MLflow vs Dagster
type: Article
description: MLflow is a lightweight experiment tracking and model registry tool optimized for ML workflows, while Dagster is a comprehensive data orchestration platform designed for complex, asset-oriented pipelines. MLflow excels at model management; Dagster excels at workflow orchestration and data lineage.
language: en-US
date published: 2026-06-17T20:42:15.263Z
date created: 2026-06-17T20:42:15.263Z
date modified: 2026-06-17T20:42:15.619Z
content reference time: 2026-06-17T20:42:15.619Z
author: A Versus B
publisher: A Versus B
about: MLflow
about: Dagster
abstract: MLflow is a lightweight experiment tracking and model registry tool optimized for ML workflows, while Dagster is a comprehensive data orchestration platform designed for complex, asset-oriented pipelines. MLflow excels at model management; Dagster excels at workflow orchestration and data lineage.
text: Primary Purpose. Learning Curve. Orchestration Capabilities. Data Lineage Tracking. Community Adoption. Open Source Maturity. Enterprise Support
conclusion text: Choose MLflow if you need rapid experiment tracking, model registry, and serving for ML teams without orchestration complexity—it's the industry standard for ML ops. Choose Dagster if you're building data pipelines with complex dependencies, asset lineage requirements, and need a unified orchestration platform for both ML and data engineering workflows.
additional property: Base Cost
  type: PropertyValue
  value: MLflow: Free | Dagster: N/A
additional property: UI/UX User Rating
  type: PropertyValue
  value: MLflow: 4.2/5 | Dagster: N/A
additional property: Setup Time (First Run)
  type: PropertyValue
  value: MLflow: 45-90 minutes | Dagster: N/A
additional property: Experiment Logging Latency
  type: PropertyValue
  value: MLflow: 15-50ms | Dagster: N/A
additional property: Model Registry Feature
  type: PropertyValue
  value: MLflow: Yes (v1.16+) | Dagster: N/A
additional property: On-Premise Deployment
  type: PropertyValue
  value: MLflow: Yes (full control) | Dagster: N/A
additional property: Pricing (Base Monthly Cost for 5-Person Team)
  type: PropertyValue
  value: MLflow: $0/month (self-hosted) or $200-300 (managed option) | Dagster: N/A
additional property: Setup Time to First Experiment
  type: PropertyValue
  value: MLflow: 120-240 minutes (self-hosted) | Dagster: N/A
additional property: Free Tier Experiment Storage
  type: PropertyValue
  value: MLflow: Unlimited (self-hosted) | Dagster: N/A
additional property: API Standardization
  type: PropertyValue
  value: MLflow: OpenML/OpenAI compliant standards; fully portable | Dagster: N/A
additional property: Built-in Model Registry Maturity
  type: PropertyValue
  value: MLflow: Production-ready since 2020; 6+ years, more basic feature set | Dagster: N/A
additional property: GitHub Community Size
  type: PropertyValue
  value: MLflow: 18,000+ stars (mlflow/mlflow repo) | Dagster: N/A
additional property: Data Residency Control
  type: PropertyValue
  value: MLflow: Full control (on-premise, private cloud) | Dagster: N/A
additional property: Experiment Tracking Dashboard
  type: PropertyValue
  value: MLflow: Yes, built-in web UI with metrics, parameters, artifacts | Dagster: N/A
additional property: Model Registry
  type: PropertyValue
  value: MLflow: Production-grade with staging, annotations, aliases | Dagster: N/A
additional property: Data Pipeline Versioning
  type: PropertyValue
  value: MLflow: Limited; basic artifact tracking | Dagster: N/A
additional property: Storage Backends Supported
  type: PropertyValue
  value: MLflow: 5+ (S3, Azure, GCS, HDFS, local) | Dagster: N/A
additional property: Language Support
  type: PropertyValue
  value: MLflow: Python, R, Java, .NET, REST API | Dagster: N/A
additional property: Git Integration
  type: PropertyValue
  value: MLflow: Limited; separate from Git workflows | Dagster: N/A
additional property: Kubernetes Requirement
  type: PropertyValue
  value: MLflow: Optional (not required) | Dagster: N/A
additional property: Native Orchestration Support
  type: PropertyValue
  value: MLflow: No (requires external tools) | Dagster: N/A
additional property: Distributed Training Support
  type: PropertyValue
  value: MLflow: Manual configuration required | Dagster: N/A
additional property: Model Serving Integration
  type: PropertyValue
  value: MLflow: Basic registry only | Dagster: N/A
additional property: Framework Integrations
  type: PropertyValue
  value: MLflow: 50+ frameworks/tools | Dagster: N/A
additional property: Minimum Required DevOps Knowledge
