Why Odine MACE runs on, and can help manage, Red Hat OpenShift and Red Hat OpenShift AI, and what that means for your team
When cloud-native scale becomes an operational challenge
As cloud environments expand, operations teams are required to manage an increasing number of clusters, services, workloads, and data flows. Yet the insight needed to operate these environments often remains distributed across separate monitoring, orchestration, ticketing, and cost-management systems. The result, observed across our combined customer base: silos, alert fatigue, slower decision-making, and operational drift that doesn’t break the system on Tuesday but does on Friday at 3 am.
Addressing this challenge requires more than expanding the existing operational toolset. Organizations need an intelligent management layer capable of interpreting signals across the environment, identifying where action is required, and coordinating the appropriate response, while preserving visibility, governance, human oversight, and sovereignty over their data, infrastructure, and operational decisions. Odine MACE is built to close that gap. It runs on Red Hat OpenShift, with Red Hat OpenShift AI providing a supported foundation for its AI capabilities, allowing Odine MACE to operate within the same trusted, governed environment as your workloads.
As the following sections explore, Odine MACE natively orchestrates OpenShift clusters as well.
A management platform that lives where your workloads do
Odine MACE is an agentic multi-cloud management platform that enables organizations to take control of their cloud, AI, and digital infrastructure environments through a unified, sovereign-by-design approach. Six specialized modules; Sense, Know, Command, Flow, Automate, Spend, operate as one continuous loop: observe → understand → decide → act → measure. Each module hands off cleanly to the next, so routine operations close themselves without a human in every link.
Critically, those agents don’t run on their own special infrastructure. They run as pods on Red Hat OpenShift, in the same cluster as the customer’s workloads, governed by the same role-based access control (RBAC), isolated by the same project boundaries, audited through the same trail. A management plane that lives outside the cluster it operates on is itself a source of risk; separate identity, separate audit, separate failure modes. Running inside Red Hat OpenShift means there is one trust boundary, one access model, and one disaster-recovery story.
The AI/ML capabilities behind Know, Command and Spend run on Red Hat OpenShift AI, the same supported, secured AI/ML platform Red Hat customers already use for their own workloads. No parallel platform. No bespoke machine learning and operations (MLOps) stack. No second governance model.
What this means for your team
When the foundation is right, the customer outcomes follow:
- Multi-cloud provisioning without the complexity: New environments in minutes, not days. Workloads land in the right cloud and the right region, at the right cost, from a self-service catalog with no tickets and no manual handoffs. One workflow spans bare metal, virtualization, Kubernetes, and cloud, so teams stop stitching tools together and senior engineers are freed from the queue for higher-value work.
- Catch problems and take action before incidents happen: The intelligence layer ingests telemetry across the estate and collapses alarm storms into real incidents, with root cause and blast radius shown instantly. Known issues are remediated automatically by agents, with a human approving anything that touches production. Problems are caught and acted on early, before customers ever notice.
- Cost saving and return on investment (ROI) control with FinOps and tool consolidation: One platform replaces a stack of single-purpose tools, and the licenses and overhead that came with them. Cloud spend is attributed, forecast, and controlled; idle resources are reclaimed and over-provisioned ones right-sized at the moment of spend, not in a quarterly review. Cost becomes part of how the estate is run, not a report read after the money is gone.
- Every change checked against your rules before it goes live: Residency, placement, and compliance rules are applied at provisioning and assessed before a change goes live, across every environment. A change that would breach policy is caught while it is still a proposal, not discovered in the next audit. One unified policy and audit trail makes the estate sovereign by rule and compliant by proof.
- Your know-how stays in the system: Every resolved incident, every runbook, and every vendor document become instantly searchable and actionable. The hard-won knowledge of senior engineers is captured where the work happens, so it stays in the system when people move on, and new team members ramp faster.
- Live in days, designed for 99.999% uptime: Odine MACE deploys on your own infrastructure, on-premises, private cloud, or sovereign environments, and is operational in days, not quarters. It is engineered for 99.999% availability and is vendor-neutral across hypervisor, cloud, and hardware, with pre-built integrations across common stacks and multi-tenant isolation built in.
How Red Hat OpenShift AI Supports Odine MACE’s Agentic AI Roadmap
This is the part of the story we, Odine, want to spend a moment on, because we think it matters to other independent software vendors (ISVs) and enterprise teams who are weighing where to host their AI workloads.
