  • End-to-End Insights: Enables active tracking of migration progress, supporting alignment with broader cloud business and financial goals.

“The growing complexity of cloud deployments and increasing investment in AI are making IT cost optimization an increasingly elusive target,” said Jevin Jensen, IDC research vice president, Intelligent Cloud and FinOps. “TCO features are needed across both public and hybrid clouds to stay on top of the constant changes and plan more proactively.”

Hybrid IT TCO Impact and AI TCO & Usage are both available now.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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 ...

Apptio Releases AI TCO & Usage and Hybrid IT TCO Impact

Apptio, an IBM company, has announced the availability of AI TCO & Usage and Hybrid IT TCO Impact, two features designed to empower enterprises to take control of their hybrid and multi-cloud strategy and to ultimately drive increased ROI from AI and hybrid IT investments.

With AI TCO & Usage, teams can actively monitor spend and performance across AI initiatives to help drive measurable business value. Hybrid IT TCO Impact provides finance teams with visibility into the financial impacts from migrating applications across hybrid environments. The new offerings join Apptio’s slate of SaaS solutions for Technology Business Management (TBM), which encompasses IT financial management (ITFM), FinOps, and strategic portfolio management (SPM), collectively designed to align organizational IT strategy to business value and outcomes.

To help address potential blind spots in cost visibility, IBM Apptio’s Hybrid IT TCO Impact provides comprehensive cost visibility, enabling companies to track application TCO and unit rates across environments, and adjust strategies to reduce costs and enhance efficiency amid constantly evolving migration needs.

“These solutions are built to fill critical gaps in enterprise TBM, to give organizations greater agility to stay on top of the constantly shifting cloud and AI landscapes,” said Eugene Khvostov, Chief Product Officer, Apptio. “AI TCO & Usage is designed to solve the AI ROI dilemma, and we developed Hybrid IT TCO Impact to support technology decision makers on their overall hybrid cloud strategy. Both solutions are specifically tailored to address some of the industry’s most pressing needs, including IT cost transparency and demonstrating value, and we’re excited to bring them to market.”

AI TCO & Usage enables organizations to track total cost and business impact across AI initiatives, helping them optimize spend, measure outcomes, and scale. With active visibility, users can make more informed investment decisions, enable solution adoption, and maximize the impact of AI across the organization. Additionally, users can:

  • Monitor the lifecycle of their AI investments, providing defensible transparency into ongoing AI costs and usage across AI models and AI solutions.
  • Proactively get ahead of AI sprawl by continuously monitoring AI TCO trends and anomalies, obtaining visibility into detailed cost drivers – cloud, vendor, labor – while surfacing AI usage and user adoption across business units.
  • Drive more informed AI scaling decisions by assessing unit economics and enable responsible AI consumption and show back across their organization.

Hybrid IT TCO Impact is a solution available for IBM Apptio Costing that helps enterprises demonstrate the financial impact of application migrations across their evolving Hybrid IT landscape, featuring:

  • Financial Visibility: Provides a dedicated financial view into application migrations, tracking cost shifts and ROI across the hybrid IT landscape. Informed by this data, organizations can balance costs across cloud and on-prem infrastructure.
  • Single-Pane Management: Unifies on-prem, private cloud, and public cloud footprints into a single view, delivering visibility into the hybrid IT estate.
  • End-to-End Insights: Enables active tracking of migration progress, supporting alignment with broader cloud business and financial goals.

“The growing complexity of cloud deployments and increasing investment in AI are making IT cost optimization an increasingly elusive target,” said Jevin Jensen, IDC research vice president, Intelligent Cloud and FinOps. “TCO features are needed across both public and hybrid clouds to stay on top of the constant changes and plan more proactively.”

Hybrid IT TCO Impact and AI TCO & Usage are both available now.

The Latest

For decades, traditional observability tools have forced IT professionals to sift through endless dashboards, turning a flood of alerts into actionable insight and driving widespread burnout. While enterprises adopted AI to eliminate this friction, most still deploy it reactively rather than proactively ... As systems become more complex and IT moves to the center of business strategy, a reactive posture is unsustainable ...

Ask an operations team what they monitor and you will hear a familiar list: latency, errors, throughput, uptime. Those signals still matter for AI, but they no longer answer the most important question. As Krishna Sai, CTO of SolarWinds, puts it: "Traditional observability tells you a system is running. AI observability tells you whether it's still reasoning soundly. Those are completely different questions" ...

AI observability serves several roles across an organization. AI engineers use it to build, evaluate, and improve AI systems. SREs use it to operate those systems reliably and efficiently in production, including managing their cost. Leaders need it to connect AI behavior and operational performance to business outcomes. The common need is a clear understanding of whether AI systems are delivering the intended result ...

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 ...