SageMaker is a fully managed AWS service with integrated end-to-end ML workflows, while Kubeflow is an open-source Kubernetes-native platform requiring self-management but offering greater flexibility and vendor independence.
Kubeflow vs SageMaker — Side-by-side comparison on A Versus B
Kubernetes-native ML orchestration platform for deploying, scaling, and managing ML workflows.
Fully managed AWS ML service for end-to-end model development, training, and deployment.
Deployment Model. Infrastructure Cost (Monthly for 100GB data). Vendor Lock-in. Setup Time (Production-ready). Built-in ML Algorithms. Model Deployment Options. Community Size (GitHub Stars)
Choose SageMaker if you need rapid deployment, integrated AWS services, and predictable pricing with minimal operational overhead—ideal for teams prioritizing time-to-value. Choose Kubeflow if you require multi-cloud flexibility, cost control over large-scale clusters, and deep customization without vendor lock-in—ideal for enterprises with mature Kubernetes expertise.
Kubeflow: 13,500+ | Amazon SageMaker: N/A
Kubeflow: 168 (with K8s cluster) | Amazon SageMaker: N/A
Hyperparameter Tuning Trials (Tested Max)
Kubeflow: Native with RBAC | Amazon SageMaker: N/A
Kubeflow: Built-in (KServe) | Amazon SageMaker: N/A
Kubeflow: Required | Amazon SageMaker: N/A
Kubeflow: Yes (Argo Workflows) | Amazon SageMaker: N/A
Kubeflow: Native (TF, PyTorch, MPI) | Amazon SageMaker: N/A
Kubeflow: 5-8 major frameworks | Amazon SageMaker: N/A
Kubeflow: Advanced (Level 5) | Amazon SageMaker: N/A
Kubeflow: 40-60 hours | Amazon SageMaker: N/A
Kubeflow: Kubernetes only | Amazon SageMaker: N/A
Kubeflow: 6 (HPO, KFServing, tracking, distributed training, AutoML, experiment management) | Amazon SageMaker: N/A
Kubeflow: Community only | Amazon SageMaker: N/A
Kubeflow: Open-source (Apache 2.0) | Amazon SageMaker: N/A
Kubeflow: YAML/Kustomize configuration | Amazon SageMaker: N/A
Kubeflow: 8-16 weeks | Amazon SageMaker: N/A
Kubeflow: 20 minutes | Amazon SageMaker: N/A
Kubeflow: $400 (compute only) | Amazon SageMaker: N/A
Kubeflow: 4/5 (Kubernetes expert required) | Amazon SageMaker: N/A
Kubeflow: Manual setup required (3-4 hours) | Amazon SageMaker: N/A
Kubeflow: 4+ (AWS, Azure, GCP, on-premise) | Amazon SageMaker: N/A
Kubeflow: Limited (requires external solutions like Determined AI) | Amazon SageMaker: N/A
Kubeflow: Manual or third-party (MLflow, Seldon) | Amazon SageMaker: N/A
Kubeflow: 13,000+ stars | Amazon SageMaker: N/A
Monthly Infrastructure Cost (single ml.m5.xlarge)
Kubeflow: $36-$144 (cluster dependent) | Amazon SageMaker: $90-$360
Kubeflow: Kubernetes cluster limit (typically 50-200) | Amazon SageMaker: 500
Kubeflow: 30-120 (manual setup required) | Amazon SageMaker: 5-15 (one-click endpoint)
Kubeflow: 13,200+ | Amazon SageMaker: Not open-source
Kubeflow: Community-driven, vendor partnerships | Amazon SageMaker: AWS Premium/Enterprise Support
Kubeflow: Low - portable across clouds | Amazon SageMaker: High - AWS-exclusive
Kubeflow: 8+ (TensorFlow, PyTorch, Scikit-learn, XGBoost, MXNet, Spark, Katib, KServe) | Amazon SageMaker: N/A
Typical Monthly Infrastructure Cost (Small Deployment)
Kubeflow: $1,500-3,000 | Amazon SageMaker: N/A
Learning Curve (1-10 scale, 10 is hardest)
Kubeflow: 120-240 minutes (K8s cluster creation, Kubeflow deployment) | Amazon SageMaker: N/A
Kubeflow: 200-500 USD (K8s cluster minimum) | Amazon SageMaker: N/A
Kubeflow: 12 frameworks (TensorFlow, PyTorch, XGBoost, Scikit-learn, Keras, etc.) | Amazon SageMaker: 12 frameworks
Kubeflow: 50-200 lines (YAML configs, custom code) | Amazon SageMaker: N/A
Kubeflow: 100+ jobs (Kubernetes scheduler) | Amazon SageMaker: N/A
Kubeflow: KServe, Seldon, TensorFlow Serving, Kubernetes-native | Amazon SageMaker: N/A
