Kubeflow vs SageMaker
type: Article
description: 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.
language: en-US
date published: 2026-07-09T14:41:33.849Z
date created: 2026-07-09T14:41:33.849Z
date modified: 2026-07-09T14:41:33.885Z
content reference time: 2026-07-09T14:41:33.885Z
author: A Versus B
publisher: A Versus B
about: Kubeflow
about: Amazon SageMaker
abstract: 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.
text: 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)
conclusion text: 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.
additional property: GitHub Stars (Community Size)
  type: PropertyValue
  value: Kubeflow: 13,500+ | Amazon SageMaker: N/A
additional property: Initial Setup Time (Hours)
  type: PropertyValue
  value: Kubeflow: 168 (with K8s cluster) | Amazon SageMaker: N/A
additional property: Hyperparameter Tuning Trials (Tested Max)
  type: PropertyValue
  value: Kubeflow: 100+ | Amazon SageMaker: N/A
additional property: Multi-Tenancy Support
  type: PropertyValue
  value: Kubeflow: Native with RBAC | Amazon SageMaker: N/A
additional property: Model Serving Integration
  type: PropertyValue
  value: Kubeflow: Built-in (KServe) | Amazon SageMaker: N/A
additional property: Production Deployments (Reported)
  type: PropertyValue
  value: Kubeflow: 500+ | Amazon SageMaker: N/A
additional property: Kubernetes Requirement
  type: PropertyValue
  value: Kubeflow: Required | Amazon SageMaker: N/A
additional property: Native Orchestration Support
  type: PropertyValue
  value: Kubeflow: Yes (Argo Workflows) | Amazon SageMaker: N/A
additional property: Distributed Training Support
  type: PropertyValue
  value: Kubeflow: Native (TF, PyTorch, MPI) | Amazon SageMaker: N/A
additional property: Framework Integrations
  type: PropertyValue
  value: Kubeflow: 5-8 major frameworks | Amazon SageMaker: N/A
additional property: Minimum Required DevOps Knowledge
  type: PropertyValue
  value: Kubeflow: Advanced (Level 5) | Amazon SageMaker: N/A
additional property: Setup Time (Baseline)
  type: PropertyValue
  value: Kubeflow: 40-60 hours | Amazon SageMaker: N/A
additional property: Infrastructure Flexibility
  type: PropertyValue
  value: Kubeflow: Kubernetes only | Amazon SageMaker: N/A
additional property: Native ML Features Count
  type: PropertyValue
  value: Kubeflow: 6 (HPO, KFServing, tracking, distributed training, AutoML, experiment management) | Amazon SageMaker: N/A
additional property: Commercial Support Tier
  type: PropertyValue
  value: Kubeflow: Community only | Amazon SageMaker: N/A
additional property: License Cost
  type: PropertyValue
  value: Kubeflow: Open-source (Apache 2.0) | Amazon SageMaker: N/A
additional property: DAG Creation Method
  type: PropertyValue
  value: Kubeflow: YAML/Kustomize configuration | Amazon SageMaker: N/A
additional property: Typical Enterprise Deployment Time
  type: PropertyValue
  value: Kubeflow: 8-16 weeks | Amazon SageMaker: N/A
additional property: Setup Time to First Training Job
  type: PropertyValue
  value: Kubeflow: 20 minutes | Amazon SageMaker: N/A
additional property: Monthly Cost (50 GPU training hours)
  type: PropertyValue
  value: Kubeflow: $400 (compute only) | Amazon SageMaker: N/A
additional property: Required DevOps Expertise Level
  type: PropertyValue
  value: Kubeflow: 4/5 (Kubernetes expert required) | Amazon SageMaker: N/A
additional property: BigQuery Native Integration
  type: PropertyValue
  value: Kubeflow: Manual setup required (3-4 hours) | Amazon SageMaker: N/A
additional property: Supported Cloud Providers
  type: PropertyValue
  value: Kubeflow: 4+ (AWS, Azure, GCP, on-premise) | Amazon SageMaker: N/A
additional property: AutoML Capabilities
  type: PropertyValue
  value: Kubeflow: Limited (requires external solutions like Determined AI) | Amazon SageMaker: N/A
additional property: Model Registry & Versioning
  type: PropertyValue
  value: Kubeflow: Manual or third-party (MLflow, Seldon) | Amazon SageMaker: N/A
additional property: Community & Adoption (2024)
  type: PropertyValue
  value: Kubeflow: 13,000+ stars | Amazon SageMaker: N/A
additional property: Monthly Infrastructure Cost (single ml.m5.xlarge)
  type: PropertyValue
  value: Kubeflow: $36-$144 (cluster dependent) | Amazon SageMaker: $90-$360
  value reference: Kubeflow
additional property: Maximum Parallel Training Jobs
  type: PropertyValue
  value: Kubeflow: Kubernetes cluster limit (typically 50-200) | Amazon SageMaker: 500
  value reference: Amazon SageMaker
additional property: Time to Deploy Model to Production
  type: PropertyValue