  type: PropertyValue
  value: MLflow: Beginner (Level 1-2) | Dagster: N/A
additional property: ML Frameworks Supported
  type: PropertyValue
  value: MLflow: 20+ native integrations | Dagster: N/A
additional property: Community Size (GitHub Stars)
  type: PropertyValue
  value: MLflow: 17,500+ stars | Dagster: 9,200+ stars
  value reference: MLflow
additional property: Inference Latency (Typical)
  type: PropertyValue
  value: MLflow: 50-200ms (deployment-dependent) | Dagster: N/A
additional property: Multi-Cloud Support
  type: PropertyValue
  value: MLflow: AWS, Azure, GCP, on-premises | Dagster: N/A
additional property: Licensing & Cost (Monthly minimum)
  type: PropertyValue
  value: MLflow: $0 (free open-source) | Dagster: N/A
additional property: Setup Time
  type: PropertyValue
  value: MLflow: 24-72 hours (self-hosted) | Dagster: N/A
additional property: End-to-End Managed Services
  type: PropertyValue
  value: MLflow: 3-4 core services (tracking, registry, projects) | Dagster: N/A
additional property: Startup Cost
  type: PropertyValue
  value: MLflow: $0 | Dagster: N/A
additional property: Monthly Cost (5-person team, cloud)
  type: PropertyValue
  value: MLflow: $500-2000 (infrastructure estimate) | Dagster: N/A
additional property: Time to Production (first model)
  type: PropertyValue
  value: MLflow: 3-5 days | Dagster: N/A
additional property: Pre-Built Integrations
  type: PropertyValue
  value: MLflow: 50+ | Dagster: 150+
  value reference: Dagster
additional property: Model Registry Features
  type: PropertyValue
  value: MLflow: Version control, staging transitions, metadata storage | Dagster: N/A
additional property: GitHub Stars (Community Adoption)
  type: PropertyValue
  value: MLflow: 19,500 | Dagster: N/A
additional property: Maximum Concurrent Experiments
  type: PropertyValue
  value: MLflow: Unlimited (self-hosted) | Dagster: N/A
additional property: Hyperparameter Optimization Methods
  type: PropertyValue
  value: MLflow: 1 (grid search via plugins) | Dagster: N/A
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: MLflow: 5-10 minutes (pip install mlflow) | Dagster: 15-20
  value reference: MLflow
additional property: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
  value: MLflow: 0 USD (runs locally or 20-50 USD for cloud hosting) | Dagster: $200-500
  value reference: MLflow
additional property: Lines of Code for Integration
  type: PropertyValue
  value: MLflow: 3-10 lines (Python API calls) | Dagster: N/A
additional property: Max Concurrent Jobs on Standard Setup
  type: PropertyValue
  value: MLflow: 10-20 jobs (single machine limited) | Dagster: N/A
additional property: Production Model Deployment Support
  type: PropertyValue
  value: MLflow: REST API, Docker, Spark, cloud-native formats (Azure, AWS SageMaker, GCP Vertex) | Dagster: N/A
additional property: Time to First Successful Experiment Tracking
  type: PropertyValue
  value: MLflow: 5-15 minutes | Dagster: N/A
additional property: Time to First Tracked Experiment
  type: PropertyValue
  value: MLflow: 5-10 minutes | Dagster: 40-90 minutes
  value reference: MLflow
additional property: Model Registry Capabilities
  type: PropertyValue
  value: MLflow: Versioning, staging transitions, production promotion, metadata tracking | Dagster: Manual tracking via assets; no built-in staging workflow
additional property: Asset Lineage Tracking
  type: PropertyValue
  value: MLflow: Run-based tracking; requires manual correlation | Dagster: Native asset graphs with automatic lineage, impact analysis, and upstream/downstream visibility
interactivity type: active
significant link: https://www.aversusb.net/entity/mlflow
significant link: https://www.aversusb.net/alternatives/mlflow
significant link: https://www.aversusb.net/entity/dagster
significant link: https://www.aversusb.net/alternatives/dagster
significant link: https://www.aversusb.net/api/answer/mlflow-vs-dagster
significant link: https://www.aversusb.net/api/v1/schema/mlflow-vs-dagster
related link: https://www.aversusb.net/compare/mlflow-vs-neptune
related link: https://www.aversusb.net/compare/dagster-vs-metaflow
related link: https://www.aversusb.net/compare/dagster-vs-mage
related link: https://www.aversusb.net/compare/mlflow-vs-weights-biases
related link: https://www.aversusb.net/compare/dagster-vs-prefect
related link: https://www.aversusb.net/compare/mlflow-vs-dvc
related link: https://www.aversusb.net/compare/dagster-vs-airbyte
related link: https://www.aversusb.net/compare/kubeflow-vs-mlflow
teaches: How to choose between MLflow and Dagster
genre: Software

