Managing AI Workloads on Kubernetes at Scale
How Sveltos Enables Multi-Cluster, GitOps-Driven MLOps
Artificial Intelligence workloads pose unique deployment challenges on Kubernetes: distributed training pipelines, inference services requiring GPUs, strict model lifecycle controls, and hybrid cloud/edge scenarios. These environments demand consistent configuration across clusters, dynamic scaling based on workload signals, and strict compliance and governance — all while minimizing manual effort.
Sveltos — a Kubernetes add-on management controller — has emerged as a tool to directly addresses these needs by offering:
Declarative multi-cluster orchestration of AI services like model servers, data pipelines, and GPU operators;
GitOps-based delivery that ensures version-controlled, reproducible deployments across dev, test, and production;
Event-driven automation for use cases like autoscaling inference workloads or provisioning resources on demand;
Template-based customization to adapt AI deployments to heterogeneous environments (e.g., different storage classes on edge clusters);
Policy enforcement and drift detection to maintain consistency, compliance, and reliability across all clusters.
These capabilities make Sveltos especially well-suited for AI/ML infrastructure teams managing workloads across diverse Kubernetes fleets.
Platform engineers must coordinate complex services across multiple clusters, often spanning cloud and edge environments. With Sveltos, they can apply declarative policies from a central management cluster to deploy and manage add-ons across the fleet. This centralized, GitOps-driven approach provides the automation and consistency that AI infrastructure demands. Features like multi-cluster orchestration, policy-driven deployments, and real-time configuration management are quickly becoming essential for modern AI/ML platforms — and these are exactly the areas where Sveltos delivers.
Sveltos Capabilities for AI Infrastructure
Sveltos simplifies multi-cluster Kubernetes operations through a declarative, GitOps-based model — a powerful fit for the dynamic, distributed nature of AI workloads. The following capabilities underpin how Sveltos helps manage the complexity of deploying AI/ML infrastructure at scale:
Declarative Multi-Cluster Management: Sveltos introduces custom resources (like ClusterProfile) to declare which add-ons to deploy to which clusters. These profiles use label selectors to target clusters, enabling a policy-driven, declarative configuration across environments. Platform engineers define desired add-ons once; Sveltos then ensures each matching cluster converges to that state. This approach leverages Kubernetes principles for consistency and reduces config drift. Sveltos even integrates with Cluster API, automatically discovering new clusters and registering external ones (GKE, EKS, etc.) for management. The result is a single “super control plane” for your fleet, treating clusters themselves as declaratively managed resources.
GitOps-Style Operations (Flux Integration): Sveltos works hand-in-hand with GitOps tools like Flux CD. Users store manifests (YAML/Helm charts, etc.) in Git, and Flux continuously syncs them to the management cluster. Sveltos then takes over to propagate that desired state out to all target clusters. This pipeline ensures version-controlled, continuous delivery of AI infrastructure components. For example, an MLOps engineer can push an updated model serving spec to Git; Flux retrieves it, and Sveltos automatically deploys it to the appropriate clusters.
Add-On Deployment & Template Automation: At its core, Sveltos supports a wide range of add-on formats – Helm charts, raw YAML/JSON manifests, Kustomize packages, Carvel ytt, Jsonnet, etc. – giving flexibility in how AI tools are packaged. Sveltos can orchestrate the rollout of these add-ons across multiple clusters with controlled ordering and dependency management. It features an optional orchestrated deployment order, so components come up in a predictable sequence (useful if an AI workflow has dependencies like “deploy the GPU driver before the model server”). Moreover, Sveltos provides a powerful templating system to customize add-on configs per cluster. Applications and add-ons can be defined as templates, which Sveltos renders by fetching live cluster data — such as labels, metadata, and resource parameters — from the management or managed clusters and injecting it into the manifest before deployment. This allows defining a common AI service (e.g. a Kafka stream or an MLflow tracking server) once, while tailoring it to each cluster’s specific needs (for example, using a different storage class on-prem vs. in the cloud). The templated, declarative approach makes it easy to scale MLOps workflows across many clusters by reusing “golden” configurations.
