5.0 out of 5 stars
(1 Rating)
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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4.7 out of 5 stars
(3 Ratings)
FinOpsly is an AI Cost Governance platform. It brings AI, cloud, data platform and SaaS spend into one attribution, policy and control layer, so enterprises can price a workload before building it, attribute every dollar to an owner, hold spend inside budget under policy, and prove what landed in run-rate.
Your AI invoice is not what your AI costs. One request draws on model tokens, retrieval, warehouse queries, GPU capacity and storage, and only the first shows up on the AI bill. FinOpsly resolves all of it, plus the seats in procurement and the compute in an untagged cloud account, to the same dimensions: owner, team, application, line of business, customer and tenant. An AI initiative's full cost becomes one figure, charged back through one hierarchy in one cycle.
Workforce AI is the tools employees use: seats and per-user token draw across GitHub Copilot, Cursor, ChatGPT Enterprise and Microsoft 365 Copilot. Application AI is the AI your product ships: tokens, compute and data joined into cost-to-serve across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, SageMaker and Databricks.
PLAN. Price a workload from its architecture before any resource exists, across model APIs, GPU capacity, data platform consumption and storage, with assumptions visible. Compare it across candidate models on your measured usage.
EXPLAIN. Attribute spend to owner, team, application, line of business and business unit across 9+ hierarchy levels. Unified tagging reconciles providers that tag inconsistently, and AI-driven bulk labeling closes large key estates. Unattributed spend is reported in dollars.
ACT. Budgets per project, team and API key, with daily burn-rate monitoring. Anomaly detection with root cause, routed to the owner. Waste detection using FinOpsly's own algorithms and ML models. Commitment planning across AWS, Azure and Google Cloud. Policy-driven parking of idle compute.
PROVE. Chargeback across AI, cloud, data and SaaS in one cycle. Realized savings tracked into run-rate against a no-action baseline. Cost per call, cost per active user, and cost-to-serve per customer and tenant.
proof: 100% attribution of AI spend; chargeback from 12.4 days to under one day across 9+ levels; 26% realized savings in AWS and 17%+ in Azure at a payments client.
Built for CIOs, CTOs and platform leaders accountable for technology spend, FinOps and finance teams running chargeback, and engineering teams who need cost signal before they decide
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Replex
Configure policies to manage and govern cloud-native environments without impacting agility or speed. Allocate budgets to individual teams or projects, keep track of costs, govern resource usage and generate real-time alerts for cost overruns. Track the complete asset life cycle from ownership and creation to modification and termination. Understand detailed resource consumption patterns and costs associated with decentralized development teams while engaging developers in creating value with each and every deployment. Ensure microservices, containers, pods, and Kubernetes clusters have the most efficient resource footprint possible without compromising reliability, availability, or performance. Replex allows you to right size Kubernetes nodes and cloud instances based on historical and real-time utilization data and is a single source of truth for all performance-critical metrics.
Learn more about Replex