I need an orchestrator to deploy machine learning models to Kubernetes clusters automatically.

If your goal is “give the orchestrator a model artifact + deployment policy, and have it automatically deploy/update/scale the model across Kubernetes clusters,” I’d build it around KServe rather than implementing the serving layer yourself.

KServe is specifically designed for ML inference on Kubernetes and provides a CRD for models, autoscaling, health checks, networking, GPU autoscaling, scale-to-zero, and canary rollouts.

Recommended architecture

                  ┌─────────────────────┐
                  │   Model Registry    │
                  │ MLflow / S3 / GCS   │
                  └──────────┬──────────┘
                             │
                             ▼
┌──────────────┐     ┌─────────────────────┐
│ Git / CI/CD  │────▶│  ML Orchestrator    │
└──────────────┘     │                     │
                     │ 1. Validate model   │
                     │ 2. Select cluster   │
                     │ 3. Generate CR      │
                     │ 4. Deploy           │
                     │ 5. Monitor          │
                     │ 6. Roll back        │
                     └─────────┬───────────┘
                               │
                 ┌─────────────┼─────────────┐
                 ▼             ▼             ▼
          ┌───────────┐ ┌───────────┐ ┌───────────┐
          │ Cluster A │ │ Cluster B │ │ Cluster C │
          │           │ │           │ │           │
          │  KServe   │ │  KServe   │ │  KServe   │
          │    ↓      │ │    ↓      │ │    ↓      │
          │ Model v42 │ │ Model v42 │ │ Model v41 │
          └───────────┘ └───────────┘ └───────────┘

The orchestrator should own

• Model lifecycle: registered → validated → staged → deployed → healthy → promoted/rolled back.
• Cluster selection: GPU/CPU availability, region, capacity, latency, tenant, and policy.
• Deployment strategy: rolling, canary, blue/green, or shadow.
• Configuration: replicas, CPU/GPU, memory, autoscaling, ingress, secrets.
• Health verification: readiness, inference smoke tests, latency/error thresholds.
• Rollback: automatically revert to the previous model if deployment health deteriorates.
• Multi-cluster reconciliation: continuously ensure actual Kubernetes state matches desired state.
• Observability: metrics, logs, traces, deployment events, and model-level SLOs.

KServe's InferenceService CRD gives you a particularly good abstraction for the deployment portion. Its standard mode can use Kubernetes HPA, while its Knative mode supports request-based autoscaling and scale-to-zero.

Example deployment specification

I would expose your own higher-level API rather than making users write KServe YAML directly:

apiVersion: ml.platform/v1
kind: ModelDeployment

metadata:
  name: fraud-detector

spec:
  model:
    uri: s3://models/fraud-detector/42
    framework: pytorch

  placement:
    clusters:
      - us-east-prod
      - us-west-prod
    gpu: true

  resources:
    requests:
      cpu: "2"
      memory: "8Gi"
      nvidia.com/gpu: "1"

  scaling:
    minReplicas: 1
    maxReplicas: 10
    metric: concurrency
    target: 20

  rollout:
    strategy: canary
    canaryPercent: 10
    promotion:
      errorRate: "< 1%"
      p95Latency: "< 200ms"
      duration: 15m

  rollback:
    enabled: true

Your controller translates this into the appropriate KServe resources.

For example, the resulting resource could look conceptually like:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: fraud-detector
spec:
  predictor:
    minReplicas: 1
    maxReplicas: 10
    scaleTarget: 20
    scaleMetric: concurrency
    model:
      modelFormat:
        name: pytorch
      storageUri: s3://models/fraud-detector/42
      resources:
        requests:
          cpu: "2"
          memory: "8Gi"
          nvidia.com/gpu: "1"

KServe can handle the resulting serving infrastructure, including autoscaling and model serving.

Components I'd use

An important design choice is to make the orchestrator declarative.

Instead of:

deploy_model()
wait()
scale_model()
update_model()
rollback_model()

make it:

Desired state:
    fraud-detector = model version 42
    in clusters A + B
    1-10 replicas
    canary 10%

Then have a reconciliation loop continually converge Kubernetes toward that state.

That makes failures, retries, controller restarts, and multi-cluster deployments much easier to handle.

KServe vs. Seldon

I'd shortlist KServe and Seldon Core 2.