Building a multi-agent management platform requires substantial artificial intelligence/machine learning (AI/ML) infrastructure: embeddings and a vector store for Know‘s document Q&A, a large language model (LLM)-serving path for Command‘s natural-language interface, forecasting and anomaly detection for Spend‘s FinOps signals, and the graphical processing unit (GPU) capacity to support these workloads at scale. For this AI subsystem, Odine selected Red Hat OpenShift AI as the underlying platform instead of developing and maintaining each infrastructure component independently. This provided a consistent foundation for deploying, operation and scaling the AI/ML workloads across Odine MACE. In practical terms, the following areas become simpler:
- Day-one data scientist productivity. Workbenches provide data scientists a ready-made Jupyter environments, eliminating laptop provisioning, compute unified device architecture (CUDA) drift, and “works on my machine” inconsistencies. Standardized images reduced the model-to-cluster cycle from weeks to hours.
- GPU capacity on demand. Accelerator profiles and the NVIDIA GPU Operator allow GPUs to be assigned to training and serving workloads on demand. This replaces speculative GPU procurement with a flexible model in which capacity is attached when needed, both for Odine and its customers.
- Distributed training without additional orchestration complexity. For larger embedding and language-model runs, Distributed Workloads including Ray, PyTorchJob, CodeFlare and Kueue, support cluster-scale training without introducing a parallel orchestration stack.
- Production-ready model serving. KServe provides a path to model serving with auto-scaling, canary rollouts and traffic splitting, reducing the need to develop and maintain these capabilities independently. Explainability built into the roadmap. Regulated organizations need to understand and demonstrate how model-driven decision are made. TrustyAI, supports this requirement within the same platform and audit trail, without the need for a bespoke explainability layer.
- Disconnected and air-gapped, out of the box. Sovereign-cloud and regulated organizations require the same AI capabilities, as everyone else, to remain deployable in disconnected environments. Red Hat OpenShift AI supports disconnected deployment as a standard architectural scenario, rather than requiring a separately adapted version.
Together, these capabilities strengthen the foundation behind Odine MACE’s AI roadmap and translate directly into customer-visible outcomes. They support faster delivery of new AI features, lower infrastructure complexity, and a consistent operational experience across connected, disconnected, and air-gapped environments. For regulated organizations, they also provide a clearer path to meeting governance, auditability and compliance requirements.
And: built to manage Red Hat OpenShift
Now the symmetric half of the story. Odine MACE doesn’t only run on Red Hat OpenShift, it supports orchestrating Red Hat OpenShift clusters end to end.
For a platform team running a Red Hat OpenShift estate, Odine MACE applies the same six-module loop to your clusters. Sense correlates events across the control plane and data plane. Know answers your operators’ questions from cluster runbooks and Red Hat documentation. Command translates plain-language intent – “drain node X if the SLA holds” – into the right Kubernetes action. Flow gates that action against your policy rules. Automate executes through the Red Hat OpenShift API and Operators. Spend ties each workload’s cost back to the service and the customer it serves.
The result: what has historically required several separate tools, and several separate dashboards, is collapsed into one continuous loop, running on the cluster your team is already running.
Where this is running today
Odine MACE is in production at Tier-1 telecom operators where 5×9s availability is the floor, across estates that include Red Hat OpenShift alongside kernel-based virtual machine (KVM), Red Hat OpenStack Services on OpenShift and VMware. The autonomous Day-2 loop runs daily, the policy gates carry the audit weight, and the platform pulls its weight as both a management tool and a tenant on the same Red Hat OpenShift cluster.
The early lessons match what customers had hoped for: keeping the management plane inside the trust boundary materially reduces incident-resolution time, and the multi-agent architecture lets operators delegate routine work without delegating accountability.
What’s next: together
Odine and Red Hat are working together on:
- Sovereign-cloud reference architectures: disconnected, air-gapped Odine MACE deployments on Red Hat OpenShift for regulated and public-sector customers.
- Agentic operations patterns on OpenShift AI: joint blueprints for retrieval-augmented generation, model serving with KServe, and TrustyAI-backed explainability inside the operations loop.
- Field motion: joint go-to-market where Odine MACE and the broader Red Hat stack (OpenShift, OpenShift AI, Ansible Automation Platform) ship together for the customer.
If you operate a Red Hat OpenShift estate at meaningful scale and have been waiting for the management story to match the platform story, get in touch with us at info@odine.com.