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

A Versus B
type: Organization

MLflow
type: SoftwareApplication
alternate name: Mlflow
language: en-US
description: Open-source ML experiment tracking and model management platform by Databricks.
subject of: MLflow vs Dagster

Dagster
type: SoftwareApplication
language: en-US
description: Modern asset-oriented data orchestration platform with first-class data lineage and testability.
subject of: MLflow vs Dagster

MLflow vs Dagster — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for MLflow vs Dagster. Contains 135 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
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  encoding format: application/json
distribution: Structured FAQ pairs JSON
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  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
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  encoding format: application/json
variable measured: Base Cost
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  value reference: Pricing
variable measured: UI/UX User Rating
  type: PropertyValue
  value reference: User Experience
variable measured: Setup Time (First Run)
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  value reference: Ease of Use
variable measured: Experiment Logging Latency
  type: PropertyValue
  value reference: Performance
variable measured: Model Registry Feature
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  value reference: Features
variable measured: On-Premise Deployment
  type: PropertyValue
  value reference: Deployment
variable measured: Pricing (Base Monthly Cost for 5-Person Team)
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variable measured: Setup Time to First Experiment
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variable measured: Free Tier Experiment Storage
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  value reference: Features
variable measured: API Standardization
  type: PropertyValue
  value reference: Integration
variable measured: Built-in Model Registry Maturity
  type: PropertyValue
  value reference: Features
variable measured: GitHub Community Size
  type: PropertyValue
  value reference: Community
variable measured: Data Residency Control
  type: PropertyValue
  value reference: Compliance
variable measured: Experiment Tracking Dashboard
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  value reference: UI & Visualization
variable measured: Model Registry
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variable measured: Data Pipeline Versioning
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variable measured: Storage Backends Supported
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variable measured: Language Support
  type: PropertyValue
  value reference: Capabilities
variable measured: Git Integration
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variable measured: Kubernetes Requirement
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variable measured: Native Orchestration Support
  type: PropertyValue
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variable measured: Distributed Training Support
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variable measured: Model Serving Integration
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variable measured: Framework Integrations
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variable measured: Minimum Required DevOps Knowledge
  type: PropertyValue
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variable measured: ML Frameworks Supported
  type: PropertyValue
  value reference: AI/ML Capabilities
variable measured: Community Size (GitHub Stars)
  type: PropertyValue
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variable measured: Inference Latency (Typical)
  type: PropertyValue
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variable measured: Multi-Cloud Support
  type: PropertyValue
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variable measured: Setup Time
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variable measured: Monthly Cost (5-person team, cloud)
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variable measured: Pre-Built Integrations
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variable measured: Model Registry Features
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variable measured: GitHub Stars (Community Adoption)
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variable measured: Maximum Concurrent Experiments
  type: PropertyValue
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variable measured: Hyperparameter Optimization Methods
  type: PropertyValue
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variable measured: Setup Time (Minutes)
  type: PropertyValue
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variable measured: Minimum Infrastructure Cost (monthly)
  type: PropertyValue
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variable measured: Lines of Code for Integration
  type: PropertyValue
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variable measured: Max Concurrent Jobs on Standard Setup
  type: PropertyValue
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variable measured: Production Model Deployment Support
  type: PropertyValue
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variable measured: Time to First Successful Experiment Tracking
  type: PropertyValue
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variable measured: Time to First Tracked Experiment
  type: PropertyValue
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variable measured: Model Registry Capabilities
  type: PropertyValue
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variable measured: Asset Lineage Tracking
  type: PropertyValue
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variable measured: GitHub Stars
  type: PropertyValue
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variable measured: Minimum Python Version Required
  type: PropertyValue
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variable measured: Maximum Concurrent Runs (single instance)
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variable measured: Type Safety & Validation
  type: PropertyValue
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variable measured: Base Monthly Cost
  type: PropertyValue
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variable measured: Enterprise Tier Monthly Cost
  type: PropertyValue
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variable measured: Initial Setup Time
  type: PropertyValue
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variable measured: Supported ML Frameworks
  type: PropertyValue
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variable measured: Team Collaboration Features
  type: PropertyValue
  value reference: Features
variable measured: Enterprise Support Response Time
  type: PropertyValue
  value reference: Support
variable measured: Minimum Python Version Supported
  type: PropertyValue
  value reference: Compatibility
variable measured: Time to First Pipeline (learning curve)
variable measured: Built-in Data Lineage
variable measured: Native Data Quality Checks
variable measured: Deployment Configurations Supported
variable measured: SaaS Pricing (base tier)
variable measured: First Release Date
variable measured: GitHub Stars (as of 2026)
variable measured: Time to First Working Pipeline (typical)
variable measured: Multi-Tenancy Support
is based on: https://www.aversusb.net/compare/mlflow-vs-dagster