Kubeflow: 18,200 stars | Amazon SageMaker: N/A
Time to First Successful Experiment Tracking
Kubeflow: 120-180 minutes | Amazon SageMaker: N/A
Kubeflow: 14-28 days | Amazon SageMaker: 1-3 days
Kubeflow: 60+ (community-maintained) | Amazon SageMaker: 15 (AWS-optimized)
Kubeflow: 13,500+ | Amazon SageMaker: 2,800+ (AWS examples repo)
Kubeflow: 5+ (KServe, Seldon, custom) | Amazon SageMaker: 3 (Endpoints, Batch, Serverless)
Kubeflow: None - platform agnostic | Amazon SageMaker: High - AWS ecosystem dependent
Kubeflow: Manual setup required, third-party tools | Amazon SageMaker: Built-in with 40% cost reduction vs manual
Kubeflow: 1+ (customer-managed) | Amazon SageMaker: 0 (AWS manages all)
Kubeflow: 2-4 weeks | Amazon SageMaker: N/A
Kubeflow: N/A | Amazon SageMaker: 17 algorithms
Monthly Compute Cost (ml.m5.large, 730 hours)
Kubeflow: N/A | Amazon SageMaker: $113.68
Kubeflow: N/A | Amazon SageMaker: 18 minutes
Kubeflow: N/A | Amazon SageMaker: 13 (SOC2, HIPAA, PCI-DSS, ISO 27001)
Kubeflow: N/A | Amazon SageMaker: SageMaker Canvas (basic drag-drop, limited customization)
Kubeflow: N/A | Amazon SageMaker: Not supported natively
Kubeflow: N/A | Amazon SageMaker: Unlimited with $200 free tier
Kubeflow: N/A | Amazon SageMaker: 0.5-1 hour (managed)
Kubeflow: N/A | Amazon SageMaker: 15+ via SageMaker SDK
Kubeflow: N/A | Amazon SageMaker: 15+ integrated services
Kubeflow: N/A | Amazon SageMaker: Model Package Groups, version control, approval workflows, bias detection
Kubeflow: N/A | Amazon SageMaker: 5-50ms (managed endpoints)
Kubeflow: N/A | Amazon SageMaker: AWS only
Kubeflow: N/A | Amazon SageMaker: $2-150 (managed services)
Kubeflow: N/A | Amazon SageMaker: 2.5 minutes
Kubeflow: N/A | Amazon SageMaker: 150+ algorithms
Kubeflow: N/A | Amazon SageMaker: $0.269
Kubeflow: N/A | Amazon SageMaker: Canvas (limited, 2024 release)
Kubeflow: N/A | Amazon SageMaker: Fully managed with 10K+ features
Kubeflow: N/A | Amazon SageMaker: Requires custom APIs
Kubeflow: N/A | Amazon SageMaker: SageMaker Experiments + CloudWatch
Kubeflow: N/A | Amazon SageMaker: $0.50-2.00 per hour (no free tier)
Kubeflow: N/A | Amazon SageMaker: $20-150 (p3.2xlarge GPU instances)
Kubeflow: N/A | Amazon SageMaker: 60-120 minutes (VPC, IAM, notebook setup)
Kubeflow: N/A | Amazon SageMaker: 80GB (A100 instances, multi-GPU support)
Kubeflow: N/A | Amazon SageMaker: 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR)
Kubeflow: N/A | Amazon SageMaker: 50,000 estimated AWS ML community
Kubeflow: N/A | Amazon SageMaker: All types: Tabular, Deep Learning, Time Series, RL, Graph, Clustering
Kubeflow: N/A | Amazon SageMaker: 6,000 tokens/sec
Kubeflow: N/A | Amazon SageMaker: 15-30 minutes
Cost per Million Tokens (A100, on-demand)
Kubeflow: N/A | Amazon SageMaker: 500+ models
Kubeflow: N/A | Amazon SageMaker: Full training, fine-tuning, auto-scaling
Kubeflow: N/A | Amazon SageMaker: 99.9% (available on Premium support)
Kubeflow: N/A | Amazon SageMaker: AWS-managed (serverless option available)
Kubeflow: N/A | Amazon SageMaker: Official AWS documentation + support plans
Kubeflow: N/A | Amazon SageMaker: 300 (built-in algorithms)
Kubeflow: N/A | Amazon SageMaker: $0 (12-month free trial, limited)
Kubeflow: N/A | Amazon SageMaker: 50-80 lines
Kubeflow: N/A | Amazon SageMaker: Advanced (model registry, drift detection, explainability)
Kubeflow: N/A | Amazon SageMaker: 200,000+ (estimated)
Kubeflow: N/A | Amazon SageMaker: Up to 90%
Kubeflow: N/A | Amazon SageMaker: Deep (40+ AWS services)
Development Time for Production Deployment
Kubeflow: N/A | Amazon SageMaker: 2-3 weeks (with managed services)
Inference Throughput (LLaMA 2 70B, A100 GPU)
Kubeflow: N/A | Amazon SageMaker: 5,500 tokens/sec (batch 32)