  value: Kubeflow: 30-120 (manual setup required) | Amazon SageMaker: 5-15 (one-click endpoint)
  value reference: Amazon SageMaker
additional property: Community Size (GitHub Stars)
  type: PropertyValue
  value: Kubeflow: 13,200+ | Amazon SageMaker: Not open-source
additional property: Enterprise Support Options
  type: PropertyValue
  value: Kubeflow: Community-driven, vendor partnerships | Amazon SageMaker: AWS Premium/Enterprise Support
additional property: Cloud Provider Lock-in Risk
  type: PropertyValue
  value: Kubeflow: Low - portable across clouds | Amazon SageMaker: High - AWS-exclusive
additional property: ML-Specific Integrations
  type: PropertyValue
  value: Kubeflow: 8+ (TensorFlow, PyTorch, Scikit-learn, XGBoost, MXNet, Spark, Katib, KServe) | Amazon SageMaker: N/A
additional property: Typical Monthly Infrastructure Cost (Small Deployment)
  type: PropertyValue
  value: Kubeflow: $1,500-3,000 | Amazon SageMaker: N/A
additional property: Learning Curve (1-10 scale, 10 is hardest)
  type: PropertyValue
  value: Kubeflow: 8/10 | Amazon SageMaker: N/A
additional property: Supported Deployment Targets
  type: PropertyValue
  value: Kubeflow: Kubernetes only | Amazon SageMaker: N/A
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: Kubeflow: 120-240 minutes (K8s cluster creation, Kubeflow deployment) | Amazon SageMaker: N/A
additional property: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
  value: Kubeflow: 200-500 USD (K8s cluster minimum) | Amazon SageMaker: N/A
additional property: Supported ML Frameworks
  type: PropertyValue
  value: Kubeflow: 12 frameworks (TensorFlow, PyTorch, XGBoost, Scikit-learn, Keras, etc.) | Amazon SageMaker: 12 frameworks
additional property: Lines of Code for Integration
  type: PropertyValue
  value: Kubeflow: 50-200 lines (YAML configs, custom code) | Amazon SageMaker: N/A
additional property: Max Concurrent Jobs on Standard Setup
  type: PropertyValue
  value: Kubeflow: 100+ jobs (Kubernetes scheduler) | Amazon SageMaker: N/A
additional property: Production Model Deployment Support
  type: PropertyValue
  value: Kubeflow: KServe, Seldon, TensorFlow Serving, Kubernetes-native | Amazon SageMaker: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: Kubeflow: 18,200 stars | Amazon SageMaker: N/A
additional property: Time to First Successful Experiment Tracking
  type: PropertyValue
  value: Kubeflow: 120-180 minutes | Amazon SageMaker: N/A
additional property: Initial Setup Time
  type: PropertyValue
  value: Kubeflow: 14-28 days | Amazon SageMaker: 1-3 days
  value reference: Amazon SageMaker
additional property: Monthly Operating Cost (100GB workload)
  type: PropertyValue
  value: Kubeflow: $1,200-2,500 | Amazon SageMaker: $800-1,800
  value reference: Amazon SageMaker
additional property: Built-in ML Algorithms
  type: PropertyValue
  value: Kubeflow: 60+ (community-maintained) | Amazon SageMaker: 15 (AWS-optimized)
  value reference: Kubeflow
additional property: Community GitHub Stars
  type: PropertyValue
  value: Kubeflow: 13,500+ | Amazon SageMaker: 2,800+ (AWS examples repo)
  value reference: Kubeflow
additional property: Model Deployment Options
  type: PropertyValue
  value: Kubeflow: 5+ (KServe, Seldon, custom) | Amazon SageMaker: 3 (Endpoints, Batch, Serverless)
  value reference: Kubeflow
interactivity type: active
significant link: https://www.aversusb.net/entity/kubeflow
significant link: https://www.aversusb.net/alternatives/kubeflow
significant link: https://www.aversusb.net/entity/amazon-sagemaker
significant link: https://www.aversusb.net/alternatives/amazon-sagemaker
significant link: https://www.aversusb.net/api/answer/kubeflow-vs-sagemaker)
significant link: https://www.aversusb.net/api/v1/schema/kubeflow-vs-sagemaker)
related link: https://www.aversusb.net/compare/kubeflow-vs-sagemaker
related link: https://www.aversusb.net/compare/sagemaker-vs-azure-ml
related link: https://www.aversusb.net/compare/kubeflow-vs-airflow
related link: https://www.aversusb.net/compare/kubeflow-vs-ray
related link: https://www.aversusb.net/compare/kubeflow-vs-mlflow
related link: https://www.aversusb.net/compare/kubeflow-vs-prefect
related link: https://www.aversusb.net/compare/mlflow-vs-sagemaker
related link: https://www.aversusb.net/compare/kubeflow-vs-vertex-ai
teaches: How to choose between Kubeflow and Amazon SageMaker
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Kubeflow vs SageMaker — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Kubeflow
type: SoftwareApplication
language: en-US
description: Kubernetes-native ML orchestration platform for deploying, scaling, and managing ML workflows.
subject of: Kubeflow vs SageMaker

