AI does not replace platform engineering. It expands the platform’s job from delivery pipelines to model access, data context, evaluation and agent governance.
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The AI platform service catalogue
Common services should remove repeated setup without taking product ownership away from delivery teams.
Make AI capabilities services, not exceptions
A useful platform exposes a small set of repeatable AI services: model access, data context, agent runtime, evaluation, serving, observability and policy. Teams consume a supported path instead of building a private stack around each pilot.
The catalogue gives each capability an owner, a service level and a default control. It also tells teams where the platform ends and their product responsibility starts.
Make AI capabilities services, not exceptions
A useful platform exposes a small set of repeatable AI services: model access, data context, agent runtime, evaluation, serving, observability and policy. Teams consume a supported path instead of building a private stack around each pilot.
The catalogue gives each capability an owner, a service level and a default control. It also tells teams where the platform ends and their product responsibility starts.
The control plane is more than an API gateway
A model gateway can provide credentials and routing. The AI control plane adds evaluated model changes, tool registration, data permissions, cost attribution and policy checks. It governs the whole runtime path.
A central policy point replaces scattered provider keys, untracked model changes and after-the-fact cost analysis with controls that work before the request runs.
Extend the platform instead of creating a new empire
An AI silo centralises specialist skills but can become a bottleneck. An enablement overlay helps teams start but often leaves shared controls unfinished. A modular extension lets the internal platform own common services while product teams keep delivery ownership.
The practical boundary is undifferentiated AI heavy lifting: policy, cost, shared runtimes and common evaluation. Keep domain prompts, user journeys and business outcomes with product teams.
AI needs an acceptance test at runtime
Code either passes a test or fails it. AI quality is probabilistic, so teams need representative task sets, release thresholds, trace sampling and a rollback path when a model or prompt change moves the result.
A platform that can report latency but not task quality only monitors half the system. Evaluation belongs beside deployment, not in a separate governance presentation.
Platform maturity for AI
The operating model changes from pilot sprawl to a platform product with measurable adoption.
AI platform maturity
A practical path for teams extending an internal developer platform
Pilot sprawl
Teams buy models and build integrations independently
Duplicate risk
Shared credentials
A common gateway without consistent delivery paths
Partial control
Common AI services
Models, data and evaluation are available by default
Repeatable
Paved AI paths
Self-service paths include cost, quality and policy controls
Governed speed
Platform product
The roadmap follows adoption and measurable outcomes
Compounding
Do not centralise domain decisions. Centralise the services that every team would otherwise rebuild with different controls.
Platform decisions
Frequently asked questions
What is AI platform engineering?
AI platform engineering applies platform-product thinking to AI systems. It gives product teams supported paths for models, data context, agents, evaluation, deployment, observability, cost controls and policy enforcement.
How is AI platform engineering different from MLOps?
MLOps focuses on the lifecycle of trained models. AI platform engineering covers the broader production estate: model access, application and agent runtime, tool and data controls, evaluation, cost attribution and developer self-service.
Should every company build a separate AI platform?
No. Most organisations should extend their internal developer platform with modular AI services. A separate platform is justified only when a distinct operating environment, specialised workload or regulatory boundary demands it.
What should an AI platform provide first?
Start with governed model access, cost attribution, a basic evaluation path and traceability. These services stop early pilots from becoming a collection of credentials and unrepeatable integrations.