Enterprise AI on Your Terms: Governed Access and Control at Scale

Enterprises racing to adopt AI often create more risk than value. Costs spike as token usage grows. Security and compliance teams cannot see which models run where. Data paths are unclear. Teams wire their own SDKs and API keys, which fragments the platform. Audits arrive only after the damage is done.

To mitigate these risks, AI needs the same treatment as every other capability in the internal developer platform (IDP). Provide it through the platform, complete with budgets, policies, and observability from day one. That is how adoption scales without chaos.

Treat AI like any other governed service

The advice for building effective platform services applies equally to AI-based services: define the service contract first; provide a stable API and usage policy; bind budgets, allowlists, and audit; route by region to meet data residency; and observe cost and quality by team and environment.

This pattern allows you to move quickly without losing control. A global retailer can set a monthly spend per business unit. A bank can route EU workloads to EU-approved providers. A pharma team can add a private model and keep the same interface.

To deliver governed capabilities, you need an orchestration layer that sits between developer entry points (such as portals, APIs, and GitOps) and the underlying infrastructure. That’s the role of Kratix, a Platform Orchestrator. 

As the diagram below shows, Kratix sits in the middle, positioned above clouds, clusters, and automation tools such as Terraform, and below portals like Backstage. Platform teams and specialists contribute Promises, which are packaged capabilities like databases, CI pipelines, or AI gateways, to the Platform. Kratix publishes these as services through a consistent contract. Each Promise runs through workflow stages that enforce policy, upgrades, and business rules before reaching its destination. The result is a platform where new capabilities can be added and governed without breaking the developer experience.

Example: packaging “AI Capability” with Kratix

With Kratix in place, AI becomes just another governed service delivered through a Promise. The “AI Capability” Promise can expose:

• A consistent API contract for text generation and embeddings.

• Central budgets, quotas, and attribution.

• Policy bundles: model allowlists, region routing, and PII handling.

• Provider maps that you can swap without changing clients.

• Optional UI for experiments, under the same controls.

Implementations sit behind the contract. For example, use an LLM gateway such as LiteLLM for provider fan-out. Keep it replaceable. If pricing, latency, or quality changes, update the mapping and maintain a stable endpoint. Finance and compliance keep visibility. Developers keep shipping.

Syntasso has released an example of this approach: the AI Promise. It provides teams with self-service access to your company’s LLMs via LiteLLM, utilising OpenWebUI as the interface. This setup allows developers to use either an API or a UI, while the platform team controls access, model choices, and budgets. You can swap models, adjust rate limits, and update policies without breaking clients, as shown by the platform architecture diagram below:

Let’s now look at the AI capability in action.

I’m using the commercially supported version of Kratix, Syntasso Kratix Enterprise (SKE), to provide a Backstage portal for the platform. The catalog shows the available services:

Let's go ahead and make a request for an AI instance:

You can see that a variety of options are available. The power of Kratix lies in the platform team defining the API and setting the boundaries that make sense for their organisation. 

Here, I am simply exposing what the models they want to give access to. In another scenario, I might have written an extensive API that exposes more fields, such as custom rate limits and budgets.

Alternatively, I might have simplified it even more by not exposing the models that can be chosen.

Once the request has been made, SKE runs a workflow to manage provisioning the resource, in this case, configure LiteLLM to create a new team with sensible defaults such as budgets, and creates an OpenWebUI instance.

The result is that the developer has access to everything they need to access the model from a single location, the platform catalog. There is no need to jump between tools, keys, or systems. The request, provisioning, policies, and access details are all managed through a single, consistent entry point. The platform becomes the one-stop shop for consuming AI, just as it does for any other governed service.

The user can now interact with the API directly or use the OpenWebUI interface, which is deployed alongside it.

The real magic is that the service is preconfigured to match the organisation’s needs, like budgets, rate limits, compliance policies, and more. Developers always consume an org-approved service, without needing to think about the guardrails.

What you saw is the contract in action: developers interact through a single endpoint and UI, while the platform enforces model allowlists, region routing, and budgets in the background.

The result is enterprise-grade control, predictable costs, audit-ready compliance, and the flexibility to adapt providers without business disruption. This allows the organisation to scale AI adoption with confidence, not chaos. 

Want to experiment with the AI Promise? Start here.

AI on your terms

Enterprises succeed with AI when it is delivered as a governed service, not a collection of ad-hoc tools. A single contract with budgets, policies, and observability provides developers with consistency and gives the business control. This avoids lock-in, enables provider flexibility, and ensures compliance from day one.

Syntasso Kratix Enterprise (SKE) provides a direct path: compose your gateway, UI, and policy into one governed capability. The AI Promise maintains a consistent interface while you adapt models, costs, and rules. The result is scale, auditability, and confidence in the adoption of enterprise AI.

Offer AI on your organisation’s terms. Standardise the contract, centralise control, and evolve behind a steady endpoint. Start small, measure outcomes, and expand safely.

Learn how SKE helps teams orchestrate governed AI adoption at scale: www.syntasso.io