Apptio is now available in the ServiceNow Store.
This integration with ServiceNow provides users with a near real-time view into their organization's actual technology cost structure. By combining operational data from ServiceNow with IT cost modeling software from Apptio, technology leaders are able to make smarter investment decisions more quickly.
In today's fast-moving marketplace, business leaders need to make smart bets and maximize investments. Making quick decisions based on this data requires navigating the most complicated challenges in IT financial management: understanding the complete topology and costs of IT investments and measuring the return on those investments. Apptio has solved this problem by leveraging ServiceNow's ability to auto-discover changes in IT assets and their interconnected relationships. This information allows Apptio to create detailed, accurate cost models which automatically update at the speed of business changes. This is the level of speed, confidence, and fidelity Apptio and ServiceNow's joint customers have been asking for.
"Apptio's mission is to help our customers tackle the mounting challenges they face by empowering them with trusted information to make decisions at the pace that modern business demands," said Scott Chancellor, CPO and Technology Officer at Apptio. "This new integration with ServiceNow is a natural extension of that mission and delivers unprecedented transparency into organizations' IT costs."
Apptio's ServiceNow connectors include seven connectors bringing information into Apptio and a new connector publishing data back to ServiceNow dashboards and are available as a certified solution in the ServiceNow Store. The ingress connectors map ServiceNow data sets directly into ApptioOne, which allows costs to be modeled based on consumptive data, creating a trustworthy, timely and accurate total cost of ownership. The egress connector then pushes this defensible application TCO data from Apptio back into native ServiceNow dashboards, providing visibility to stakeholders across the business. This new level of bi-directional integration enables users to not only see their operational data and metrics for top applications, but also the fully burdened cost structure with continuous updates.
The Latest
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...