As AI workloads move from experimentation into production, platform engineering teams are being asked to support a fundamentally different operating model.
The shift is not only technical. It is forcing CIOs and platform leaders to rethink how infrastructure, data services and developer experience are owned, funded and governed across the enterprise.
At the infrastructure layer, the shift is driven by hardware and networking requirements that differ sharply from traditional application platforms.
Donnie Page, solutions engineer at Itential, explains that the fundamental shift is moving from an application-centric (CPU-based) to an AI-native (accelerated) architecture.
“This requires a platform capable of managing heterogeneous compute—specifically GPU orchestration and fractional sharing, for example, NVIDIA MIG,” he says.
NVIDIA’s Multi-Instance GPU (MIG) capability allows a single physical GPU to be subdivided so multiple models or pipelines can share the same device without contention—an essential control point for cost and capacity.
He adds that AI platforms must also address training and data-movement bottlenecks.
“Beyond compute, the architecture must incorporate low-latency fabric networking–like RDMA–to support distributed training and high-throughput data layers that can feed models at scale without bottlenecks,” Page says.
Remote Direct Memory Access (RDMA) enables nodes to exchange data directly without CPU involvement, which is critical for multi-GPU and multi-node training jobs that depend on fast synchronization.
For platform engineers, that means introducing schedulers and orchestration layers that can carve GPUs into smaller, isolated partitions and allocate them across multiple workloads.
AI Training, Inference Pipelines
Compute is only part of the problem. AI training and inference pipelines place new demands on networking and data movement.
At the platform interface, abstraction becomes a primary design requirement.
“One aspect is the necessity to abstract infrastructure for developers,” says Pavlo Baron, CEO of Platform Engineering Labs.
He explains that platform teams operate on a low level of detail and provide reusable infrastructure services with clear boundaries and minimum, high-level configuration, such as geography, load profile, t-shirt size.
Instead of exposing raw infrastructure primitives, platform teams are expected to provide opinionated service interfaces—standardized configuration choices and predefined capacity profiles.
This allows users to request resources without understanding the underlying complexity of accelerators, network fabrics, and data layers.
That abstraction challenge becomes more difficult because the user base is expanding beyond application developers.
“Platform teams must transition from being ‘infrastructure providers’ to ‘platform product managers,’” Page says. “The customer base has expanded from software engineers to include data scientists and business domain experts.”
This change forces platform organizations to rethink ownership boundaries, where ownership ends not at the VM or container, but at the inference runtime.
“We must provide ‘Inference-as-a-Service’—abstracting away the complexities of GPU drivers, model versioning, and environment setup into a seamless, self-service ‘Golden Path’ for non-infrastructure personas,” Page explains.
In practical terms, this means that platform teams now own the operational layer that exposes models to applications.
That includes standardized serving environments, curated base images, GPU drivers, and deployment patterns that allow data scientists and business teams to deploy models without building bespoke infrastructure pipelines.
Platform-as-a-Product Mindset
This expansion of responsibility also requires organizational realignment, as successful AI-native platforms require a “Platform-as-a-Product” mindset.
“This means embedding forward-deployed engineers (FDEs) within AI units to co-create initial patterns before they are centralized,” Page says.
Forward-deployed engineers operate directly with AI and data science teams to shape reference architectures and workflows before those patterns are formalized into shared platform services.
“We also need dedicated product management to translate data science needs into infrastructure roadmap items,” he says.
Governance and cost controls must also evolve to keep pace with scale, and to handle the increased scale, manual gatekeeping should be replaced with Policy-as-Code, automating the ‘Guardrails’ for FinOps and security, so governance doesn’t become a bottleneck to innovation.
Policy-as-code allows usage limits, security controls, and cost policies to be enforced automatically through configuration and APIs rather than approval workflows—a critical requirement when GPU resources and model deployments can be created and destroyed rapidly.
Shifting Success Metrics
The success metrics for platform engineering are also shifting as AI workloads introduce new cost and performance characteristics.
“Success criteria must evolve beyond standard ‘Golden Signals’ to include AI-specific unit economics and quality metrics,” Page says.
Rather than building entirely separate AI platforms, Page argues for extending existing internal platforms with modular AI capabilities.
“This is a trade-off between operational simplicity and functional depth,” he says. “Building a separate ‘AI Silo’ often leads to tool sprawl and fragmented expertise.”
Instead, a more mature approach is a modular platform strategy, which integrates specialized AI capabilities (like Vector DBs or GPU Clusters) as plug-and-play modules within the existing platform.
“This allows the organization to leverage its existing security, auth, and automation investments while providing the high-performance ‘paved roads’ required for AI workloads,” Page says.