KServe is the cleaner choice if your primary requirement is standardized model deployment/serving on Kubernetes with autoscaling and rollout capabilities.

Seldon Core 2 is particularly attractive if you need more sophisticated inference graphs, model pipelines, explainers, drift/outlier detection, and large-scale multi-model serving. Its current documentation describes support for TensorFlow, PyTorch, sklearn, ONNX, custom Python models, asynchronous pipelines, Kafka, tracing, and metrics.

So my default stack would be:

Custom Orchestrator → KServe → Kubernetes

with Argo CD added if you want GitOps, and a model registry/object store underneath.

If you tell me whether you're deploying LLMs, traditional ML models, or both, I can sketch the actual orchestrator design—including its API, Kubernetes CRDs, controller/reconciliation loop, multi-cluster architecture, and deployment workflow.

I would expose your own higher-level API rather than making users write KServe YAML directly:

To automatically orchestrate and deploy machine learning models to Kubernetes clusters, you need a specialized MLOps serving or pipeline orchestrator rather than a raw CI/CD tool. The top open-source and enterprise-grade orchestrators fit different architectural preferences:

• KServe : The industry standard for Kubernetes-native model serving. It integrates with Knative to provide serverless autoscaling (including scaling to zero), request batching, and canary rollouts for ML models.
• Kubeflow : A comprehensive, end-to-end machine learning toolkit for Kubernetes. It handles everything from training workflows via Kubeflow Pipelines to model serving via KServe or MLServer.
• BentoML : An extremely developer-friendly framework for packaging and serving models. It features BentoCloud or native Kubernetes/Helm chart generation to containerize models and push high-performance inference services straight to a cluster.
• Argo Workflows + KitOps : A lightweight approach where Argo Workflows manages the directed acyclic graphs (DAGs) on Kubernetes, combined with KitOps to package models, data, and code into secure OCI artifacts.
• Seldon Core : Another powerful Kubernetes CRD-based serving orchestrator designed to manage complex inference graphs, outlier detection, and A/B testing out of the box.

To help narrow down the best orchestration setup for your workflow, tell me:

• Are you looking for real-time HTTP/gRPC inference or batch processing?
• Do you need an end-to-end data science platform (like Kubeflow) or a lightweight serving tool (like BentoML or KServe)?
• Are you managing the Kubernetes clusters yourself or using a managed cloud service (EKS, GKE, AKS)?

To automatically deploy machine learning models to Kubernetes clusters, Kubeflow combined with KServe or Apache Airflow paired with BentoML/KServe serve as the leading open-source orchestration frameworks.

Here are the top orchestrators used for automated Kubernetes-native machine learning deployments:

Top ML Orchestrators for Kubernetes

• Kubeflow + KServe : A comprehensive, native Kubernetes MLOps platform. Kubeflow orchestrates the end-to-end training pipeline, while KServe handles serverless model inference, autoscaling (including scale-to-zero), and revisions directly inside Kubernetes.
• Apache Airflow : A general-purpose workflow orchestrator ideal for scheduling complex data engineering and batch ML jobs. It can automatically trigger container builds and apply Kubernetes manifests or Helm charts via the KubernetesPodOperator.
• BentoML : A flexible model-serving framework that packages models into standardized production artifacts ("Bentos") and containerizes them for seamless, automated GitOps or CI/CD deployment to Kubernetes.
• Prefect : A modern, Python-first workflow orchestrator that manages dataflows and can easily trigger deployments, run infrastructure tasks, and sync artifacts to Kubernetes clusters with minimal boilerplate.

To help narrow down the best choice for your workflow, tell me:

• Are you looking for a full end-to-end MLOps platform (like Kubeflow) or a lightweight model serving tool (like BentoML or KServe)?
• Do you already have a CI/CD pipeline tool (like GitHub Actions or Argo CD) in place?

If you need an orchestrator that automatically deploys machine learning models to Kubernetes clusters, you are looking for an MLOps deployment platform rather than just a Kubernetes operator. A good architecture usually combines model lifecycle management, CI/CD, Kubernetes-native serving, and cluster automation.