Event‑Driven Automation: Uniquely, Sveltos includes an event framework that enables reactive automation across clusters. Using Lua scripts, platform engineers define triggers on Kubernetes resource events—such as new nodes joining, workloads scaling, or changes to ConfigMaps or custom resources—and tie them to add-on deployments. In practice, this means Sveltos can dynamically deploy or adjust components in response to real‑time signals. For AI scenarios, you might use this to auto‑scale inference services: for example, when an external monitoring controller detects GPU utilization crossing 80% and updates a ConfigMap, a Lua‑based rule in Sveltos picks up that change and deploys an extra model‑serving instance or even provisions a new micro‑cluster. Paired with a GPU‑aware autoscaler, Sveltos ensures every newly‑provisioned node immediately gets the correct driver stack and monitoring agents. Event‑driven rules make an AI platform more adaptive, since Sveltos responds automatically to resource changes without manual intervention. This “Kubernetes on autopilot” model enables AI platforms to adapt automatically to changing workloads — aligning with the on-demand nature of many AI systems.
Integration with Helm and Kubernetes Ecosystem: Since many AI/ML tools are distributed as Helm charts or Kubernetes manifests, Sveltos’s native support for Helm is valuable. It can directly deploy charts (with user-defined values) to target clusters, including pulling from OCI registries or private chart repos. This means that installing complex AI software (e.g. Kubeflow components, NVIDIA’s GPU Operator, KServe for model serving) can be automated via Sveltos – no manual helm install on each cluster. Sveltos also plays nicely with other ecosystem tools: for instance, it can integrate with Flux’s source controller to consume Git repositories of manifests , and it can complement infrastructure provisioning tools (like Cluster API or Crossplane) by handling post-provisioning configuration.
Multi-Tenancy and Drift Management: In multi-tenant AI platforms (common in enterprises where different teams or customers share infrastructure), Sveltos offers isolation controls. It provides ClusterProfile/Profile resources to scope which add-ons apply to which clusters or namespaces, enabling full isolation between tenants or safe sharing of clusters with proper segmentation. This is useful if, say, an AI SaaS provider hosts models for multiple clients on the same Kubernetes cluster – Sveltos can ensure each client’s agents and services are deployed only in their allowed environments. Additionally, Sveltos has agent-based drift detection to notify or correct any configuration drift. If someone inadvertently changes or deletes a critical ML service on a cluster, Sveltos can detect that and revert to the declared state, maintaining reliability.
With these capabilities, Sveltos provides a comprehensive toolkit for declarative, event-driven, multi-cluster management. Next, we map these features to specific AI deployment challenges and illustrate how Sveltos can add value.
AI Deployment Challenges Addressed by Sveltos
Orchestrating AI Model Training and Inference
Challenge: AI model training and inference often involve orchestrating multiple services (data preprocessors, training jobs, model serving endpoints, etc.) across distributed infrastructure. Teams need to deploy complex frameworks like Kubeflow or custom model servers reliably on many clusters – and keep them updated. Moreover, on-demand inference and bursty training loads require quickly provisioning or reconfiguring clusters to run AI workloads when needed.
How Sveltos Helps: Sveltos’s declarative add-on management is well-suited to deploy entire AI stacks across clusters in a repeatable way. Platform engineers can define a cluster profile for “AI Training Cluster” that includes add-ons like Kubeflow Pipelines, Jupyter notebooks, and a GPU driver – and have Sveltos apply it to any cluster intended for model training. Likewise, an “Inference Cluster” profile might declare KServe or NVIDIA Triton Inference Server plus monitoring agents. When new clusters come online (via Cluster API or other means), Sveltos auto-discovers and configures them with the required AI services, eliminating manual setup. This ensures every environment (dev, prod, edge, etc.) has the correct components for running AI jobs.