MLflow vs Dagster — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for MLflow vs Dagster.
has defined term: Pricing
has defined term: User Experience
has defined term: Ease of Use
has defined term: Performance
has defined term: Features
has defined term: Deployment
has defined term: Usability
has defined term: Integration
has defined term: Community
has defined term: Compliance
has defined term: UI & Visualization
has defined term: Model Management
has defined term: Data Management
has defined term: Integrations
has defined term: Capabilities
has defined term: Architecture
has defined term: Ecosystem
has defined term: Learning Curve
has defined term: AI/ML Capabilities
has defined term: Infrastructure
has defined term: Developer Experience
has defined term: Setup & Deployment
has defined term: Scalability
has defined term: Cost
has defined term: Production Readiness
has defined term: Time to Value
has defined term: ML Ops Features
has defined term: Data Observability
has defined term: Adoption & Community
has defined term: Technical Requirements
has defined term: Data Quality
has defined term: Support
has defined term: Compatibility
has defined term: Data Governance
has defined term: Maturity
has defined term: Community Adoption
has defined term: Capability
has defined term: Learning Resources
has defined term: Enterprise
has defined term: Requirements
has defined term: Code Quality
has defined term: Implementation Speed
has defined term: Transformation
has defined term: Cost Structure
has defined term: Monitoring
has defined term: Application Domain
has defined term: Learning
has defined term: Adoption
has defined term: Community & Adoption
has defined term: Onboarding
has defined term: Community Activity
has defined term: Community Support
has defined term: Performance & Scale
has defined term: Speed
has defined term: Development
has defined term: Functionality
has defined term: Operations
has defined term: Productivity
has defined term: Integration Ecosystem

Pricing
type: DefinedTerm
term code: pricing
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

Performance
type: DefinedTerm
term code: performance
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Compliance
type: DefinedTerm
term code: compliance
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

UI & Visualization
type: DefinedTerm
term code: ui-&-visualization
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

Data Management
type: DefinedTerm
term code: data-management
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

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

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

Setup & Deployment
type: DefinedTerm
term code: setup-&-deployment
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

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

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

Data Observability
type: DefinedTerm
term code: data-observability
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

Technical Requirements
type: DefinedTerm
term code: technical-requirements
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Data Quality
type: DefinedTerm
term code: data-quality
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Data Governance
type: DefinedTerm
term code: data-governance
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Maturity
type: DefinedTerm
term code: maturity
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Learning Resources
type: DefinedTerm
term code: learning-resources
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Requirements
type: DefinedTerm
term code: requirements
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Code Quality
type: DefinedTerm
term code: code-quality
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Implementation Speed
type: DefinedTerm
term code: implementation-speed
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Transformation
type: DefinedTerm
term code: transformation
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Cost Structure
type: DefinedTerm
term code: cost-structure
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Monitoring
type: DefinedTerm
term code: monitoring
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Application Domain
type: DefinedTerm
term code: application-domain
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Learning
type: DefinedTerm
term code: learning
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

