By Frank La Vigne
A control plane for enterprise AI is the layer between the people and agents using AI and the infrastructure that runs it, meaning your models, GPUs, clusters, and cloud accounts. It decides who can use what, sends each request to the right resource, and records what that request cost.
Klique is the control plane for enterprise AI. Teams get self-service access to approved tools, models, and GPUs, and IT sets the policies, quotas, and budgets behind them. This post covers who uses Klique, how it fits into the stack you already run, and what happens to a request on its way through.
Who uses Klique?
Klique serves four kinds of users: AI Consumers, AI Builders, AI Operators, and AI agents, and each one needs something different from the platform.
- AI Consumers are analysts, marketers, support teams, and anyone else using AI tools in their daily work. They pick from a catalog of AI tools that IT has vetted and approved, so they can start working without filing a ticket.
- AI Builders do the hands-on work, from building agentic applications to data ingestion and model fine-tuning, and they need to ship without waiting on DevOps.
- AI Operators are the IT admins and platform owners who allocate resources securely, track AI usage, and manage running costs. They set the rules once, and those rules apply to every team.
- Agents are the newest type of user. When a builder ships an agent, Klique gives the agent its own identity, quota, and audit trail, so IT can see and cap what it spends just as it would for a person.
How does Klique fit into your existing stack?
Klique runs on the infrastructure you already have. It connects to Kubernetes clusters, cloud accounts, and on-premises systems and manages them from a single control plane, so you don’t have to migrate workloads or replace tools your teams depend on.
The AI Engine is where teams build, deploy, and use models and AI applications. The Orchestration Engine runs those workloads on governed compute, and the Governance Engine controls who can use what through unified security, policy, and identity.
Governance Engine: Identity and Policy
The Governance Engine is Klique’s identity and policy layer for AI governance. Each person and agent gets a scoped identity tied to the organization’s existing SSO provider, such as Okta or Active Directory. Each one also gets its own key, and no one shares credentials.
Access rules, quotas, and budgets attach to that identity and to the project it’s working on. That means a budget follows the work even when people move between teams. Token management and spend tracking show what AI costs and what it returns, by team, project, and request, down to the individual user or agent.
Budgets and rate limits cap usage as each request comes in, and when a team hits its budget, IT can move it to a cheaper model so the work keeps going.
AI Engine: Build, Deploy, and Consume AI in One Place
The AI Engine is where models and AI applications are built, deployed, and consumed. Builders take a model from development to production there and publish it so other teams across the organization can use it. Every model sits behind the same gateway, including models hosted in-house, open-source models from Hugging Face and others, and third-party APIs like OpenAI and Anthropic.
Klique routes each request to the right model based on the policies IT sets for cost, performance, and data sensitivity. For example, a contract review might go to a frontier reasoning model while a simple classification job goes to a small one. Users never see this routing, and when IT switches a workload to a different model, it’s a policy change that doesn’t require rewriting the application.
Orchestration Engine: GPUs Across Mixed Hardware
The Orchestration Engine is Klique’s GPU orchestration layer. It schedules AI workloads across GPUs of different types and generations, in the cloud and on-premises.
Fractional GPU allocation is the practice of splitting one GPU between several workloads. A small inference job uses only the share it needs, and a training run can use the rest of the card.
How Does Klique Track AI Spend?
Klique logs every request through the gateway and every GPU job with the user or agent, team, project, and model behind it. Showback (reporting costs to each team) and chargeback (billing those costs back to them) roll that data up by team, project, and budget. That gives finance and IT the same AI FinOps view they already have for cloud spend.
Klique also tracks token spend on external APIs and infrastructure spend on self-hosted models together. You can compare what a workload costs on a third-party model against what it costs on your own GPUs.
Klique’s own pricing is per GPU or per user, so the platform’s cost stays predictable as token usage grows.
What Does This Look Like Day to Day?
An analyst opens the Klique catalog, picks an approved AI assistant, and starts working under her own identity. A data scientist starts a notebook on a fraction of a GPU, fine-tunes a model, and deploys it behind the same gateway her colleagues already use. An agent she built runs overnight against its own budget, and it switches to a cheaper model if it gets close to its cap. In the morning, the platform team opens one dashboard and sees usage and spend for all three, broken down by project.
Why Does a Control Plane Matter for AI ROI?
For AI to generate real ROI, it must be tightly integrated into enterprise systems without the friction of complex handoffs. Klique’s multi-tenant platform facilitates this integration, delivering secure, governed environments where AI innovations can be developed, deployed, and scaled in-house.
For enterprises and service providers, built-in billing and chargeback map spend to teams, projects, and budgets. That transparency keeps teams on budget and ties AI investment to business outcomes.
Shadow AI is the use of AI tools that IT hasn’t approved or can’t see. As the control plane for enterprise AI, Klique gives teams a governed path from shadow AI to sanctioned, efficient, and secure AI at speed.
To learn more about how Klique can change the way you deliver AI, schedule a demo or explore the platform through the following links:
FAQ
What is a control plane for enterprise AI? A control plane for enterprise AI is the layer that decides who can use which tools, models, and GPUs, and it records what each request costs. IT sets the policies there once, and they apply to every team.
What is Klique? Klique is the control plane for enterprise AI. Teams use it to get self-service access to tools, models, and GPUs, and IT uses it to set the access rules and budgets behind them.
Does Klique work with existing infrastructure? Yes. Klique connects to Kubernetes, cloud accounts, and on-premises systems without requiring you to migrate what you already run.
How is Klique different from a standard LLM gateway? A standard LLM gateway routes model traffic. Klique routes model traffic too, and it also schedules the GPUs your self-hosted models run on, so IT manages access, budgets, and chargeback for both in the same place.
What is fractional GPU allocation? Fractional GPU allocation splits a single GPU across several workloads, so each job uses only the share of the card it needs. Klique assigns these fractions dynamically across mixed hardware.
How does Klique help control AI costs? Klique routes each request to the model that fits IT’s policies for cost, performance, and data sensitivity, and it splits GPUs between workloads. It also enforces budgets as each request comes in, so a team that reaches its cap gets moved to a cheaper model or paused before it overspends.