Sveltos also enables controlled rollouts and updates of AI services. Using its orchestrated deployment order and dependency rules, an admin can update a model‑serving component across dozens of clusters with minimal downtime — Sveltos sequences the rollout or even performs a canary deployment if configured (for example, targeting test clusters before production). Its event‑driven automation can handle more dynamic scenarios too: when a user submits an inference request, a custom Kubernetes event can trigger Sveltos (via a Lua script) to deploy a temporary model‑serving instance or spin up a micro‑cluster. In real‑world Inference-as-a-Service deployments with Sveltos, this on‑demand orchestration powers frictionless AI workflows: data scientists simply “bring their trained model,” and Sveltos automatically provisions the right services and dependencies in the appropriate cluster.
Managing Multi-Cluster GPU Resources
Challenge: Training and serving modern AI models often require GPU acceleration. Organizations may have multiple Kubernetes clusters with GPU nodes (on different clouds or on-prem datacenters), and need to efficiently distribute AI workloads among them. Key issues include ensuring all clusters have NVIDIA drivers and GPU libraries installed, scheduling jobs to clusters with available GPU capacity, and maximizing utilization of expensive GPU resources. Manually configuring each cluster with the GPU Operator or migrating workloads when one cluster is overloaded can be error-prone.
How Sveltos Helps: Sveltos enables policy‑driven GPU management across clusters. One can define a policy that any cluster labeled accelerator=gpu should have the NVIDIA GPU Operator (which installs drivers, CUDA libraries, etc.) deployed. Sveltos continuously ensures that Operator (packaged as a Helm chart) is installed and healthy on all matching clusters. Because Sveltos continually reconciles the GPU Operator, driver versions stay aligned across the fleet; if a node or operator pod disappears, it’s re‑installed automatically. Many production AI platforms use this pattern to automatically enable GPU capabilities on any new cluster. With Sveltos, clusters are “GPU‑ready” out‑of‑the‑box, which is critical for scaling AI workloads.
Additionally, Sveltos’s centralized policy engine can optimize GPU usage across clusters. You can label clusters by GPU type or capacity (or even use a Node Feature Discovery add‑on to automate this), then steer different workloads accordingly. For example, a heavy training job could be automatically placed on a cluster with A100 GPUs, whereas an inference microservice might land on an edge cluster with a smaller GPU. In one multi‑cluster AI cloud deployment, teams achieved policy‑driven automation that optimizes GPU utilization for maximum efficiency. This involves Sveltos coordinating where inference tasks run based on cluster load or proximity. Indeed, Sveltos can work with external schedulers or controllers—e.g. a “smart routing” service that assigns an incoming inference to the nearest available GPU cluster, and then Sveltos ensures the appropriate model server is deployed at runtime. By abstracting GPU resource management into high‑level policies, Sveltos lets platform teams avoid writing one‑off scripts for each cluster. Instead, they get a consistent way to distribute AI workloads across a pool of GPU clusters.
The real-time configuration management aspect means any change (like adding GPUs or a cluster going offline) is handled simply by updating labels or policies and letting Sveltos converge the fleet. Overall, Sveltos brings centralized automation and awareness to multi‑cluster GPU management, which is vital for keeping expensive accelerators utilized while meeting AI application SLAs.
Lifecycle Management for AI Services and Agents
Challenge: AI systems aren’t static – models get updated, data pipelines evolve, and AI-driven agents (e.g. autonomous processes or microservices) might be spun up in response to events. Managing the lifecycle of these AI components (deploying, upgrading, scaling, and retiring them) in a reliable way is difficult at scale. For instance, an enterprise might have dozens of machine learning microservices (for data ingestion, feature extraction, model predictions, etc.) that need coordinated updates across many clusters. Without a consistent process, there’s risk of version skew (e.g. one cluster running an old model) or downtime during upgrades.