Amazon SageMaker
type: SoftwareApplication
alternate name: AS
alternate name: Amazon Sagemaker
language: en-US
description: Fully managed AWS ML service for end-to-end model development, training, and deployment.
subject of: Kubeflow vs SageMaker

Kubeflow vs SageMaker — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Kubeflow vs SageMaker. Contains 115 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: GitHub Stars (Community Size)
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Initial Setup Time (Hours)
  type: PropertyValue
  value reference: Setup
variable measured: Hyperparameter Tuning Trials (Tested Max)
  type: PropertyValue
  value reference: Performance
variable measured: Multi-Tenancy Support
  type: PropertyValue
  value reference: Features
variable measured: Model Serving Integration
  type: PropertyValue
  value reference: Features
variable measured: Production Deployments (Reported)
  type: PropertyValue
  value reference: Enterprise Adoption
variable measured: Kubernetes Requirement
  type: PropertyValue
  value reference: Infrastructure
variable measured: Native Orchestration Support
  type: PropertyValue
  value reference: Features
variable measured: Distributed Training Support
  type: PropertyValue
  value reference: Features
variable measured: Framework Integrations
  type: PropertyValue
  value reference: Ecosystem
variable measured: Minimum Required DevOps Knowledge
  type: PropertyValue
  value reference: Learning Curve
variable measured: Setup Time (Baseline)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Infrastructure Flexibility
  type: PropertyValue
  value reference: Deployment
variable measured: Native ML Features Count
  type: PropertyValue
  value reference: ML Capabilities
variable measured: Commercial Support Tier
  type: PropertyValue
  value reference: Support
variable measured: License Cost
  type: PropertyValue
  value reference: Pricing
variable measured: DAG Creation Method
  type: PropertyValue
  value reference: Developer Experience
variable measured: Typical Enterprise Deployment Time
  type: PropertyValue
  value reference: Time to Production
variable measured: Setup Time to First Training Job
  type: PropertyValue
  value reference: Operational Efficiency
variable measured: Monthly Cost (50 GPU training hours)
  type: PropertyValue
  value reference: Cost
variable measured: Required DevOps Expertise Level
  type: PropertyValue
  value reference: Operational Burden
variable measured: BigQuery Native Integration
  type: PropertyValue
  value reference: Ecosystem Integration
variable measured: Supported Cloud Providers
  type: PropertyValue
  value reference: Ecosystem
variable measured: AutoML Capabilities
  type: PropertyValue
  value reference: Features
variable measured: Model Registry & Versioning
  type: PropertyValue
  value reference: Model Management
variable measured: Community & Adoption (2024)
  type: PropertyValue
  value reference: Community
variable measured: Monthly Infrastructure Cost (single ml.m5.xlarge)
  type: PropertyValue
  value reference: Cost
variable measured: Maximum Parallel Training Jobs
  type: PropertyValue
  value reference: Performance
variable measured: Time to Deploy Model to Production
  type: PropertyValue
  value reference: Operations
variable measured: Community Size (GitHub Stars)
  type: PropertyValue
  value reference: Community
variable measured: Enterprise Support Options
  type: PropertyValue
  value reference: Support & Services
variable measured: Cloud Provider Lock-in Risk
  type: PropertyValue
  value reference: Flexibility
variable measured: ML-Specific Integrations
  type: PropertyValue
  value reference: ML Capabilities
variable measured: Typical Monthly Infrastructure Cost (Small Deployment)
  type: PropertyValue
  value reference: Total Cost of Ownership
variable measured: Learning Curve (1-10 scale, 10 is hardest)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Supported Deployment Targets
  type: PropertyValue
  value reference: Flexibility
variable measured: Setup Time (Minutes)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
  value reference: Cost
variable measured: Supported ML Frameworks
  type: PropertyValue
  value reference: Integration
variable measured: Lines of Code for Integration
  type: PropertyValue
  value reference: Ease of Use
variable measured: Max Concurrent Jobs on Standard Setup
  type: PropertyValue
  value reference: Scalability
variable measured: Production Model Deployment Support
  type: PropertyValue
  value reference: Production Readiness
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Time to First Successful Experiment Tracking
  type: PropertyValue
  value reference: Time to Value
variable measured: Initial Setup Time
  type: PropertyValue
  value reference: Development Efficiency
variable measured: Monthly Operating Cost (100GB workload)
  type: PropertyValue
  value reference: Pricing
variable measured: Built-in ML Algorithms
  type: PropertyValue
  value reference: Features
variable measured: Community GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: Model Deployment Options
  type: PropertyValue
  value reference: Flexibility
variable measured: Vendor Lock-in Risk
  type: PropertyValue
  value reference: Architecture
variable measured: Data Labeling Integration
  type: PropertyValue
  value reference: Features
variable measured: Infrastructure Requirements
  type: PropertyValue
  value reference: Operations
variable measured: Minimum Infrastructure Setup Time
  type: PropertyValue
  value reference: Time to Production
variable measured: Open-Source License Cost
  type: PropertyValue
  value reference: Pricing
variable measured: Built-in Algorithms Available
  type: PropertyValue
  value reference: Feature Set
variable measured: Monthly Compute Cost (ml.m5.large, 730 hours)
  type: PropertyValue
  value reference: Pricing
variable measured: Average Time to Production
  type: PropertyValue
  value reference: Implementation
variable measured: Compliance Certifications
  type: PropertyValue
  value reference: Security
variable measured: No-Code Model Builder Capability
  type: PropertyValue
  value reference: Usability
variable measured: Microsoft Enterprise Tool Integration
  type: PropertyValue
  value reference: Integration
variable measured: Market Share (2024)
variable measured: Free Trial Duration
variable measured: Setup Time
variable measured: ML Frameworks Supported
variable measured: End-to-End Managed Services
variable measured: Model Registry Capabilities
variable measured: Inference Latency (Typical)
variable measured: Multi-Cloud Support
variable measured: Licensing & Cost (Monthly minimum)
is based on: https://www.aversusb.net/compare/kubeflow-vs-sagemaker)

