For platform, security, and FinOps teams

Phantom vs Kubernetes with OPA

Q: Do we have to replace the AI tools and agent frameworks we already use?

No. PhantomAgent sits alongside existing tools and agent stacks. Applications connect through the control plane so teams can apply consistent policy and visibility without rip-and-replace.

Q: Where does PhantomAgent run?

On infrastructure your organization controls, including major public clouds, Kubernetes, and on-prem environments. Deployment details depend on your security and networking requirements.

Q: Can we keep sensitive models and data in our own environments?

Yes. Self-hosted and internally operated models are a core use case. PhantomAgent applies the same policy, visibility, and audit expectations to internal models as to external APIs.

Q: What can we actually evaluate in a demo trial?

Trials focus on practical questions in your environments: policy enforcement, usage visibility by team or project, routing around unhealthy infrastructure, and audit trails. Scope is tailored to your evaluation goals.

Q: What does enterprise licensing include?

Production deployment in your environments, commercial terms aligned to your scale, and support for operating the control plane. Pricing and packaging depend on scope and rollout plan.

Compare an enterprise AI control plane with building and operating your own governance layer on Kubernetes and Open Policy Agent.

Compare the governance work you need to do

Kubernetes and OPA are useful building blocks for a team that wants to own its policy implementation. OPA can enforce admission policies on Kubernetes objects; Gatekeeper adds constraints and audit functionality. Phantom positions policy, workload placement, chargeback, failover, and audit as one control plane on infrastructure you control. The choice depends on which integration and operating work your team wants to own.

Source: Open Policy Agent Kubernetes documentation. Reviewed October 7, 2026. This comparison describes published positioning, not a hands-on benchmark.

Start with placement, policy, and cost ownership

Phantom governs where models, agents, and tools run on infrastructure your organization controls. Apply centralized policy across agents, attribute costs to teams, projects, and workloads, and export an audit trail for security reviews.

You keep your existing AI tools and agent frameworks. The control plane sits alongside them. Public cloud, Kubernetes, on-prem, and customer-owned environments are part of the deployment conversation; Phantom does not broker GPUs or resell inference.

Use your own workloads to evaluate fit

Bring one workload, its allowed environments, the team responsible for spend, and the evidence security needs. In the trial, evaluate placement and access policy, usage attribution, routing around unhealthy infrastructure, and audit exports. Confirm identity and logging integrations and the rollout scope before committing to a license.

FAQ

Frequently asked questions

Answers for platform, security, and infrastructure teams evaluating a governed AI control plane.

Do we have to replace the AI tools and agent frameworks we already use?

Where does PhantomAgent run?

Can we keep sensitive models and data in our own environments?

What can we actually evaluate in a demo trial?

What does enterprise licensing include?

Deployment: who operates the governance layer?

With a Kubernetes and OPA build, your platform team selects, connects, and maintains the components. Phantom also runs on infrastructure your organization controls. Compare installation, upgrades, incident response, and support responsibilities before choosing either approach.

Placement: what decision are you enforcing?

Kubernetes supports node selection and affinity rules for Pod placement. OPA can validate workload configuration during admission. For either approach, test how your rule covers an AI request that crosses a model API, an agent, and a tool outside the cluster. Phantom’s stated scope includes placement across cloud, Kubernetes, and private environments; verify it against your actual execution path.

Kubernetes documentation: assigning Pods to nodes

Chargeback: can FinOps trace a cost to its owner?

For a build, map the usage and billing sources you will join to teams, projects, and workloads. Set rules for shared costs and missing ownership. For Phantom, use the trial to check that the reported attribution matches those same rules. Compare the work needed to keep that mapping accurate; do not assume either approach resolves missing source data.

Audit: can security reconstruct one execution?

Gatekeeper includes audit functionality, so a DIY build does not start without policy evidence. Check whether your assembled records connect the policy decision, workload placement, identity, execution, and cost. Ask Phantom to produce the same evidence for the same workload, including an export your security team can review.

This is an evaluation checklist, not a performance benchmark. Connector coverage, retention, support terms, and pricing need confirmation for your deployment.