Building Standardized Service Catalogs for Infrastructure and AI

Enterprises can create standardized service catalogs by turning repeatable infrastructure and AI capabilities into approved service definitions with known parameters, ownership, quota, approval, delivery workflow, metering, and lifecycle. The source material does not define one formal page called a service catalog, but it provides the building blocks required to create one.

The strongest source-supported approach is to catalog what users are already allowed to request: standard compute specifications, bare-metal delivery, storage and connectivity services, model deployment templates, inference services, API access, quota changes, and other repeatable workflows.

What is a service catalog in this context?

A service catalog is a controlled list of services users can request or consume. For infrastructure, a catalog item can represent a compute resource, a bare-metal server, a storage allocation, a dedicated network service, or a quota change. For AI services, it can represent an approved model deployment, an inference instance, a model API, a knowledge-base service, or an agent service.

The catalog item hides unnecessary implementation details. The user chooses the service and approved parameters, and the platform applies governance and delivery logic. The source supports this pattern through standardized resource specifications, templates, workflows, service publication, and metering.

Why standardize infrastructure requests?

Standardization reduces one-off manual work. Without a service catalog, users often request infrastructure in free text: "Please give me a powerful GPU server."

That creates several follow-up questions. Which accelerator, and how many? How much memory, and for how long? Which project and which quota? Which operating system and which network?

A standard catalog item turns that conversation into defined fields. The source scheduling model already uses standard resource specifications for heterogeneous GPU and NPU capacity, which is the right foundation.

What should a compute catalog item contain?

A compute item can define the resource capability rather than one physical machine. Source-supported parameters include:

• Accelerator type
• Accelerator count
• CPU
• Memory
• Whole-card or sliced form
• Tenant
• Project
• Quota
• Priority

The source does not define a universal catalog schema. A practical catalog entry should also identify the service owner, approval policy, usage-metering rule, and return or expiry behavior.

The user should understand what they receive and how it is governed, and the infrastructure team should understand how the request maps to the resource pool.

How should heterogeneous accelerators appear in the catalog?

Present approved resource specifications while retaining actual vendor and model compatibility underneath. The source platform supports heterogeneous accelerator pooling, so the catalog can avoid forcing every user to understand each hardware vendor. It still has to show the capability differences that matter to the workload.

A standard service can specify the resource class, memory level, whole or sliced resource, and supported workload type. The scheduler can then map the request to compatible healthy hardware.

For the underlying resource management, how can enterprises manage GPUs and NPUs from multiple vendors in one platform explains why standardization and hardware detail must coexist.

What should a bare-metal catalog item contain?

The source bare-metal delivery workflow provides a clear standardized service. A catalog item can define the approved hardware class, operating system, driver set, Monitoring Agent, cluster role, and network profile.

The request enters the workflow, the platform validates the physical server, provisioning runs, post-install health is verified, and the result is written back. That turns "build me a server" into a repeatable delivery service.

For the delivery details, how automated bare metal provisioning works for physical servers, operating systems, GPU drivers, and monitoring agents explains the full flow.

How should storage appear in the catalog?

Storage services can be standardized by the parameters the platform can govern. The source materials include storage management, capacity, performance, quotas, and usage, so a storage catalog item could define the storage service, capacity, tenant or project, quota, performance requirement where supported, and lifecycle.

The source does not specify one formal storage catalog interface. The source-supported principle is to convert repeatable allocation rules into standard service definitions, and the same applies to other infrastructure resources.

How should network services appear?

The source platform manages dedicated circuits and network topology. A network catalog can standardize requests such as a private inter-site connection, a bandwidth change, network access, or an approved connectivity profile. The exact service depends on the enterprise network operating model.

The catalog should connect the request with its owner, circuit or network object, approval, cost, delivery workflow, and monitoring. That keeps the commercial and operational record aligned.

How should model deployment appear in the catalog?

The source MaaS layer already uses deployment templates, which makes model deployment a strong catalog candidate. A catalog item can include:

• Approved model
• Approved model version
• Deployment template
• Resource specification
• Project
• Replica or scale policy
• Service tier

The user chooses the approved combination, the platform creates inference instances, and the service gateway publishes the endpoint. That works as a catalog-style service even though the source does not call it a service catalog.

How should model APIs appear?