Onboarding
type: DefinedTerm
term code: onboarding
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Community Activity
type: DefinedTerm
term code: community-activity
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Community Support
type: DefinedTerm
term code: community-support
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Performance & Scale
type: DefinedTerm
term code: performance-&-scale
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Speed
type: DefinedTerm
term code: speed
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Development
type: DefinedTerm
term code: development
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

Productivity
type: DefinedTerm
term code: productivity
in defined term set: MLflow vs Dagster — Attribute Category Vocabulary

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

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-06-17T20:42:15.619Z
author: A Versus B
publisher: A Versus B
is part of: mlflow-vs-dagster
main entity: Can MLflow be used for orchestrating data pipelines?
  type: Question
  accepted answer: Answer
    text: MLflow Projects provides basic job scheduling and parameter configuration, but it is not designed for complex DAG orchestration. For multi-step pipelines with dependencies and conditional logic, Dagster is a better choice. MLflow excels at experiment tracking and model management within existing orchestration platforms.
    language: en-US
main entity: Can Dagster track ML experiments like MLflow does?
  type: Question
  accepted answer: Answer
    text: Dagster can log and track experiment metrics through custom I/O managers and event logging, but it does not provide a dedicated experiment registry like MLflow. Many teams use Dagster for orchestration + MLflow for experiment tracking. Alternatively, Dagster integrates with external experiment tracking systems.
    language: en-US
main entity: Which tool is better for production ML model serving?
  type: Question
  accepted answer: Answer
    text: MLflow has native model serving capabilities (MLflow Models, MLflow Serving) with support for multiple frameworks. Dagster is optimized for orchestration and doesn't include built-in serving features. However, Dagster can orchestrate containerized model serving pipelines and integrates with deployment platforms like Kubernetes.
    language: en-US
main entity: Do both tools support cloud deployment?
  type: Question
  accepted answer: Answer
    text: Yes. MLflow integrates natively with Databricks, AWS, GCP, and Azure. Dagster supports Kubernetes, Docker, AWS ECS, and cloud data warehouses (Snowflake, BigQuery, Redshift). Both have managed cloud options through their respective companies (Databricks for MLflow, Dagster Cloud for Dagster).
    language: en-US
main entity: What is the cost difference between MLflow and Dagster?
  type: Question
  accepted answer: Answer
    text: Both offer free open-source versions. MLflow's enterprise features are managed by Databricks with custom pricing. Dagster Cloud offers tiered pricing starting at ~$500/month for small deployments. For self-hosted deployments, both are free with operational costs limited to infrastructure.
    language: en-US

How to Choose Between MLflow and Dagster
type: HowTo
description: MLflow is a lightweight experiment tracking and model registry tool optimized for ML workflows, while Dagster is a comprehensive data orchestration platform designed for complex, asset-oriented pipelines. MLflow excels at model management; Dagster excels at workflow orchestration and data lineage.
language: en-US
total time: PT3M
step: Consider: Primary Purpose
step: Consider: Learning Curve
step: Consider: Orchestration Capabilities
step: Consider: Data Lineage Tracking
step: Consider: Community Adoption
step: Consider: Open Source Maturity
step: Consider: Enterprise Support
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose MLflow if you need rapid experiment tracking, model registry, and serving for ML teams without orchestration complexity—it's the industry standard for ML ops. Choose Dagster if you're building data pipelines with complex dependencies, asset lineage requirements, and need a unified orchestration platform for both ML and data engineering workflows.

Consider: Primary Purpose
type: HowToStep
position: 1
text: Primary Purpose: MLflow has Experiment tracking, model registry, and model serving, while Dagster has Data orchestration, asset management, and workflow automation.

Consider: Learning Curve
type: HowToStep
position: 2
text: Learning Curve: MLflow has Steep for orchestration, shallow for tracking, while Dagster has Steep for configuration, moderate for usage.

