April 2026 monthly summary for redhat-openshift-ecosystem/community-operators-prod focusing on Koku Metrics Operator enhancements and NVIDIA MIG support. Key outcomes include the delivery of Koku Metrics Operator v4.4.0 with enhanced usage metrics collection for OpenShift cost management, improved configurability, and NVIDIA MIG metrics support. The change is backed by commit 8184ba7cc5dc55b353e239ccbe7e52d020ea64c4 (koku-metrics-operator v4.4.0 (#9343)). This work improves visibility into OpenShift usage and GPU resource costs, enabling better cost governance and capacity planning.

Concise monthly summary for March 2026 focused on delivering scalable, high-value improvements to project-koku/koku. Implemented AWS Reports Tag Rate Limiting Enhancement to broadening tag throttling to AWS reports, aligning resource usage controls with existing OCP report throttling, and setting the foundation for handling heavy time scopes and large date ranges.

February 2026 Monthly Summary for project-koku/koku focusing on OpenShift resource filtering, tag query handling, and on-prem deployment support. Delivered features and fixes that enhance cloud-provider-specific resource retrieval, improve query reliability, reduce server load, and enable hybrid cloud/on-prem deployments with minimal configuration changes.

January 2026 (Month: 2026-01) – Project: project-koku/koku Concise monthly summary focusing on business value, technical delivery, and impact across features and fixes implemented this month. Key accomplishments include delivery of critical fixes, security hardening, CI improvements, and tenancy-aware ingestion enhancements, all contributing to cost accuracy, safer data handling, and operability.

December 2025 focused on delivering robust GPU cost analytics and expanding API flexibility for OpenShift-backed resources, with targeted reliability improvements to the GPU reporting pipeline. Key outcomes include a new GPU report endpoint with a cost model, enhanced aggregation, unique GPU counts for OpenShift reports, and preserved UI readability. Also added a global openshift parameter for filtering across AWS and GCP views, enabling customers to segment cost data by OpenShift accounts. The team implemented substantial query and data handling improvements, corrected ordering and grouping edge cases, and introduced unit tests to enforce correctness. These efforts drive faster, more accurate cost visibility for AI/ML workloads and OpenShift-managed resources, underpinning cost optimization decisions and better customer satisfaction.

November 2025 Monthly Summary for project-koku/koku: What we delivered this month centers on GPU cost visibility and usage analytics for OpenShift, along with robustness improvements and tooling upgrades that support reliable, scalable reporting and forecasting for GPU-related workloads. Key business value: - Improved accuracy and granularity of GPU cost reporting for OpenShift on-prem, enabling better budgeting and chargeback. - Expanded visibility into GPU usage with a new API endpoint and report type, supporting data-driven optimization of GPU assets. - Strengthened testing and test infrastructure to reduce regressions and accelerate future iterations. Overall impact: - Delivered end-to-end GPU reporting enhancements (config, processing, reporting type, and tests) and introduced GPU cost metric support across the cost model, including migrations and a feature flag to govern roll-out. - Established a robust data path from ingestion to API consumption, with safeguards for missing Trino tables and improved error handling in metrics endpoints. - Upgraded test tooling (nise) enabling richer mocks for more reliable test coverage. Technologies/skills demonstrated: - Back-end: Python/SQL, Trino ingestion, OpenShift cost modeling, OpenAPI spec, feature flag patterns. - Data engineering: report processor/post-processor enhancements, new GPU metrics and table mappings, SQL migrations. - DevX: unit tests for GPU reporting and cost metrics, endpoint design for GPU usage, and improved test infrastructure. Notes on scope: - All changes are contained to repo project-koku/koku and align with COST-7000, COST-6627, COST-6436, and COST-5796 work items, with a collaboration note on GPU cost metrics co-authored by Cody Myers and myersCody.

October 2025 monthly summary for project-koku/koku focusing on delivering user-facing features, fixing critical report filtering issues, expanding currency coverage, and hardening report processing. The work emphasized business value by improving reporting accuracy, enabling new currency support for NGN, and increasing resilience against transient data processing errors.

Concise monthly summary for 2025-09 focused on delivering measurable business value and robust technical improvements for project-koku/koku.