Kubeflow vs SageMaker — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Kubeflow vs SageMaker.
has defined term: Community & Adoption
has defined term: Setup
has defined term: Performance
has defined term: Features
has defined term: Enterprise Adoption
has defined term: Infrastructure
has defined term: Ecosystem
has defined term: Learning Curve
has defined term: Ease of Use
has defined term: Deployment
has defined term: ML Capabilities
has defined term: Support
has defined term: Pricing
has defined term: Developer Experience
has defined term: Time to Production
has defined term: Operational Efficiency
has defined term: Cost
has defined term: Operational Burden
has defined term: Ecosystem Integration
has defined term: Model Management
has defined term: Community
has defined term: Operations
has defined term: Support & Services
has defined term: Flexibility
has defined term: Total Cost of Ownership
has defined term: Integration
has defined term: Scalability
has defined term: Production Readiness
has defined term: Time to Value
has defined term: Development Efficiency
has defined term: Architecture
has defined term: Feature Set
has defined term: Implementation
has defined term: Security
has defined term: Usability
has defined term: Adoption
has defined term: Trial Period
has defined term: AI/ML Capabilities
has defined term: ML Ops Features
has defined term: MLOps
has defined term: Model Coverage
has defined term: Security & Compliance
has defined term: Capabilities
has defined term: Reliability
has defined term: Resources
has defined term: Production Features
has defined term: User Base
has defined term: Time to Market
has defined term: Support & Licensing
has defined term: Model Performance