Consider: Orchestration Capabilities
type: HowToStep
position: 3
text: Orchestration Capabilities: MLflow has Limited; basic job scheduling via Projects, while Dagster has Comprehensive; native DAG support with 50+ integrations.

Consider: Data Lineage Tracking
type: HowToStep
position: 4
text: Data Lineage Tracking: MLflow has Minimal; tracks model parameters only, while Dagster has Native asset lineage with full dependency graph visualization.

Consider: Community Adoption
type: HowToStep
position: 5
text: Community Adoption: MLflow has 10,000+ GitHub stars, ~2,500 companies using, while Dagster has 5,000+ GitHub stars, ~800 companies using.

Consider: Open Source Maturity
type: HowToStep
position: 6
text: Open Source Maturity: MLflow has Production-ready since 2018, Apache-licensed, while Dagster has Production-ready since 2021, Elastic-licensed core.

Consider: Enterprise Support
type: HowToStep
position: 7
text: Enterprise Support: MLflow has Databricks-backed; enterprise version available, while Dagster has Elementl-backed; commercial support with tiered plans.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: MLflow vs Dagster
review body: MLflow is a lightweight experiment tracking and model registry tool optimized for ML workflows, while Dagster is a comprehensive data orchestration platform designed for complex, asset-oriented pipelines. MLflow excels at model management; Dagster excels at workflow orchestration and data lineage.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: MLflow vs Dagster

GitHub Stars
type: PropertyValue
value: MLflow: 19,000+ stars | Dagster: 9,500+
value reference: MLflow

Minimum Python Version Required
type: PropertyValue
value: MLflow: Python 3.7+ | Dagster: Python 3.8+

Maximum Concurrent Runs (single instance)
type: PropertyValue
value: MLflow: 100-500 (before metadata DB bottleneck) | Dagster: 500+ (optimized for high-volume asset materialization)
value reference: Dagster

Type Safety & Validation
type: PropertyValue
value: MLflow: Manual logging of types; no enforcement | Dagster: Built-in Python type hints with automatic validation and schema enforcement

Base Monthly Cost
type: PropertyValue
value: MLflow: $0 (self-hosted) | Dagster: N/A

Enterprise Tier Monthly Cost
type: PropertyValue
value: MLflow: $0 | Dagster: N/A

Initial Setup Time
type: PropertyValue
value: MLflow: 3-5 hours | Dagster: N/A

Supported ML Frameworks
type: PropertyValue
value: MLflow: 100+ | Dagster: Framework-agnostic via Python ops; requires custom integration

Team Collaboration Features
type: PropertyValue
value: MLflow: 2 (basic sharing, artifacts) | Dagster: N/A

Enterprise Support Response Time
type: PropertyValue
value: MLflow: N/A (community only) | Dagster: N/A

Minimum Python Version Supported
type: PropertyValue
value: MLflow: N/A | Dagster: Python 3.8

Time to First Pipeline (learning curve)
type: PropertyValue
value: MLflow: N/A | Dagster: 45-60 minutes

Built-in Data Lineage
type: PropertyValue
value: MLflow: N/A | Dagster: Automatic and built-in

Native Data Quality Checks
type: PropertyValue
value: MLflow: N/A | Dagster: Yes - Dagster asset checks

Deployment Configurations Supported
type: PropertyValue
value: MLflow: N/A | Dagster: 12+ (K8s, Docker, ECS, serverless)

SaaS Pricing (base tier)
type: PropertyValue
value: MLflow: N/A | Dagster: Free for self-hosted, $99/month for Dagster+

First Release Date
type: PropertyValue
value: MLflow: N/A | Dagster: 2019

GitHub Stars (as of 2026)
type: PropertyValue
value: MLflow: N/A | Dagster: 8,400+

Time to First Working Pipeline (typical)
type: PropertyValue
value: MLflow: N/A | Dagster: 4-6 hours

Multi-Tenancy Support
type: PropertyValue
value: MLflow: N/A | Dagster: Enterprise-grade built-in

Python Version Support
type: PropertyValue
value: MLflow: N/A | Dagster: 3.8+

Supported Deployment Platforms
type: PropertyValue
value: MLflow: N/A | Dagster: 6+ (K8s, Docker, serverless, hybrid)

