Key Takeaways
- AI orchestration is foundational for scaling AI workloads and delivering apps faster, enabling modern businesses to unlock the full potential of their AI systems.
- Without a well-defined AI orchestration strategy, teams risk delays, inefficiencies, and spiraling infrastructure complexity that hinder innovation and operational efficiency.
- Rafay simplifies infrastructure orchestration with centralized environment management, automation, and built-in policy enforcement across environments using advanced AI orchestration tools, supporting multiple models and complex tasks seamlessly.
Why AI Application Delivery Is Different from Cloud-Native Workloads
AI application delivery is fundamentally more complex than traditional software delivery for cloud-native workloads. From training large language models (LLMs) to deploying real-time inference services, AI workloads are:
- Data-intensive: Requiring massive volumes of structured and unstructured data from multiple data sources and data pipelines, including data ingestion from various sources and databases.
- Compute-heavy: Dependent on high-performance GPUs and specialized infrastructure optimized for machine learning and artificial intelligence workloads.
- Distributed: Deployed across multi-cloud, on-premises, and hybrid environments, often leading to data silos and challenges in data management.
Teams must support rapid iteration across environments while managing GPU resource allocation, model versioning, and compliance. Traditional CI/CD pipelines and orchestration strategies weren’t built to handle these demands, unlike traditional automation.
The Infrastructure Orchestration Challenge
AI orchestration refers to automating the coordination of infrastructure, workloads, and processes across environments. For AI/ML, this includes:
- Provisioning GPUs and Kubernetes clusters
- Scheduling and scaling AI workloads
- Monitoring model performance and cost impact
- Securing data and enforcing policies
Common obstacles include:
- Manual provisioning: Slows delivery and increases the risk of misconfigurations.
- Fragmented environments: Lack of visibility, inconsistent governance, and data silos that impede effective data collection and workflow automation.
- Resource contention: Competing teams over-allocate GPUs, leading to waste and increased operational costs.
Without AI orchestration, teams end up with brittle, inefficient systems that hinder AI innovation and operational efficiency, limiting the ability to leverage AI-generated content and retrieval augmented generation techniques effectively.
What the “Right” Infrastructure Orchestration Strategy Looks Like
A modern AI orchestration strategy for AI workloads should support:
1. Self-Service and Automation
Empower internal teams (e.g., data scientists, ML engineers, and AI agents) to provision environments and deploy workloads on-demand without waiting on DevOps, automating workflows and repetitive tasks. AI orchestration ensures that multiple models and different tools work in harmony to complete complex tasks.
2. Standardized Pipelines
Establish reusable deployment workflows across dev, test, and production to ensure consistency, compliance, and faster decision making. This includes integrating data management best practices to handle customer data securely and efficiently.
3. Centralized Control
Consolidate infrastructure visibility and governance into a single control plane. Teams can manage clusters, GPUs, policies, and more from one place, streamlining workflows and maintaining compliance across large enterprises and various industries.
4. Policy-Based Governance
Implement rules for GPU access, workload prioritization, budget enforcement, and security policies, tailored for AI-specific challenges, with audit trails for transparency. This supports process automation while ensuring data security and regulatory compliance.
How Platform Teams Enable Faster AI Delivery
Platform engineering plays a critical role in supporting high-velocity AI development. They build the internal platforms and automation that ML teams depend on. Key responsibilities include:
- Provisioning Infrastructure: GPU clusters, Kubernetes environments, storage, and networking optimized for AI workloads.
- Managing Access & Security: Role-based controls and audit logging to protect sensitive data and maintain compliance.
- Enforcing Standards: Blueprints for how AI workloads are deployed and monitored, ensuring consistent model performance and governance.
- Scaling Efficiently: Autoscaling clusters based on real-time demand to handle increased complexity and multiple sources of data.
A strong platform team reduces bottlenecks, accelerates time-to-market, and improves cost efficiency by optimizing data flow and resource allocation across AI systems.