Community & Adoption
type: DefinedTerm
term code: community-&-adoption
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Learning Curve
type: DefinedTerm
term code: learning-curve
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

ML Capabilities
type: DefinedTerm
term code: ml-capabilities
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Time to Production
type: DefinedTerm
term code: time-to-production
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Operational Efficiency
type: DefinedTerm
term code: operational-efficiency
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Operational Burden
type: DefinedTerm
term code: operational-burden
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Ecosystem Integration
type: DefinedTerm
term code: ecosystem-integration
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Model Management
type: DefinedTerm
term code: model-management
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Support & Services
type: DefinedTerm
term code: support-&-services
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Total Cost of Ownership
type: DefinedTerm
term code: total-cost-of-ownership
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Production Readiness
type: DefinedTerm
term code: production-readiness
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Time to Value
type: DefinedTerm
term code: time-to-value
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Development Efficiency
type: DefinedTerm
term code: development-efficiency
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Feature Set
type: DefinedTerm
term code: feature-set
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Trial Period
type: DefinedTerm
term code: trial-period
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

AI/ML Capabilities
type: DefinedTerm
term code: ai/ml-capabilities
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

ML Ops Features
type: DefinedTerm
term code: ml-ops-features
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

MLOps
type: DefinedTerm
term code: mlops
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Model Coverage
type: DefinedTerm
term code: model-coverage
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Security & Compliance
type: DefinedTerm
term code: security-&-compliance
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Resources
type: DefinedTerm
term code: resources
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Production Features
type: DefinedTerm
term code: production-features
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

User Base
type: DefinedTerm
term code: user-base
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Time to Market
type: DefinedTerm
term code: time-to-market
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Support & Licensing
type: DefinedTerm
term code: support-&-licensing
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