Month: 2025-08 | Repository: project-koku/koku. Focused on deprecation messaging for Azure v1 reports. Implemented a log-level change from error to warning and added a clear message guiding users toward modern report schemas. This reduces log noise, improves user guidance during migration, and prepares the product for deprecation of Azure v1 reporting. No other high-impact features or bug fixes were recorded this month.

July 2025 delivered targeted enhancements to improve reporting accuracy, cost visibility, and reliability across cloud providers. Key work includes dynamic tag-key filtering in the Daily Summary Report via a Common Table Expression to fetch enabled keys for AWS/Azure/GCP and OCP, and adding vm_kubevirt_io_name to the enabled list; CZK currency support with corresponding updates to currency lists and exchangerates migration; fractional-hour VM cost calculations for tag-based usage with refactored SQL/Python logic and updated tests; and a stability fix for month-boundary VM summaries by adjusting the query to use the second-to-last day with valid VM data. These changes reduce data gaps, broaden currency coverage, and improve cost precision and test resilience, delivering measurable business value for multi-cloud cost visibility and reporting reliability.

June 2025 — Key accomplishments in project-koku/koku included strengthening build isolation, simplifying cloud-provider strategy, and boosting dashboard performance. Delivered a Tekton pipeline enhancement to run builds with a dedicated service account, deprecated IBM Cloud in favor of OCI with a clean migration, and reduced CI noise by gating Jenkins tests behind a PR label. Expanded key retrieval for more comprehensive queries and delivered Grafana dashboard performance improvements with pre-aggregated metrics, resulting in faster dashboards and lower query load. These changes collectively improve security, maintainability, and business analytics capabilities while reducing operational overhead.

May 2025 monthly summary for project-koku/koku: The team delivered targeted features and reliability improvements that reduce maintenance overhead, improve cost visibility, and accelerate issue diagnosis in the Dockerized environment. Key work includes OCI provider removal to streamline the codebase, a Docker logs debug Makefile target to speed debugging, and granular OpenShift infrastructure cost reporting. CI reliability was improved by adjusting timeouts to reduce flaky tests, aligning delivery with business needs for faster feedback and stable releases.

Concise monthly summary for 2025-04 focused on delivering business-value through cost visibility features and data integrity improvements in project-koku/koku.

March 2025 – Project-koku/koku: Focused on accuracy, governance, and cost visibility. Delivered three major items: (1) date delta calculation fix for negative time scopes to ensure the relative delta is exactly one month, eliminating misaligned period estimates in reporting; (2) a feature-flagged API path for the new source status retrieval method with backward-compatible POST routing and tests validating flag behavior; and (3) a cost-model enhancement introducing cluster_core_cost_per_hour and integrating it into configuration and SQL queries to account for cluster-level core-hour usage. These changes improve data accuracy, enable safer feature rollouts, and provide more precise cost visibility for multi-tenant deployments. Technologies demonstrated include Python backend work, feature flag patterns, test-driven development, and SQL-based cost modeling. Business value: more reliable period reporting, controlled feature experiments, and improved cost accounting for cluster usage.

February 2025: Implemented cost visibility enhancements for OpenShift VM costs in the Koku project to improve cost governance and transparency. Added new cost-related fields to the OpenShift VM provider map and introduced detailed cost breakdowns for distributed resources (platform, worker, network, and storage). Included a unit test validating accurate reporting of distributed costs in the virtual machines endpoint, enabling precise chargeback/showback and better resource planning. This work strengthens cost accuracy, reduces manual reconciliation, and sets the groundwork for future cost dimensions across VM resources.

January 2025 performance summary for project-koku/koku: Focused on CI/CD improvements by centralizing the build pipeline configuration into a dedicated koku-ci repository, moving the pipeline setup out of the main repository, and updating references in PR and push workflows. This change reduces main-repo churn, improves pipeline security and maintainability, and lays groundwork for faster, more reliable builds.

December 2024 — Project: project-koku/koku. Delivered improvements to Azure Cost Report Processing and enhanced resilience for missing CostModel references, enabling more reliable cost visibility and cleaner data hygiene. These updates reduce processing errors, support multi-file types, and prevent orphaned data.

November 2024: Targeted bug fix in EC2 Compute Cost Report to ensure it uses the most recent EC2 resource data, improving accuracy, reliability, and governance of cost reporting.