How Sveltos Helps: Sveltos approaches lifecycle management declaratively, which inherently handles many lifecycle tasks as part of continuous reconciliation. When using Sveltos, the desired state of each AI service (and the cluster add-ons supporting it) is stored as YAML. This makes upgrades as simple as updating the version in one place (the Git repo or cluster profile) – Sveltos will detect the change and roll it out to all clusters per the defined rules. Because Sveltos can enforce an orchestrated rollout order and even supports dependencies (dependsOn) between resources, it can orchestrate complex updates safely. For example, if an AI agent consists of a Kafka queue, a Python worker, and an API server, one could specify that the queue must update first, then the worker, then the API, preventing incompatible versions from interacting during the process. This level of control is beyond basic Kubernetes Deployments and is facilitated by Sveltos treating add-ons as part of a higher-level policy graph.
For long-running AI services, Sveltos’s drift detection and self-healing ensure lifecycle stability. If an AI microservice (say, an anomaly detection agent) crashes or is removed on one cluster, the drift agent in Sveltos can trigger re-deployment to restore the desired state. This reduces the ops burden of babysitting dozens of microservices – the platform can heal itself. Sveltos can also be used to gracefully retire services: removing an entry from a cluster profile will signal Sveltos to delete those resources from all target clusters, a controlled teardown of an AI service across the environment.
Crucially, Sveltos’s event-driven actions enable automated lifecycle transitions. AI agents often need to scale out during peak demand or scale in when idle. Sveltos reacts to changes in Kubernetes resources — such as ConfigMaps, Secrets, or custom resources — to trigger these actions. To incorporate metrics like load or utilization, external systems (e.g. a Prometheus-based controller) can update a resource when a threshold is crossed, and Sveltos will respond. For example, a retail AI agent might launch an extra “worker” pod in regional clusters when traffic spikes, by having a controller update a ConfigMap to indicate high load; Sveltos detects the change and deploys a pre-defined add-on. Similarly, it can scale down or remove components when Lua-based rules detect a cleanup condition. This kind of event-based automation puts Kubernetes management on autopilot — allowing AI platforms to adapt to changing workloads (such as new model versions or seasonal demand) without manual intervention.
Enabling Scalable MLOps Workflows
Challenge: MLOps refers to the end-to-end process of deploying machine learning, from data prep and model training to validation, deployment, and monitoring. Scalable MLOps often involves multiple Kubernetes clusters (for example: a dev cluster for experiments, a staging cluster for model validation, and production clusters serving the model). The challenge is to provide a consistent pipeline across these stages and to manage the promotion of models and pipeline components through the environments. Without the right tooling, teams resort to manual porting of configurations or ad-hoc scripts for each environment, leading to inconsistencies and slower iteration.
How Sveltos Helps: Sveltos can act as the backbone of a declarative MLOps platform. Using its GitOps integration, everything from data pipeline definitions to model deployment configs can be version-controlled and automatically synced out. This ensures that, say, your feature engineering service and monitoring agents are deployed uniformly in all relevant clusters. One powerful pattern is to use Git branches or directories for different stages of the ML lifecycle (dev/staging/prod). Flux can synchronize each to the management cluster, and Sveltos can apply them to the corresponding cluster groups. For instance, when a model is ready for production, merging its config into the “prod” Git folder could trigger Sveltos to deploy that model server onto all production clusters labeled env=prod. This provides structured promotion of models in a auditable, reproducible way.
Moreover, Sveltos’s templating and grouping capabilities help address the heterogeneity in MLOps. Not all clusters are identical – your on-prem training cluster might have different storage or more GPUs than a cloud serving cluster. Sveltos templates let you reuse one base manifest (for a Spark job, as an example) and fill in environment-specific values (like dataset path or GPU count) per cluster. The platform team can maintain one logical definition of each pipeline component, avoiding drift between environments while still accommodating their differences. This templated approach was noted as a key to scaling AI/ML workloads across distributed infrastructure, covering the needs of MLOps with repeatable patterns.
Another area Sveltos aids MLOps is observability integration. An important aspect of MLOps is monitoring model performance and data drift. Sveltos can deploy standard monitoring stacks (Prometheus, Grafana, OpenTelemetry collectors) uniformly, and even configure notifications (Slack, Teams, etc.) for certain events. For example, if an ML pipeline fails or a model accuracy drops, Kubernetes can emit an alert that Sveltos catches to send a notification or even trigger a remedial action. While specialized ML monitoring tools exist, Sveltos ensures that the necessary monitoring components are present on each cluster and kept up-to-date, forming a reliable nervous system for MLOps. In sum, Sveltos provides the plumbing for MLOps at scale – consistent environments, automated promotions, and integrated tooling – so data scientists and ML engineers can focus on models and data, not on manually syncing Kubernetes YAML across clusters.