Recommended architecture

                 ┌──────────────────┐
                 │ Model Registry   │
                 │ (MLflow, etc.)   │
                 └────────┬─────────┘
                          │
                          ▼
                 ┌──────────────────┐
                 │ Deployment       │
                 │ Orchestrator     │
                 │ (Argo / Custom)  │
                 └────────┬─────────┘
                          │
             ┌────────────┼────────────┐
             ▼            ▼            ▼
        Kubernetes A  Kubernetes B  Kubernetes C
             │            │            │
             ▼            ▼            ▼
        KServe       Seldon       Custom
        Serving      Core         Services

Components I would consider

1. Model registry

Stores approved models, versions, metadata, and artifacts.

• MLflow — common choice for model tracking and registry. It supports packaging models and deploying them into Kubernetes-based serving systems such as KServe.

2. Kubernetes-native model serving

Handles replicas, scaling, rollouts, and inference endpoints.

• KServe — provides Kubernetes-native inference resources with features such as autoscaling, canary deployments, and framework support.
• Seldon — another Kubernetes serving option.

3. Workflow/orchestration layer

Coordinates deployment decisions.

Good options:

• Argo Workflows — pipeline execution and automation.
• Argo CD — continuously reconciles desired model deployments from Git.
• Kubeflow — broader ML platform with pipelines, notebooks, training, and serving.

4. Custom deployment controller (if you need a true orchestrator)

For a platform team, I would build a Kubernetes Operator:

Example custom resource:

apiVersion: ml.platform.io/v1
kind: ModelDeployment
metadata:
  name: fraud-detector
spec:
  model:
    registry: mlflow
    name: fraud-model
    version: "42"

  target:
    clusters:
      - prod-us-east
      - prod-eu-west

  strategy:
    type: canary
    traffic:
      initial: 10

  resources:
    gpu: 1
    replicas: 3

The controller would:

1. Watch ModelDeployment resources.
2. Pull the approved model artifact.
3. Generate KServe/Seldon resources.
4. Apply them to target clusters.
5. Monitor health metrics.
6. Promote, rollback, or stop rollout automatically.

A production-grade stack

A strong default design:

Deployment flow example

1. Data scientist registers model:

fraud-model:v42

1. Approval gate runs:

accuracy > 95%
latency < 100ms
security scan passed

1. Orchestrator creates deployment:

ModelDeployment CRD
        |
        ▼
KServe InferenceService
        |
        ▼
Kubernetes Deployment
        |
        ▼
Traffic split:
  10% new model
  90% old model

1. Metrics decide:

• Promote to 100%
• Roll back
• Notify owners

If you want to build it yourself

I would implement:

• Go Kubernetes Operator using Kubebuilder
• CRDs for models, environments, and rollout policies
• Argo CD ApplicationSets for multi-cluster deployment
• KServe InferenceServices as the serving abstraction
• MLflow Registry API as the source of truth

This gives you a reusable "model deployment control plane" similar to what large ML platforms build internally. MLflow + KServe is already a supported pattern for Kubernetes model deployment.

I can also provide a reference architecture or a starter Kubernetes Operator implementation if you want to build this orchestrator.

To automatically orchestrate and deploy machine learning models to Kubernetes clusters , you should use a combination of a Kubernetes-native model serving platform (like KServe or BentoML ) paired with a GitOps continuous delivery tool (like Argo CD ) or an ML pipeline orchestrator (like Kubeflow Pipelines). Rather than a single monolithic tool, automated ML deployment relies on a decoupled stack that monitors model registries or source repositories and reconciles the live cluster state.

Top Orchestration & Deployment Tools

Recommended Architecture Pattern

1. Package: Use an ML framework packaging tool to turn your model artifact into an OCI-compliant container image or point to a registered model URI in an object store.
2. Define: Expose the model using a custom resource definition (CRD) via a serving layer like KServe or BentoML to manage traffic routing and hardware allocation (GPUs).
3. Sync: Automate promotion by updating a version tag or model URI inside a Git repository, allowing Argo CD to safely synchronize the desired state with your live Kubernetes cluster.

To help narrow down the ideal stack, let me know:

• Are you deploying classic ML models (scikit-learn/XGBoost) or Large Language Models (LLMs)?
• Do you already use a specific CI/CD or GitOps tool (like GitHub Actions or Argo CD)?
• Will your clusters require GPU acceleration nodes?

Yes. If by orchestrator you mean “give it a model artifact + configuration, and it automatically deploys, scales, updates, and monitors the model across Kubernetes,” I’d build around KServe rather than writing the Kubernetes orchestration layer from scratch.