Rafay’s Infrastructure Orchestration Capabilities for AI Workloads
Rafay provides a comprehensive AI infrastructure orchestration platform purpose-built for modern AI/ML workloads. It enables platform teams to:
- Manage Kubernetes & GPU infrastructure from a single pane of glass, supporting multi-cloud and hybrid environments without silos.
- Automate provisioning and scaling based on workload demand, enabling efficient handling of multiple models and data pipelines.
- Enforce security and operational policies across environments, maintaining compliance and audit trails.
- Integrate with CI/CD and GitOps workflows for seamless automation and model retraining without human intervention.
- Monitor real-time performance, utilization, and cost impact to optimize operational efficiency and reduce operational costs.
Whether you’re training LLMs or deploying inference pipelines, Rafay helps you deliver AI solutions faster and more efficiently, transforming industries by accelerating AI-driven innovation.
Best Practices for AI App Orchestration
1. Adopt GitOps for Deployment Automation
Use GitOps as the single source of truth for infrastructure and application configurations. This enables reproducibility, version control, and auditability, reducing operational risks and supporting process automation.
2. Implement Autoscaling for Workload Efficiency
Configure dynamic scaling policies for clusters and GPUs to meet changing demand while avoiding overprovisioning and minimizing operational costs, ensuring efficient use of resources in large enterprises.
3. Define Guardrails Early
Set limits and quotas on compute, GPU, and network usage to prevent cost overruns and ensure fair resource sharing, supporting intelligent automation and maintaining compliance.
4. Use Monitoring to Guide Optimization
Track workload-level metrics for latency, memory usage, throughput, and cost to refine deployments and resource allocation, enhancing model performance and enabling faster decision making.
5. Standardize with Blueprints
Define templated infrastructure and deployment patterns to enforce governance and speed up new environment provisioning, aligning with business goals and supporting the integration of new models and AI agents.
Conclusion: Why Infrastructure Orchestration Is Essential for AI Success
As AI becomes more deeply embedded in enterprise operations, the speed at which organizations can deploy new AI apps will define their competitive edge. A modern AI orchestration strategy eliminates the infrastructure friction that slows teams down, freeing them to focus on innovation.
With Rafay, platform teams gain the right tools to automate, scale, and govern AI workload delivery across any environment. From reducing operational complexity to optimizing GPU utilization, Rafay is the infrastructure orchestration layer that powers scalable, secure, and cost-efficient AI solutions, helping organizations transform industries and realize the surge underscores the value of coordinated AI workflows.
Learn More About Rafay’s Platform for AI Workloads
Ready to accelerate your AI application delivery? Explore Rafay’s solution for deploying a GPU PaaS or book a demo to see how our AI infrastructure orchestration platform can streamline your AI initiatives.
FAQs
What is infrastructure orchestration in AI application delivery?
AI orchestration refers to the automation of infrastructure provisioning, workload deployment, and governance policies across environments. For AI, this includes managing GPUs, Kubernetes clusters, and AI-specific workload needs, ensuring multiple models and data sources work seamlessly together.
Why is orchestration important for AI workloads?
AI workloads require significant infrastructure coordination, including high-performance compute, storage, and multi-cloud support. AI orchestration helps simplify this complexity and ensures speed, efficiency, and governance, enabling organizations to leverage AI driven innovation and complex tasks.
How does Rafay support infrastructure orchestration for AI workloads?
Rafay provides a centralized control plane for managing Kubernetes and GPU resources, automating deployments, enforcing policies, and gaining visibility across environments, supporting data ingestion, data management, and workflow automation.
Can Rafay help reduce infrastructure costs?
Yes. With autoscaling, real-time observability, and policy enforcement, Rafay ensures that resources are allocated efficiently—minimizing idle compute and reducing waste while maintaining compliance and security.
Who benefits from Rafay’s infrastructure orchestration platform?
Platform engineering teams, DevOps, ML/AI teams, and IT leaders benefit from Rafay’s ability to streamline operations, accelerate delivery, and enforce standards for AI application development across various industries and large enterprises.