The API gateway provides a stable service boundary. A catalog entry for a model API can define:

• Model service
• Tenant eligibility
• Project
• API key policy
• Quota
• Rate limit
• Fallback policy
• Metering definition

The source model-service layer includes key management, rate limits, quotas, routing, fallback, and Token metering. Those operating controls are what make an API service catalog useful. The catalog entry should describe the governed service and keep the internal inference topology out of view.

How should AI agent services appear?

The source agent-orchestration layer lets an orchestration be published as a service, so an approved agent can also become a catalog item. A user can discover the agent's purpose, version, business domain, access scope, and service status.

The source also tracks call volume, latency, success rate, and failed samples. Those operational measures help determine whether the catalog item is still healthy and worth keeping published.

How should quotas be attached to catalog items?

Quota should be part of the entitlement policy. The source platform supports quota at tenant and project level. For a compute item, quota can limit how much resource the project can request, and for a model-service item, quota can limit service use according to the supported gateway policy.

A catalog should therefore answer who is eligible, how much they can consume, and what happens when the limit is reached. The user should not need to learn the quota only after the request fails.

How should approvals be attached?

Approval should depend on cost, risk, and ownership. The source workflow engine supports dynamic and multi-level approval, so different catalog items can have different policies. For example, a routine small compute request inside quota can follow a lightweight path, while a large capacity expansion requires manager approval. Sensitive access requires security approval, and a production infrastructure change requires operations approval.

The source does not prescribe these exact policies. It provides the workflow mechanism, and the enterprise should define the approval logic for each service item.

How should pricing or cost context be included?

The source platform tracks card hours, Token usage, energy, project cost, and tenant cost, so a catalog item can be connected to an approved metering definition.

The source does not require every request to show an upfront price, though it does support traceable consumption after delivery. For mature internal services, historical unit cost can also help owners understand the impact of choosing one resource specification over another.

Whatever you show, use the same cost definition as the formal operations and billing pages.

How should service ownership be defined?

Every catalog item should have an operational owner. Possible owners include the infrastructure team, network team, storage team, AI platform team, model-service owner, or security team.

The source data foundation includes people, services, assets, and activities in one relationship model. That lets the catalog item connect to the team responsible for delivery and support, so if the service fails, the incident already knows the responsible owner.

How should the lifecycle of a catalog item work?

A service definition should have its own lifecycle, moving through draft, approved, published, updated, deprecated, and retired states.

The source does not define those exact catalog states. However, it does support model versions, deployment templates, workflow versions, asset lifecycle, and service publication. The source-grounded inference is that standardized services need controlled versioning and retirement so old definitions do not remain available indefinitely. Any lifecycle states should be implemented according to the enterprise process.

How should catalog items connect to automation?

The catalog item should point to the approved workflow or template that delivers it, and this is where the catalog becomes more than documentation. The source workflow engine can trigger resource scheduling, automation scripts, bare-metal delivery, and quota changes. The MaaS layer can trigger model deployment and service publication.

In that arrangement, the catalog item is the front door and the workflow is the delivery engine, while the scheduler and automation systems remain the execution layer.

How should the catalog connect to metering?

Every delivered service should create the same usage records used by the operations platform. A compute service creates card-hour usage, a model API creates Token usage, a storage service creates storage usage, and a dedicated circuit creates service and cost records.

The catalog therefore needs no new billing vocabulary. It reuses the same project and tenant ownership model already used by metering.

For ownership and cost rollup, how enterprises can track infrastructure costs back to departments, projects, applications, or customers explains how those relationships should be preserved.

What should the first catalog contain?

Start with repeatable, high-volume, well-governed services. A source-grounded first catalog could include:

• Standard GPU resource request
• Bare-metal AI node delivery
• Storage allocation
• Project quota change
• Approved model deployment
• Inference API service
• Agent service

That is enough to prove the model. Do not start by cataloging every infrastructure action.

A platform example that provides the specifications, templates, workflows, MaaS services, quotas, and metering needed to build this kind of catalog is Sensaka.

If I were creating the first service catalog, I would select the requests that already arrive repeatedly through tickets and standardize those first. Each item should have approved parameters, an owner, an approval rule, an execution workflow, and a metering definition. If any of those is missing, the item is still just documentation.