Recommended architecture

                 ┌─────────────────────┐
                 │   Model Registry    │
                 │ MLflow / S3 / GCS   │
                 └──────────┬──────────┘
                            │
                            ▼
┌──────────────┐    ┌───────────────────┐
│ Git / API /  │───▶│  ML Orchestrator  │
│ Model Event  │    │                   │
└──────────────┘    │ • validate model  │
                    │ • choose cluster  │
                    │ • generate CR     │
                    │ • deploy/rollback│
                    │ • monitor         │
                    └─────────┬─────────┘
                              │
                              ▼
                    ┌───────────────────┐
                    │ Kubernetes        │
                    │                   │
                    │ KServe            │
                    │   ↓               │
                    │ InferenceService  │
                    │   ↓               │
                    │ Model Server      │
                    └───────────────────┘

KServe is specifically designed for this layer: its InferenceService Kubernetes CRD abstracts model deployment, autoscaling, networking, health checks, GPU scaling, scale-to-zero, and canary rollouts. It supports common frameworks including PyTorch, TensorFlow, XGBoost, scikit-learn and ONNX.

What I'd make the orchestrator do

1. Receive a deployment request

   model: fraud-detector
   version: 42
   artifact: s3://models/fraud-detector/42/
   framework: pytorch
   cpu: 2
   memory: 8Gi
   gpu: 1
   replicas:
     min: 1
     max: 10
   

2. Validate the model

   • artifact exists
   • model metadata is valid
   • image/model-server compatibility
   • resource requirements
   • security/policy checks

3. Select the Kubernetes cluster

   • dev/staging/prod
   • GPU vs CPU cluster
   • region
   • available capacity
   • compliance requirements

4. Generate/apply a KServe InferenceService

5. Wait for readiness

   • Kubernetes scheduling
   • model download
   • container health
   • inference health check

6. Automatically expose the endpoint

7. Monitor it

   • latency
   • throughput
   • errors
   • CPU/GPU utilization
   • replica count
   • model health

8. Perform progressive deployments

   v41 ──────────────── 100%
              \
               v42 ── 5% ── 25% ── 50% ── 100%
   

   Roll back automatically if error rate or latency exceeds your threshold. KServe supports canary/A-B deployment patterns.

The important distinction

I would not make the orchestrator itself responsible for serving inference.

Use separate layers:

This keeps your custom code relatively small.

When I'd use Ray instead

If your models are LLMs, multi-model pipelines, or Python-heavy inference applications, I'd seriously consider Ray Serve + KubeRay. Ray Serve supports model composition, dynamic scaling, batching, multi-GPU serving, and arbitrary Python business logic. KubeRay provides the Kubernetes-native RayService resource and handles the underlying Ray cluster and Serve deployment.

So my rule of thumb would be:

• Traditional ML / standardized inference → KServe
• LLMs / complex inference graphs / Python application logic → Ray Serve
• Full ML platform → Kubeflow + KServe; Kubeflow is explicitly designed as a composable foundation for AI platforms on Kubernetes.

If you're building this yourself, I can also lay out a production-grade orchestrator design (API + database + controller architecture + Kubernetes CRDs + deployment workflow) and give you the initial implementation structure.

If you need an orchestrator that automatically deploys machine learning models to Kubernetes clusters, you’re usually looking for an MLOps deployment control plane rather than just a Kubernetes operator. A good architecture is:

             Model Registry
                  |
                  v
        Deployment Orchestrator
                  |
     +------------+-------------+
     |            |             |
 Kubernetes   Kubernetes    Kubernetes
 Cluster A    Cluster B     Cluster C
     |            |             |
 Model Pods   Model Pods   Model Pods
     |
 Monitoring / Autoscaling / Rollbacks

A practical stack:

1. Model deployment layer

Use a Kubernetes-native serving platform:

• KServe — provides Kubernetes custom resources for deploying models, autoscaling, health checks, canary rollouts, and multi-framework serving.
• Kubeflow — broader ML platform if you also need pipelines, notebooks, model registry, and training workflows.

