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FinOps Foundation Launches FOCUS 1.3

The FinOps Foundation, a part of the Linux Foundation's nonprofit technology consortium focused on advancing the people and practice of FinOps, announced the launch of FOCUS 1.3 (FinOps Open Cost and Usage Specification) adding solutions to three persistent problems: splitting shared resource costs, tracking contract commitments accurately, and verifying data freshness.

With FOCUS 1.3 – the fifth major release since the FOCUS specification launched in 2023 – the specification includes transparency for provider-defined services that allocate shared costs for containers and databases, introduces a new external dataset for contract or negotiated agreements, and includes metadata signals for dataset completeness and recency – all of which allow practitioners to more accurately and securely manage their multi-provider reporting workflows.

All major clouds have been contributors to the ongoing FOCUS 1.3 specification development. Further, FOCUS is already supported by cloud and technology providers worldwide including Alibaba, AWS, Databricks, Google Cloud, Grafana, Huawei, Microsoft, Oracle Cloud Infrastructure, OVH Cloud, and Tencent. In addition, many enterprises are using FOCUS language in their internal reporting systems, and generating FOCUS datasets for their data center and private cloud spending.

The overarching aim of FOCUS is to reduce complexity across cloud, SaaS, data center, and other technology vendors to reduce complexity for FinOps practitioners who are managing the value of these technologies. The list of FOCUS dataset generators originally included major cloud providers, and now has grown to encompass SaaS, software, data and platform providers.

"With over a dozen providers already supporting FOCUS and AWS announcing generally available FOCUS 1.2 support, FOCUS is hitting yet another big milestone in terms of a rapidly maturing data structure with the new 1.3 release. The common definitions that FOCUS provides for billing data generators enable FinOps practitioners to spend less time on data normalization and more time finding valuable business insights," said J.R. Storment, Executive Director of the FinOps Foundation. "FOCUS 1.3 goes deeper to unpack shared cost allocations, enables a broader swath of providers to support the specification, adds a new contract dataset that can be permissioned separately from cost and usage data, and helps shine light on the freshness of data."

New enhancements include:

  • Split Shared Costs and Understanding of Data Generators' Allocation Method. Today, shared resources, such as Kubernetes pods and database instances, force practitioners to build custom allocation logic or accept non-granular cost allocations. That changes with FOCUS 1.3. New allocation-specific columns let data generators expose how they split costs across workloads. Practitioners see the methodology—not just the result. This will especially benefit platform engineering teams, FinOps analysts allocating shared infrastructure, and DevOps leaders managing multi-tenant clusters.
  • Track Contract Commitments in a Dedicated Dataset. Practitioners can make more informed decisions if they understand how much their existing cost and usage is applicable to terms spelled out in service provider contracts. But those details are difficult to tie back to data presented in cost and usage datasets. In FOCUS 1.3, a new, "Contract Commitment" dataset isolates contract terms, including such things as start/end dates, remaining units, and descriptions, from cost/usage rows. One query shows all active commitments. This enhancement marks the first instance of extending FOCUS language to an adjacent dataset, giving practitioners a structured method to understand contract commitments.
  • Verify Data Recency and Completeness. Processing stale or incomplete cost and usage data can break automated reconciliation workflows. Practitioners might only discover missing rows after month-end close. With FOCUS 1.3, providers must timestamp datasets and flag completeness status so users know if data is subject to revision. This will result in less processing of incomplete data and more confidence in making decisions knowing that data is complete and relevant.
  • Service Provider and Host Provider Columns. Definitions will be more delineated between service and host providers so that practitioners will more easily see who they should engage for support and billing inquiries.

In addition to the launch of the FOCUS 1.3 specification, the FinOps Foundation is announcing a forthcoming update to the FinOps Certified FOCUS Analyst course to include features from FOCUS 1.2. The updated course will allow for more detailed, unified reporting across cloud, SaaS, and PaaS services.

The FOCUS project always welcomes feature requests for future releases and contributors The FOCUS 1.4 specification is already under development, given a backlog of member feedback and requests. Future FOCUS versions will deepen support for cloud and SaaS billing while expanding to additional spending scopes, including data center and AI - join the FOCUS Project to help develop the next release.