Documentation Quality (Page Count)
type: PropertyValue
value: MLflow: N/A | Dagster: 800+

Time to First Workflow
type: PropertyValue
value: MLflow: N/A | Dagster: 20-30 minutes

Minimum Code for Basic Workflow
type: PropertyValue
value: MLflow: N/A | Dagster: ~200 lines

Asset Lineage Tracking Coverage
type: PropertyValue
value: MLflow: N/A | Dagster: Native asset-level (100%)

Self-Hosted Feature Parity
type: PropertyValue
value: MLflow: N/A | Dagster: 100% of features

Startup Overhead for Self-Hosted
type: PropertyValue
value: MLflow: N/A | Dagster: 2 CPU / 4GB RAM minimum

Enterprise Governance Features
type: PropertyValue
value: MLflow: N/A | Dagster: ~15+ features (full compliance suite)

Community GitHub Stars
type: PropertyValue
value: MLflow: N/A | Dagster: ~9.2k stars

First Release Year
type: PropertyValue
value: MLflow: N/A | Dagster: 2019

Available Integrations
type: PropertyValue
value: MLflow: N/A | Dagster: 50+

Minimum Python Version
type: PropertyValue
value: MLflow: N/A | Dagster: 3.9+

Type Safety Feature
type: PropertyValue
value: MLflow: N/A | Dagster: Built-in Dagster Types with validation

Automatic Lineage Detection
type: PropertyValue
value: MLflow: N/A | Dagster: Yes, native support

Managed Cloud SLA
type: PropertyValue
value: MLflow: N/A | Dagster: 99.9%

Pre-built Connectors
type: PropertyValue
value: MLflow: N/A | Dagster: ~50 connectors

Minimum Time to First Data Pipeline
type: PropertyValue
value: MLflow: N/A | Dagster: 8-24 hours

Orchestration Complexity Support
type: PropertyValue
value: MLflow: N/A | Dagster: Enterprise-grade (dynamic, conditional)

Required Technical Skill Level
type: PropertyValue
value: MLflow: N/A | Dagster: Advanced (Python/software engineering)

Data Transformation Capabilities
type: PropertyValue
value: MLflow: N/A | Dagster: 9/10 (advanced custom logic)

Cloud Platform Pricing Model
type: PropertyValue
value: MLflow: N/A | Dagster: Usage-based (compute units)

Asset Lineage & Observability
type: PropertyValue
value: MLflow: N/A | Dagster: Native, built-in with asset graph

Built-in Orchestration Engine
type: PropertyValue
value: MLflow: N/A | Dagster: Yes - native DAG, scheduling, dynamic branching

Cloud Pricing (per compute unit/seat)
type: PropertyValue
value: MLflow: N/A | Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week)

Primary Use Case
type: PropertyValue
value: MLflow: N/A | Dagster: End-to-end pipeline orchestration and execution

Data Warehouse Integrations (Native)
type: PropertyValue
value: MLflow: N/A | Dagster: Cloud-agnostic; works with 15+ systems

Time to Competency (for SQL analysts)
type: PropertyValue
value: MLflow: N/A | Dagster: 40-60 hours (requires Python learning)

GitHub Community Stars
type: PropertyValue
value: MLflow: N/A | Dagster: 9,800+ stars

Enterprise Adoption (tracked companies)
type: PropertyValue
value: MLflow: N/A | Dagster: 2,000+ (estimated from public case studies)

GitHub Stars (Community Size)
type: PropertyValue
value: MLflow: N/A | Dagster: 9,200+

Time Since First Release
type: PropertyValue
value: MLflow: N/A | Dagster: 5 years (2019)

Built-in Data Quality Testing
type: PropertyValue
value: MLflow: N/A | Dagster: Native assertions & sensors

Data Lineage Model
type: PropertyValue
value: MLflow: N/A | Dagster: Asset-centric with lineage tracking

Estimated Learning Curve (Hours to Productivity)
type: PropertyValue
value: MLflow: N/A | Dagster: 40-60 hours

Active Contributors (Monthly)
type: PropertyValue
value: MLflow: N/A | Dagster: 40+

Production Deployments (Estimated)
type: PropertyValue
value: MLflow: N/A | Dagster: 500+