Example flow:

1. Model artifact registered:

s3://models/fraud-detector/v42/model.onnx

2. Orchestrator creates:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: fraud-detector
spec:
  predictor:
    model:
      modelFormat:
        name: onnx
      storageUri: s3://models/fraud-detector/v42

3. KServe creates and manages the Kubernetes workload.

---

2. The orchestrator itself

Build a controller/service responsible for:

Inputs

• New model version
• Deployment policy
• Target cluster
• Resource requirements
• Compliance rules

Actions

• Select Kubernetes cluster
• Generate manifests
• Deploy model
• Validate health
• Shift traffic
• Roll back if needed

A typical implementation:

API Gateway
     |
Deployment Service (Python/Go)
     |
+----+----------------+
|                     |
Kubernetes API    Model Registry
|
KServe CRDs
|
Inference Services

Recommended components:

---

3. Multi-cluster deployment

For multiple Kubernetes clusters:

                 Orchestrator
                      |
          +-----------+-----------+
          |                       |
     Cluster Registry        Policy Engine
          |
  +-------+-------+
  |       |       |
 EKS     GKE    On-prem

Useful patterns:

• Register clusters:

  cluster:
    name: gpu-prod-us
    provider: eks
    gpu: true
    region: us-east
  

• Add placement rules:

  if model.gpu_required:
      deploy_to(cluster.gpu=true)
  
  if latency < 50ms:
      deploy_nearest_region()
  

---

4. Deployment lifecycle automation

Your orchestrator should manage:

Blue/green

v41  ────────100%
v42  ────────0%

test v42

v41  ────────0%
v42  ────────100%

Canary

90% traffic → old model
10% traffic → new model

observe metrics

shift traffic gradually

KServe supports canary deployments and autoscaling patterns for model serving.

---

5. Example internal API

Your orchestrator could expose:

POST /deployments

{
  "model": "recommendation-engine",
  "version": "2026.08.15",
  "framework": "pytorch",
  "target": "gpu-production",
  "replicas": 3,
  "strategy": "canary"
}

It creates:

Deployment
Service
Ingress
HPA
KServe InferenceService
Monitoring Rules

---

6. A strong production design

I would typically build it as:

• Argo CD → GitOps deployment engine
• KServe → model serving runtime
• Kubeflow Model Registry or MLflow → model lifecycle
• Custom Kubernetes Operator → orchestration logic
• Prometheus → performance signals
• OpenTelemetry → tracing
• Vault → credentials

The custom operator becomes your “AI deployment brain,” while Kubernetes and KServe handle the infrastructure mechanics.

If you’re building this from scratch, a Kubernetes Operator + KServe + GitOps approach is usually the cleanest long-term architecture.

To automate the deployment of machine learning models to Kubernetes clusters, you typically use a specialized ML inference/serving orchestrator that runs natively on Kubernetes . Rather than writing raw Kubernetes Deployment and Service YAML manifests for every model version, these tools abstract the infrastructure while handling scaling, traffic routing, and hardware acceleration (GPUs/TPUs).

The top open-source orchestration tools designed specifically for automated machine learning model deployment on Kubernetes include:

• KServe : A Kubernetes-native model serving platform built on top of Knative. It provides serverless inference, scale-to-zero capabilities, request/response logging, and easy integration with standard runtimes (TensorFlow, PyTorch, Triton, vLLM for LLMs). It is ideal if you want autoscaling based on actual request traffic.
• Seldon Core : An open-source framework to package, deploy, and monitor machine learning models on Kubernetes. It excels at advanced deployment patterns like canary rollouts, A/B testing, and multi-model complex graphs (ensembles, routers, and transformers).
• BentoML : An open-source MLOps framework that standardizes how you package model artifacts into cloud-ready bundles called "bentos". Its companion ecosystem (Yatai) is designed specifically to orchestrate and scale these bentos natively on Kubernetes clusters.
• Kubeflow Pipelines : A comprehensive toolkit for running end-to-end ML workflows on Kubernetes. While often used for training, it orchestrates the entire lifecycle—allowing you to define DAGs (Directed Acyclic Graphs) that automatically build, test, and trigger model deployments as the final step of a pipeline.
• Argo CD : While not machine learning-specific, many modern MLOps stacks use Argo CD alongside a model registry like MLflow. When a new model version is registered, an automated GitOps workflow updates the desired deployment state in a Git repository, and Argo CD synchronizes it to your target Kubernetes clusters.

To help narrow down which orchestrator fits your environment, tell me:

• Are you deploying traditional ML models (Scikit-Learn, XGBoost) or Large Language Models (LLMs)?
• Do you need advanced traffic management like A/B testing or canary deployments?
• Are you managing a single Kubernetes cluster or multi-cluster/multi-cloud environments?