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FinOps Foundation Launches FOCUS 1.3

The FinOps Foundation, a part of the Linux Foundation's nonprofit technology consortium focused on advancing the people and practice of FinOps, announced the launch of FOCUS 1.3 (FinOps Open Cost and Usage Specification) adding solutions to three persistent problems: splitting shared resource costs, tracking contract commitments accurately, and verifying data freshness.

With FOCUS 1.3 – the fifth major release since the FOCUS specification launched in 2023 – the specification includes transparency for provider-defined services that allocate shared costs for containers and databases, introduces a new external dataset for contract or negotiated agreements, and includes metadata signals for dataset completeness and recency – all of which allow practitioners to more accurately and securely manage their multi-provider reporting workflows.

All major clouds have been contributors to the ongoing FOCUS 1.3 specification development. Further, FOCUS is already supported by cloud and technology providers worldwide including Alibaba, AWS, Databricks, Google Cloud, Grafana, Huawei, Microsoft, Oracle Cloud Infrastructure, OVH Cloud, and Tencent. In addition, many enterprises are using FOCUS language in their internal reporting systems, and generating FOCUS datasets for their data center and private cloud spending.

The overarching aim of FOCUS is to reduce complexity across cloud, SaaS, data center, and other technology vendors to reduce complexity for FinOps practitioners who are managing the value of these technologies. The list of FOCUS dataset generators originally included major cloud providers, and now has grown to encompass SaaS, software, data and platform providers.

"With over a dozen providers already supporting FOCUS and AWS announcing generally available FOCUS 1.2 support, FOCUS is hitting yet another big milestone in terms of a rapidly maturing data structure with the new 1.3 release. The common definitions that FOCUS provides for billing data generators enable FinOps practitioners to spend less time on data normalization and more time finding valuable business insights," said J.R. Storment, Executive Director of the FinOps Foundation. "FOCUS 1.3 goes deeper to unpack shared cost allocations, enables a broader swath of providers to support the specification, adds a new contract dataset that can be permissioned separately from cost and usage data, and helps shine light on the freshness of data."

New enhancements include:

  • Split Shared Costs and Understanding of Data Generators' Allocation Method. Today, shared resources, such as Kubernetes pods and database instances, force practitioners to build custom allocation logic or accept non-granular cost allocations. That changes with FOCUS 1.3. New allocation-specific columns let data generators expose how they split costs across workloads. Practitioners see the methodology—not just the result. This will especially benefit platform engineering teams, FinOps analysts allocating shared infrastructure, and DevOps leaders managing multi-tenant clusters.
  • Track Contract Commitments in a Dedicated Dataset. Practitioners can make more informed decisions if they understand how much their existing cost and usage is applicable to terms spelled out in service provider contracts. But those details are difficult to tie back to data presented in cost and usage datasets. In FOCUS 1.3, a new, "Contract Commitment" dataset isolates contract terms, including such things as start/end dates, remaining units, and descriptions, from cost/usage rows. One query shows all active commitments. This enhancement marks the first instance of extending FOCUS language to an adjacent dataset, giving practitioners a structured method to understand contract commitments.
  • Verify Data Recency and Completeness. Processing stale or incomplete cost and usage data can break automated reconciliation workflows. Practitioners might only discover missing rows after month-end close. With FOCUS 1.3, providers must timestamp datasets and flag completeness status so users know if data is subject to revision. This will result in less processing of incomplete data and more confidence in making decisions knowing that data is complete and relevant.
  • Service Provider and Host Provider Columns. Definitions will be more delineated between service and host providers so that practitioners will more easily see who they should engage for support and billing inquiries.

In addition to the launch of the FOCUS 1.3 specification, the FinOps Foundation is announcing a forthcoming update to the FinOps Certified FOCUS Analyst course to include features from FOCUS 1.2. The updated course will allow for more detailed, unified reporting across cloud, SaaS, and PaaS services.

The FOCUS project always welcomes feature requests for future releases and contributors The FOCUS 1.4 specification is already under development, given a backlog of member feedback and requests. Future FOCUS versions will deepen support for cloud and SaaS billing while expanding to additional spending scopes, including data center and AI - join the FOCUS Project to help develop the next release.

The Latest

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 ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.