Edge AI and Hybrid Cloud Scenarios
Challenge: AI is increasingly deployed in edge computing scenarios (think AI inferencing on factory floors, retail stores, or remote sensors) as well as in hybrid cloud setups (combining on-premise datacenters with public cloud for bursting). These environments add more complexity: edge clusters may be lightweight (running k3s or k0s Kubernetes), have intermittent connectivity, and need a small footprint solution for updates. The hybrid aspect means one needs to enforce global policies (e.g. security or data locality rules) across very different infrastructure. Managing AI services that span central cloud and far-edge devices demands a unified but flexible control mechanism.
How Sveltos Helps: Sveltos was built with edge and hybrid in mind – it treats “clusters anywhere” as part of one fleet. It can register a variety of cluster types (from managed cloud K8s to microclusters on IoT devices) and apply consistent policies to them all. Because Sveltos’s approach is infrastructure-agnostic, an organization can define, for example, an “edge inference” add-on bundle (which might include a lightweight model, an MQTT broker for sensor data, and a monitoring agent) and have Sveltos enforce that on every edge cluster. When a new edge location comes online, simply tagging it with the appropriate labels will cause Sveltos to deploy the full AI stack there automatically. This drastically reduces the operational overhead of scaling out to hundreds of edge sites. Many production platforms leverage this capability to deliver Kubernetes‑based AI on any infrastructure — from public clouds to far‑edge devices — with zero lock‑in, using open Kubernetes standards.
For hybrid cloud AI, Sveltos’s centralized governance ensures that global policies (like compliance rules or resource quotas) are consistently applied, while still respecting local differences. For instance, an AI application may require that certain data-processing only happens in specific geographic clusters for compliance. Sveltos can enforce that by only deploying the data-processing add-on to clusters in that region (controlled by selectors or cluster classifications). At the same time, something like a fraud-detection model might be deployed to all clusters globally. Sveltos thus acts as a policy engine to push the right AI workloads to the right locations. Its multi-tenancy and role-based controls also mean edge clusters shared between different applications can be partitioned logically – useful when a single edge device is running models for both, say, computer vision and natural language processing from different teams.
Another advantage is Sveltos’s lightweight footprint on the managed clusters. Only the management cluster runs Sveltos’s controllers; the target clusters just need connectivity to receive applied resources (or at most a small agent for status). This suits edge devices where running heavyweight controllers on each would be impractical. Even if an edge cluster is temporarily offline, Sveltos will reconcile as soon as it reconnects, bringing it up-to-date with any missed deployments. In scenarios like the real-world sea algae monitoring on Raspberry Pi clusters (demonstrated at KubeCon EU 2025), one can envision Sveltos ensuring each distributed sensor cluster always has the latest AI model for detection and the messaging software (e.g. NATS) configured correctly – all triggered from a central controller once the devices check in. In summary, Sveltos enables scalable edge AI by providing central control with local execution. Whether deploying to 5 clusters or 500 edge nodes, the declarative model remains the same. This helps organizations build AI solutions that truly span hybrid cloud to edge, confident that Sveltos will keep every node configured according to plan (and quickly update it as the AI models or rules evolve over time).
Real-World Example: European AI Cloud Provider Using Sveltos for Inference-as-a-Service
To illustrate these concepts, let’s look at a real-world deployment. A European cloud provider specializing in private AI adopted Sveltos to power its Inference-as-a-Service offering for customers. In this setup, Sveltos handles dynamic multi-cluster configuration: when a customer submits an AI inference task, the platform can automatically provision the necessary resources across its fleet of Kubernetes clusters, and Sveltos ensures the required services (the specific ML model server, GPU drivers, networking policies, etc.) are deployed on the appropriate clusters on-the-fly. This enables the provider to offer on-demand AI inference workloads across Kubernetes clusters with high efficiency.