Kubeflow: N/A | Amazon SageMaker: 78 GB (standard)
Kubeflow: N/A | Amazon SageMaker: 5-10 minutes (managed)
Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
Kubeflow: N/A | Amazon SageMaker: $2.10 (SageMaker on-demand)
Kubeflow: N/A | Amazon SageMaker: 50+ marketplace models
Kubeflow: N/A | Amazon SageMaker: Fully managed by AWS
Kubeflow: N/A | Amazon SageMaker: 99.9% (AWS SLA)
Kubeflow: N/A | Amazon SageMaker: 24/7 AWS enterprise support
AutoML Accuracy (Tabular Classification)
Kubeflow: N/A | Amazon SageMaker: $4,200
Kubeflow: N/A | Amazon SageMaker: 47 models
Kubeflow: N/A | Amazon SageMaker: Unlimited
How to choose between Kubeflow and Amazon SageMaker
Kubeflow vs SageMaker — Structured Comparison Data
Machine-readable comparison dataset for Kubeflow vs SageMaker. Contains 115 attribute comparisons, 5 FAQ pairs.
Kubeflow vs SageMaker — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Kubeflow vs SageMaker.
Which platform is cheaper for enterprise-scale ML workloads?
Kubeflow typically costs 30-50% less at scale (1,000+ GB data) because you only pay for compute resources and eliminate SageMaker's per-endpoint surcharges. However, Kubeflow requires 3-4 dedicated DevOps engineers ($300K+ annually), offsetting savings for smaller teams. SageMaker's predictable pay-per-use model suits 50-500 GB workloads best.
Can I migrate models from SageMaker to Kubeflow?
Yes, but with effort. Both support ONNX and containerized models. The main challenge is migrating SageMaker-specific integrations (Data Labeling, Feature Store APIs). Budget 2-6 weeks to port a production pipeline from SageMaker to KServe/Kubeflow depending on AWS service dependencies.
Which has better support for distributed GPU training?
Kubeflow offers superior flexibility—native support for Horovod, PyTorch Distributed, and custom distributed frameworks across heterogeneous hardware. SageMaker's distributed training requires using SageMaker-specific Python APIs, limiting framework choices. For cutting-edge distributed training, Kubeflow is preferred by research teams.
SageMaker has a gentler curve (1-2 weeks for basic competency) using Python SDK and AWS Console. Kubeflow requires Kubernetes fundamentals (CRDs, namespaces, YAML), taking 3-4 weeks. However, once proficient, Kubeflow's configuration-as-code approach scales better than SageMaker's UI-driven workflows.
Which is better for real-time inference at scale?
SageMaker Endpoints with auto-scaling handle real-time inference more easily, with guaranteed 99.9% uptime SLA and AWS-managed infrastructure. Kubeflow requires setting up KServe with custom scaling policies and monitoring. SageMaker is simpler; Kubeflow offers more control over latency/cost trade-offs.
How to Choose Between Kubeflow and Amazon SageMaker
Deployment Model: Kubeflow has Self-managed on Kubernetes clusters, while Amazon SageMaker has Fully managed AWS service.
Consider: Infrastructure Cost (Monthly for 100GB data)
Infrastructure Cost (Monthly for 100GB data): Kubeflow has $1,200-2,500 (cluster + ops), while Amazon SageMaker has $800-1,800 (pay-per-use).
Vendor Lock-in: Kubeflow has None - runs anywhere with Kubernetes, while Amazon SageMaker has AWS ecosystem dependent.
Setup Time (Production-ready): Kubeflow has 2-4 weeks, while Amazon SageMaker has 1-3 days.
Built-in ML Algorithms: Kubeflow has 60+ community-maintained operators, while Amazon SageMaker has 15+ AWS-optimized built-in algorithms.
Model Deployment Options: Kubeflow has KServe, Seldon Core, custom endpoints, while Amazon SageMaker has SageMaker Endpoints, Batch Transform, Serverless.
Community Size (GitHub Stars): Kubeflow has 13,500+ stars, while Amazon SageMaker has 2,800+ stars (AWS docs/examples).