Just when cloud-native application development seemed settled...

Just when cloud-native application development seemed settled with Kubernetes and micro-services, the rise of production-scale ML and AI completely reshaped the game. Not to worry, if you adopted platform engineering already, you are already one step ahead. If you did not - welp, there's lots to worry about!

Why AI Teams Need Flexible Infrastructure

AI and data science teams need flexible infrastructure to scale and adapt because their workloads are dynamic, resource-intensive, and constantly evolving.

Diverse Workload Requirements

AI and data science workflows involve a mix of:

• Model training (high-performance GPUs, TPUs, distributed computing)
• Inference (low-latency, autoscaling endpoints)
• Data processing (ETL, feature engineering, batch vs. streaming)

Each stage has different infrastructure needs, requiring adaptable compute, storage, and orchestration strategies.

Rapid Experimentation & Model Iteration

• AI teams frequently test new foundational models, architectures, hyperparameters, and datasets.
• A rigid infrastructure limits agility.
• Teams need on-demand provisioning of resources to iterate faster.
• Self-service environments help data scientists experiment without waiting on DevOps, but only if they really are self-service.

Scaling AI for Production

Scalability for Production Deployments

• A small-scale experiment might run on a single machine, but a production deployment requires scaling across multiple nodes, regions, or cloud providers.
• AI-driven applications must autoscale dynamically based on demand to optimize costs and performance.

Multi-Cloud & Hybrid Workloads

• Many AI teams work across on-prem, cloud, and edge environments.
• Some models require proximity to data sources for compliance or performance reasons.
• Cloud-agnostic and portable infrastructure prevents vendor lock-in but is an expensive initial investment if you are relying on the same talent pool to become experts in an exploding combination of technologies and tools.

Cost Optimization & Resource Efficiency

• Right-sizing Resources: Right-sizing resources is critical to avoid excessive spending on idle GPUs or over-provisioned clusters.
• FinOps Integration: FinOps integration ensures spending is tracked and optimized across teams. Nobody likes surprises like these!
• Workload Shifting: Flexibility allows teams to shift workloads to cost-effective providers or spot instances. Sounds easy, right?

Compliance, Security & Governance

Sensitive Data Handling

AI models sometimes need to handle sensitive data (healthcare, finance, PII).

Regional Compliance

Different regions have data sovereignty laws (GDPR, HIPAA).

Access Control

Teams need policy-driven access control and security while ensuring workflows remain efficient. Have you tried hand-crafting an IAM role recently?

Platform Engineering - The Best Way Forward For Everyone

AI Engineers

Want fast, flexible resources to train models.

DevOps Teams

Need standardized infrastructure to ensure reliability.

FinOps Teams

Require visibility and control over cloud spending.

Platform Approach

A flexible platform approach ensures all teams get what they need without unnecessary complexity (in theory). Pragmatic investments in this key area of the engineering org can really accelerate scaling without skyrocketing costs.

The Bottom Line

AI infrastructure must be agile, scalable, and cost-aware while maintaining control, security, and governance. The challenge is enabling this flexibility without creating complexity or operational bottlenecks - which is why platform engineering solutions like StarOps are going to be critical (in practice). True self-service, true flexibility and true cost-control. Welcome to cloud-native AI application development.