The News
At KubeCon North America 2025, CloudBolt announced Kubernetes cost allocation capabilities providing visibility down to the container level through integration of StormForge agent telemetry with CloudBolt billing data, moving beyond node or cluster-scope cost tracking. The feature, currently in private preview with several customers providing feedback, represents a significant advancement from previous offerings, where StormForge provided estimates based on user inputs and CloudBolt offered billing overlays.
The new integration delivers actual, detailed container-level cost tracking. CloudBolt emphasizes unique value through unified billing data aggregation across clouds that accurately maps costs at the container level, even as workloads move between nodes, with flexible chargeback models allowing organizations to decide whether to pass cloud provider discounts to internal development teams. The company positions the StormForge-CloudBolt integration as an all-in-one solution with a unified UI and backend, contrasting with competitors’ offering siloed or partial tools, targeting both FinOps and platform engineering teams seeking to identify high-spend areas and prioritize optimizations.
CloudBolt plans a broader public release following private preview validation and is preparing a significant presence at the upcoming FinOpsX event to differentiate in a crowded market. The company’s roadmap prioritizes GPU optimization, particularly fractional GPU usage, as the next major focus area, acknowledging industry-wide challenges in AI cost forecasting around token usage and the financial impact of training large language models versus incremental updates.
Analyst Take
CloudBolt’s container-level cost allocation addresses a genuine gap in Kubernetes financial management. Most cost tools provide cluster or namespace-level visibility, but organizations need granular attribution to specific workloads and teams to drive accountability and optimization decisions. Container-level tracking enables accurate chargeback and showback models that align cloud spending with business value, but the technical complexity of maintaining accurate cost mapping as containers move across nodes, scale dynamically, and share infrastructure resources remains substantial.
The integration of StormForge telemetry with CloudBolt billing data suggests a technical approach that combines resource utilization metrics with actual cloud provider billing, but accuracy depends on correct attribution of shared infrastructure costs (networking, storage, control plane) that don’t map cleanly to individual containers. Organizations evaluating container-level cost allocation must verify that the methodology produces defensible numbers that finance and engineering teams both accept, rather than creating disputes about cost accuracy.
The positioning as an “all-in-one solution” with unified UI and backend contrasts with the broader market trend toward composable FinOps tooling, where organizations integrate best-of-breed point solutions. CloudBolt’s integrated approach reduces operational complexity and data consistency challenges inherent in multi-tool stacks, but it also creates vendor dependency and may limit flexibility as organizations’ needs evolve.
The flexible chargeback model, allowing organizations to decide whether to pass cloud provider discounts to development teams, addresses a real enterprise policy question: should internal teams benefit from enterprise discount agreements, or should central IT retain savings to offset platform costs? However, this flexibility also creates complexity as different chargeback policies across teams or business units complicate cost comparisons and may create internal equity concerns.
Our Day 1 research found that 43.90% of IT budgets go to cloud infrastructure, making accurate cost allocation and optimization commercially significant, but the question is whether container-level granularity provides actionable insights or simply creates data overload that teams lack the resources to act upon.
The private preview approach with customer feedback before public launch reflects product development discipline, but it also suggests the feature may not yet be production-ready for broad deployment. Container-level cost allocation at scale introduces performance concerns; collecting, processing, and storing granular telemetry for every container across large Kubernetes fleets generates substantial data volume and processing overhead.
Organizations must evaluate whether the cost allocation system itself introduces meaningful infrastructure costs or performance impact that offsets optimization benefits. The planned broader release following validation will reveal whether early customer feedback identifies fundamental issues requiring architectural changes or simply refinement of existing capabilities.
The GPU optimization roadmap focuses particularly on fractional GPU usage, positioning CloudBolt to address emerging cost challenges as AI workloads proliferate. Our Day 0 research found that 70.4% of organizations are investing in AI/ML capabilities, and GPU costs represent a substantial portion of AI infrastructure spending. However, GPU cost optimization differs fundamentally from CPU/memory optimization.
GPUs are typically fully utilized or idle with little middle ground. Fractional GPU sharing introduces performance unpredictability, and the relationship between GPU allocation and model training time or inference throughput is non-linear. CloudBolt’s acknowledgment of industry-wide challenges in AI cost forecasting, particularly around token usage and training versus fine-tuning costs, reflects an honest assessment of unsolved problems. The company’s success depends on whether it can deliver GPU optimization capabilities that provide genuine cost reduction without compromising AI workload performance or introducing operational complexity that offsets savings.
“Here are two screenshots of our new capability (first is without the idle costs distributed and second is with). This is pulling FOCUS data across the public clouds and includes any discounts applied, then maps the usage with the data collected from the StormForge agent to do cost allocation down to the container level. You can allocate by any dimension including labels.” – Yasmin Rajabi
Looking Ahead
CloudBolt’s success with container-level cost allocation depends on demonstrating clear ROI through optimization actions that customers take based on the visibility provided. Cost visibility alone does not reduce spending; organizations must translate insights into concrete actions like rightsizing workloads, eliminating waste, or renegotiating cloud commitments. The next 12-18 months will reveal whether CloudBolt’s customers achieve measurable cost reductions that justify the platform’s investment, or whether container-level granularity proves interesting but not actionable. The company’s challenge is moving beyond cost reporting to delivering prescriptive recommendations and automated optimizations that drive results without requiring dedicated FinOps team resources that many organizations lack.
The competitive landscape for Kubernetes cost management and FinOps platforms is intensifying as cloud providers, observability vendors, and specialized FinOps tools all target the same market. CloudBolt competes with cloud-native cost tools like AWS Cost Explorer and Azure Cost Management that benefit from deep provider integration, with Kubernetes-focused platforms like Kubecost that offer open-source alternatives, with observability vendors like Datadog adding cost features, and with enterprise FinOps platforms like Apptio and CloudHealth that provide broader financial management capabilities.
The company’s differentiation through StormForge integration and container-level granularity provides positioning, but success requires demonstrating advantages that justify dedicated platform investment versus adopting cost features embedded in tools organizations already deploy. As FinOps capabilities become commoditized and embedded across infrastructure tooling, CloudBolt must either deliver substantially superior outcomes or find adjacent value propositions, like the planned GPU optimization, that create sustained differentiation beyond basic cost allocation and reporting.
High Availability Becomes a Strategic Control Layer in 2026 IT Architectures
NKP 2.17 Signals a Security-First Turn in Enterprise Kubernetes Platforms
Omni Agent Intelligence Signals the Next Phase of AI-Native CX Governance
Always-On Retail Becomes a Platform Requirement at NRF 2026
Agentic Commerce Moves From Concept to Control Plane at NRF 2026