Model Performance
type: DefinedTerm
term code: model-performance
in defined term set: Kubeflow vs SageMaker — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T14:41:33.885Z
author: A Versus B
publisher: A Versus B
is part of: kubeflow-vs-sagemaker)
main entity: Which platform is cheaper for enterprise-scale ML workloads?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I migrate models from SageMaker to Kubeflow?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which has better support for distributed GPU training?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: What's the learning curve difference?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which is better for real-time inference at scale?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between Kubeflow and Amazon SageMaker
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Infrastructure Cost (Monthly for 100GB data)
step: Consider: Vendor Lock-in
step: Consider: Setup Time (Production-ready)
step: Consider: Built-in ML Algorithms
step: Consider: Model Deployment Options
step: Consider: Community Size (GitHub Stars)
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Deployment Model
type: HowToStep
position: 1
text: Deployment Model: Kubeflow has Self-managed on Kubernetes clusters, while Amazon SageMaker has Fully managed AWS service.

Consider: Infrastructure Cost (Monthly for 100GB data)
type: HowToStep
position: 2
text: Infrastructure Cost (Monthly for 100GB data): Kubeflow has $1,200-2,500 (cluster + ops), while Amazon SageMaker has $800-1,800 (pay-per-use).

Consider: Vendor Lock-in
type: HowToStep
position: 3
text: Vendor Lock-in: Kubeflow has None - runs anywhere with Kubernetes, while Amazon SageMaker has AWS ecosystem dependent.

Consider: Setup Time (Production-ready)
type: HowToStep
position: 4
text: Setup Time (Production-ready): Kubeflow has 2-4 weeks, while Amazon SageMaker has 1-3 days.

Consider: Built-in ML Algorithms
type: HowToStep
position: 5
text: Built-in ML Algorithms: Kubeflow has 60+ community-maintained operators, while Amazon SageMaker has 15+ AWS-optimized built-in algorithms.

Consider: Model Deployment Options
type: HowToStep
position: 6
text: Model Deployment Options: Kubeflow has KServe, Seldon Core, custom endpoints, while Amazon SageMaker has SageMaker Endpoints, Batch Transform, Serverless.

Consider: Community Size (GitHub Stars)
type: HowToStep
position: 7
text: Community Size (GitHub Stars): Kubeflow has 13,500+ stars, while Amazon SageMaker has 2,800+ stars (AWS docs/examples).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Kubeflow vs SageMaker
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Kubeflow vs SageMaker

Vendor Lock-in Risk
type: PropertyValue
value: Kubeflow: None - platform agnostic | Amazon SageMaker: High - AWS ecosystem dependent

Data Labeling Integration
type: PropertyValue
value: Kubeflow: Manual setup required, third-party tools | Amazon SageMaker: Built-in with 40% cost reduction vs manual

Infrastructure Requirements
type: PropertyValue
value: Kubeflow: 1+ (customer-managed) | Amazon SageMaker: 0 (AWS manages all)
value reference: Amazon SageMaker

Minimum Infrastructure Setup Time
type: PropertyValue
value: Kubeflow: 2-4 weeks | Amazon SageMaker: N/A

Open-Source License Cost
type: PropertyValue
value: Kubeflow: Free | Amazon SageMaker: N/A

Built-in Algorithms Available
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 17 algorithms

Monthly Compute Cost (ml.m5.large, 730 hours)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $113.68

Average Time to Production
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 18 minutes

Compliance Certifications
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 13 (SOC2, HIPAA, PCI-DSS, ISO 27001)

No-Code Model Builder Capability
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: SageMaker Canvas (basic drag-drop, limited customization)

Microsoft Enterprise Tool Integration
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Not supported natively

Market Share (2024)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 31%

Free Trial Duration
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Unlimited with $200 free tier

Setup Time
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 0.5-1 hour (managed)

ML Frameworks Supported
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 15+ via SageMaker SDK

