FinOpsly is the Value Control™ platform for Cloud, Data, and AI economics.
It helps enterprises move beyond cost visibility to actively control spend and business outcomes through explainable, policy-governed AI automation.
Unlike reporting-only FinOps tools, FinOpsly unifies cloud (AWS, Azure, GCP), data (Snowflake, Databricks, BigQuery), and AI costs into a single system of action — enabling teams to plan spend before it happens, automate optimization safely, and prove value in weeks, not quarters.
FinOpsly enables enterprises to:
Map spend to business value across products, teams, customers, and workloads
Explain cost drivers clearly with AI-generated context and root-cause analysis
Automate optimization safely using policy-driven, explainable agents
Prevent drift and overages before they impact budgets or performance
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JS7 JobScheduler is an Open Source workload automation system designed for performance, resilience and security. It provides unlimited performance for parallel execution of jobs and workflows. JS7 offers cross-platform job execution, managed file transfer, complex no-code job dependencies and a real REST API.
Platforms
- Cloud scheduling from Containers for Docker®, Kubernetes®, OpenShift® etc.
- True multi-platform scheduling on premises for Windows®, Linux®, AIX®, Solaris®, macOS® etc.
- Hybrid use for cloud and on premises
User Interface
- Modern, no-code GUI for inventory management, monitoring and control with web browsers
- Near real-time information brings immediate visibility of status changes and log output of jobs and workflows
- Multi-client capability, role based access management
High Availability
- Redundancy and Resilience based on asynchronous design and autonomous Agents
- Clustering for all JS7 products, automatic fail-over and manual switch-over
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StormForge
StormForge Optimize Live continuously rightsizes Kubernetes workloads to ensure cloud-native applications are both cost effective and performant while removing developer toil.
As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the Kubernetes horizontal pod autoscaler (HPA) at enterprise scale. Optimize Live addresses both over- and under-provisioned workloads by analyzing usage data with advanced machine learning to recommend optimal resource requests and limits.
Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always right-sized, and freeing developers from the toil and cognitive load of infrastructure sizing.
Organizations see immediate benefits from the reduction of wasted resources — leading to cost savings of 40-60% along with performance and reliability improvements across the entire estate.
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Bluesky
Bluesky’s patent-pending technology analyzes how you use Snowflake to provide an accurate breakdown on individual query costs. See how much every user and team is spending. Bluesky groups queries that are structurally similar, using our advanced knowledge of real-world workloads. Quickly find the most expensive workload slices and prioritize your optimization efforts. Get proactive alerts of anomalies or exceptions that really matter. Take action quickly without drowning in noise. Bluesky provides tuning recommendations prioritized by the impact on your environment. Our projected cost savings and query performance improvements are based on how you actually use data, not a theoretical environment that doesn’t exist. Automatically block low-value but expensive queries like data pipelines that keep failing. Configurable blocking rules keep you innovating without overspending. Experiment, tune, and debug with confidence, with a full blocking audit trail, of course!
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