If you want an orchestrator that automatically deploys ML models onto Kubernetes, my first choice would be KServe.

Best fit: KServe

KServe is Kubernetes-native and is specifically designed for production model inference. It provides an InferenceService CRD, autoscaling, health checks, GPU autoscaling, scale-to-zero, and canary rollouts.

A typical architecture would be:

        Git / Model Registry
                │
                ▼
       ┌─────────────────┐
       │ Deployment      │
       │ Orchestrator    │
       └────────┬────────┘
                │
        generates/applies
        InferenceService
                │
                ▼
       ┌─────────────────┐
       │    Kubernetes   │
       │                 │
       │     KServe      │
       │        │        │
       │   ┌────┴────┐   │
       │   │ Model   │   │
       │   │ Server  │   │
       │   └─────────┘   │
       └─────────────────┘
                │
                ▼
          Inference API

For example, your orchestrator could receive:

model:
  name: fraud-detector
  version: "42"
  framework: pytorch
  artifact: s3://models/fraud/42
  resources:
    gpu: 1
  replicas:
    min: 1
    max: 10
  rollout:
    strategy: canary
    percentage: 10

and translate that into a KServe InferenceService. KServe then handles much of the Kubernetes-specific serving machinery.

Other good options

Kubeflow is particularly interesting if you also need training pipelines and a model registry; its Model Registry can integrate directly with KServe for deployment.

KubeRay / Ray Serve is a stronger choice if your "deployment" involves multi-model pipelines or distributed Python inference. KubeRay's RayService CRD manages the Ray cluster and Serve application together and supports upgrades.

What I'd build

If your requirement is "a model gets registered → automatically deployed to the appropriate Kubernetes cluster → monitored → rolled out/rolled back", I'd use:

Model Registry → Git/CI trigger → Deployment Orchestrator → KServe → Kubernetes

with the orchestrator responsible for:

• selecting the target cluster
• selecting CPU/GPU resources
• generating the deployment specification
• deploying the model
• waiting for readiness
• running smoke tests
• doing canary/blue-green rollout
• automatically rolling back failed versions
• updating deployment status
• enforcing policies/quotas
• optionally deploying across multiple clusters/clouds

KServe is the serving layer, rather than the entire orchestration platform. That distinction matters: if you need the orchestrator itself, I'd put a small Kubernetes controller/API in front of KServe rather than trying to make KServe do everything.

If you tell me whether you're deploying LLMs, PyTorch/TensorFlow models, or arbitrary ML models, I can recommend a concrete architecture and stack.

To automate machine learning model deployments to Kubernetes clusters, you need an orchestration layer that handles model packaging, endpoint creation, autoscaling (including scale-to-zero), and safe rollouts.

The top-tier open-source orchestrators and model-serving frameworks designed natively for Kubernetes are categorized below by their strengths:

• Kubeflow — The comprehensive, Kubernetes-native machine learning toolkit. It covers the entire lifecycle from pipeline orchestration to training and model serving via its ecosystem. It is ideal if you want an all-in-one platform on K8s, though it comes with a steep operational learning curve.
• KServe — Focused specifically on standardized, high-performance model serving on Kubernetes. It leverages Knative to provide serverless features like scale-to-zero, autoscaling based on traffic, and sophisticated canary/blue-green rollouts out of the box.
• Seldon Core — A robust framework that turns containerized models into microservices on Kubernetes. It uses custom resource definitions (SeldonDeployment ) to orchestrate complex serving topologies like A/B tests, model chains, and ensembles.
• Argo Workflows — A lightweight, Kubernetes-native workflow engine implemented as a CRD. While not an ML server by itself, it is heavily used to orchestrate data pipelines, batch inference, and automated CI/CD triggers that package models and apply configurations directly to clusters.
• BentoML — Excellent for the "last mile" of packaging models into production-ready API services. It features native containerization support and works cleanly with Kubernetes manifests for smooth CI/CD deployments.

To help narrow down the best choice, tell me:

• Are you deploying classical ML models (scikit-learn, XGBoost) or Large Language Models (LLMs) requiring specialized GPU scheduling?
• Do you already have Kubernetes expertise on your team, or are you looking for the lowest operational overhead?