End-to-End Managed Services
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 15+ integrated services

Model Registry Capabilities
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Model Package Groups, version control, approval workflows, bias detection

Inference Latency (Typical)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 5-50ms (managed endpoints)

Multi-Cloud Support
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: AWS only

Licensing & Cost (Monthly minimum)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $2-150 (managed services)

Model Deployment Time
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 2.5 minutes

Pre-built ML Algorithms
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 150+ algorithms

AutoML Accuracy on Tabular Data
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 87.3%

Compute Instance Cost (ml.m5.xlarge)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $0.269

No-code Interface Maturity
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Canvas (limited, 2024 release)

Feature Store Capability
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Fully managed with 10K+ features

Microsoft Ecosystem Integration
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Requires custom APIs

Training Job Monitoring & Debugging
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: SageMaker Experiments + CloudWatch

Pre-trained Models Available
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 2,000

Minimum Inference Cost
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $0.50-2.00 per hour (no free tier)

Typical ML Training Cost
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $20-150 (p3.2xlarge GPU instances)

Setup Time to First Model Deployment
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 60-120 minutes (VPC, IAM, notebook setup)

Maximum Single GPU Memory
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 80GB (A100 instances, multi-GPU support)

Enterprise Compliance Certifications
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR)

Community Size
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 50,000 estimated AWS ML community

Supported ML Model Types
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: All types: Tabular, Deep Learning, Time Series, RL, Graph, Clustering

Inference Throughput (single A100 GPU)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 6,000 tokens/sec

Setup Time (basic inference)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 15-30 minutes

Cost per Million Tokens (A100, on-demand)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $0.85

Supported Models (major open-source)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 500+ models

Training Capabilities
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Full training, fine-tuning, auto-scaling

Enterprise SLA Uptime
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 99.9% (available on Premium support)

Infrastructure Management
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: AWS-managed (serverless option available)

Community & Documentation
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Official AWS documentation + support plans

Model Hub Size
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 300 (built-in algorithms)

Free Tier Cost
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $0 (12-month free trial, limited)

Average Model Fine-Tuning Time
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 50-80 lines

Enterprise Monitoring/Governance
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Advanced (model registry, drift detection, explainability)

Monthly Active Users
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 200,000+ (estimated)

Compute Cost Reduction (Spot Instances)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Up to 90%

AWS Integration Depth
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Deep (40+ AWS services)

Development Time for Production Deployment
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 2-3 weeks (with managed services)

Inference Throughput (LLaMA 2 70B, A100 GPU)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 5,500 tokens/sec (batch 32)

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 78 GB (standard)

Deployment Time
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 5-10 minutes (managed)

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $2.10 (SageMaker on-demand)

Model Support (Open-Source LLMs)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 50+ marketplace models

Infrastructure Management Required
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Fully managed by AWS

SLA Availability Guarantee
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 99.9% (AWS SLA)

Enterprise Support Availability
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 24/7 AWS enterprise support

AutoML Accuracy (Tabular Classification)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 87.2%

Monthly Cost (100 training jobs)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: $4,200

Feature Store Query Latency (p99)
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 45ms

Pre-built Industry Models
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 47 models

Enterprise Market Share
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: 32%

Model Training Parallelization
type: PropertyValue
value: Kubeflow: N/A | Amazon SageMaker: Unlimited

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
xpath: //*[@id='verdict']

Kubeflow — Wikipedia
type: CreativeWork

Amazon SageMaker — Wikipedia
type: CreativeWork

Compare Kubeflow vs Amazon SageMaker
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/kubeflow-vs-sagemaker)
object: Kubeflow
object: Amazon SageMaker