In this case, Sveltos proved critical for flexible, controlled orchestration of AI/ML workloads across different environments (OpenStack private cloud, bare-metal clusters, and more). For example, the team centrally defines policies tied to GPU availability — integrating the NVIDIA GPU Operator so that any cluster with GPUs is automatically configured for inference jobs. Policies also govern how and where an inference instance is deployed: using Sveltos, the platform can route an inference request to the optimal cluster (considering latency, data location, etc.) and instantiate the serving stack there, then tear it down when complete. This kind of agility comes from the multi-cluster orchestration and real-time config management that Sveltos provides. It allows provider’s platform engineers to “move fast, stay in control, and scale smartly” even as demand fluctuates.
This architecture highlights how Sveltos integrates with other open tools to streamline AI deployments. It uses Cluster API for creating the Kubernetes clusters, then uses Sveltos to handle post-provisioning add-ons and policies on those clusters. It also incorporates Flux for GitOps and observability tools to complete the picture of an AI platform: declarative cluster creation, plus declarative add-on management. By leveraging Sveltos (already battle-tested in thousands of production clusters), this platform achieves policy-based add-on orchestration at scale, extending Cluster API’s capabilities to be truly production-ready for complex workloads like AI. The combination is symbiotic: the system handles multi-cloud cluster provisioning and lifecycle, while Sveltos manages continuous day-2 operations — including add-ons, configurations, and reactive automation across clusters. Together, this enables teams to unify diverse infrastructure under a single control plane and deliver AI services “with assurance of privacy and sovereignty” in a multi-cloud context.
The success of this deployment underscores how Sveltos’s flexible, declarative architecture can be directly applied to cutting-edge AI infrastructure. From private AI clouds to enterprise-scale AI products, Sveltos is proving to be a core building block for AI workloads. It addresses many of the pain points that AI platform operators face — from deploying GPU software at scale, to automating model releases, to enforcing cross-cluster policy — using the familiar, robust Kubernetes declarative paradigm. Any organization building an internal AI platform or offering AI services across multiple clusters can adopt Sveltos (either directly or via a downstream platform) to drastically simplify management. If no off-the-shelf integration exists for a particular AI tool, Sveltos’s flexibility means teams can create their own Helm chart or manifest and still manage it via the same central controller. As one industry observer noted, this strategic integration of Sveltos shows promising potential for AI infrastructure evolution, and could represent a new standard for platform engineering efficiency at scale.
Sveltos as a Foundation for AI Infrastructure
As AI workloads scale across cloud, on-prem, and edge infrastructure, the operational complexity of managing Kubernetes environments grows exponentially. Sveltos offers a modern solution: a declarative, GitOps-friendly, multi-cluster management system that aligns directly with the unique requirements of AI/ML platforms.
By supporting centralized orchestration, dynamic configuration, event-driven automation, and per-cluster customization, Sveltos enables platform engineers to deploy and maintain complex AI stacks — from GPU operators and model servers to full MLOps pipelines — with consistency and precision. Real-world usage, like the on-demand inference platform built around Sveltos, shows that these capabilities are already powering scalable, flexible AI infrastructure in production.
Sveltos’s architecture — built around declarative management, GitOps workflows, and multi-cluster automation — aligns naturally with the operational demands of AI infrastructure. Teams looking to standardize and scale AI workloads on Kubernetes — without reinventing infrastructure tooling — can adopt Sveltos incrementally: start with a few shared components, expand to policy-driven GPU management, and eventually automate end-to-end AI workload orchestration.
In a landscape where agility and reliability are critical for AI success, Sveltos offers both — bridging the gap between Kubernetes operations and AI deployment needs. As the ecosystem matures, Sveltos is well positioned to become a foundational layer for AI infrastructure teams building the next generation of intelligent systems.
Table: How Sveltos Solves Key AI Infrastructure Challenges