Market Share (2024)
type: PropertyValue
value reference: Adoption

Free Trial Duration
type: PropertyValue
value reference: Trial Period

Setup Time
type: PropertyValue
value reference: Implementation

ML Frameworks Supported
type: PropertyValue
value reference: AI/ML Capabilities

End-to-End Managed Services
type: PropertyValue
value reference: Features

Model Registry Capabilities
type: PropertyValue
value reference: ML Ops Features

Inference Latency (Typical)
type: PropertyValue
value reference: Performance

Multi-Cloud Support
type: PropertyValue
value reference: Flexibility

Licensing & Cost (Monthly minimum)
type: PropertyValue
value reference: Pricing

Model Deployment Time
type: PropertyValue
value reference: Performance

Pre-built ML Algorithms
type: PropertyValue
value reference: Features

AutoML Accuracy on Tabular Data
type: PropertyValue
value reference: Performance

Compute Instance Cost (ml.m5.xlarge)
type: PropertyValue
value reference: Pricing

No-code Interface Maturity
type: PropertyValue
value reference: Usability

Feature Store Capability
type: PropertyValue
value reference: MLOps

Microsoft Ecosystem Integration
type: PropertyValue
value reference: Integration

Training Job Monitoring & Debugging
type: PropertyValue
value reference: MLOps

Pre-trained Models Available
type: PropertyValue
value reference: Model Coverage

Minimum Inference Cost
type: PropertyValue
value reference: Pricing

Typical ML Training Cost
type: PropertyValue
value reference: Pricing

Setup Time to First Model Deployment
type: PropertyValue
value reference: Ease of Use

Maximum Single GPU Memory
type: PropertyValue
value reference: Infrastructure

Enterprise Compliance Certifications
type: PropertyValue
value reference: Security & Compliance

Community Size
type: PropertyValue
value reference: Community

Supported ML Model Types
type: PropertyValue
value reference: Capabilities

Inference Throughput (single A100 GPU)
type: PropertyValue
value reference: Performance

Setup Time (basic inference)
type: PropertyValue
value reference: Ease of Use

Cost per Million Tokens (A100, on-demand)
type: PropertyValue
value reference: Cost

Supported Models (major open-source)
type: PropertyValue
value reference: Ecosystem

Training Capabilities
type: PropertyValue
value reference: Features

Enterprise SLA Uptime
type: PropertyValue
value reference: Reliability

Infrastructure Management
type: PropertyValue
value reference: Operations

Community & Documentation
type: PropertyValue
value reference: Support

Model Hub Size
type: PropertyValue
value reference: Resources

Free Tier Cost
type: PropertyValue
value reference: Pricing

Average Model Fine-Tuning Time
type: PropertyValue
value reference: Ease of Use

Enterprise Monitoring/Governance
type: PropertyValue
value reference: Production Features

Monthly Active Users
type: PropertyValue
value reference: User Base

Compute Cost Reduction (Spot Instances)
type: PropertyValue
value reference: Pricing

AWS Integration Depth
type: PropertyValue
value reference: Integration

Development Time for Production Deployment
type: PropertyValue
value reference: Time to Market

Inference Throughput (LLaMA 2 70B, A100 GPU)
type: PropertyValue
value reference: Performance

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value reference: Performance

Deployment Time
type: PropertyValue
value reference: Performance

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value reference: Cost

Model Support (Open-Source LLMs)
type: PropertyValue
value reference: Flexibility

Infrastructure Management Required
type: PropertyValue
value reference: Operations

SLA Availability Guarantee
type: PropertyValue
value reference: Reliability

Enterprise Support Availability
type: PropertyValue
value reference: Support & Licensing

AutoML Accuracy (Tabular Classification)
type: PropertyValue
value reference: Model Performance

Monthly Cost (100 training jobs)
type: PropertyValue
value reference: Pricing

Feature Store Query Latency (p99)
type: PropertyValue
value reference: Performance

Pre-built Industry Models
type: PropertyValue
value reference: Features

Enterprise Market Share
type: PropertyValue
value reference: Adoption

Model Training Parallelization
type: PropertyValue
value reference: Scalability