Provider/Integration Count
type: PropertyValue
value: MLflow: N/A | Dagster: ~50

Type Safety Support
type: PropertyValue
value: MLflow: N/A | Dagster: Strong (compile-time + runtime)

Community Slack Members
type: PropertyValue
value: MLflow: N/A | Dagster: 2,500+

Built-in Provider Integrations
type: PropertyValue
value: MLflow: N/A | Dagster: 50+

First Official Release
type: PropertyValue
value: MLflow: N/A | Dagster: 2018

Native Asset Lineage Tracking
type: PropertyValue
value: MLflow: N/A | Dagster: Full asset-level lineage

Type Safety Features
type: PropertyValue
value: MLflow: N/A | Dagster: First-class type definitions

Learning Curve Time (Average)
type: PropertyValue
value: MLflow: N/A | Dagster: 3-4 weeks to proficiency

Enterprise SaaS Option Available
type: PropertyValue
value: MLflow: N/A | Dagster: Dagster Cloud (official)

Maximum Daily Task Executions (Tested)
type: PropertyValue
value: MLflow: N/A | Dagster: 100K+ (typical deployments)

Pre-built Data Connectors
type: PropertyValue
value: MLflow: N/A | Dagster: 50+ connectors

Minimum Learning Curve (1-10 scale)
type: PropertyValue
value: MLflow: N/A | Dagster: 7/10 (Python required)

Time to Deploy First Integration
type: PropertyValue
value: MLflow: N/A | Dagster: 24-48 hours (development needed)

Native dbt Integration
type: PropertyValue
value: MLflow: N/A | Dagster: Full native integration

Typical Time to Build Custom Connector
type: PropertyValue
value: MLflow: N/A | Dagster: 5-10 days

Number of Integrations
type: PropertyValue
value: MLflow: N/A | Dagster: 100+ (Python, Spark, K8s, APIs, databases, cloud)

Learning Curve (Developer Hours)
type: PropertyValue
value: MLflow: N/A | Dagster: 40-80 hours (requires Python, orchestration concepts)

Native Scheduling Support
type: PropertyValue
value: MLflow: N/A | Dagster: Yes - built-in with Dagster Daemon

Native Monitoring & Alerting
type: PropertyValue
value: MLflow: N/A | Dagster: Yes - built-in monitoring, alerting, and error tracking

Supported Programming Languages
type: PropertyValue
value: MLflow: N/A | Dagster: Python, SQL, Spark, Go, shell scripts, APIs

Average Time to Deploy First Pipeline
type: PropertyValue
value: MLflow: N/A | Dagster: 15-25 hours (setup + learning)

dbt Package Ecosystem Size
type: PropertyValue
value: MLflow: N/A | Dagster: Not applicable

Initial Release
type: PropertyValue
value: MLflow: N/A | Dagster: 2019

Production Organizations (Reported)
type: PropertyValue
value: MLflow: N/A | Dagster: 2,000+

Available Providers/Operators
type: PropertyValue
value: MLflow: N/A | Dagster: Limited (community growing)

Native Data Lineage Support
type: PropertyValue
value: MLflow: N/A | Dagster: Yes (first-class asset lineage)

Built-in Testing Framework
type: PropertyValue
value: MLflow: N/A | Dagster: Yes (comprehensive in-process testing)

Python Type Safety Support
type: PropertyValue
value: MLflow: N/A | Dagster: Strong (enforced type hints)

Learning Curve Steepness
type: PropertyValue
value: MLflow: N/A | Dagster: Moderate-to-High (asset-oriented paradigm)

SpeakableSpecification
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MLflow — Wikipedia
type: CreativeWork

Dagster — Wikipedia
type: CreativeWork

Compare MLflow vs Dagster
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/mlflow-vs-dagster
object: MLflow
object: Dagster

Time to First Pipeline (learning curve)
type: PropertyValue
value reference: Ease of Use

Built-in Data Lineage
type: PropertyValue
value reference: Features

Native Data Quality Checks
type: PropertyValue
value reference: Data Governance

Deployment Configurations Supported
type: PropertyValue
value reference: Deployment

SaaS Pricing (base tier)
type: PropertyValue
value reference: Pricing

First Release Date
type: PropertyValue
value reference: Maturity

GitHub Stars (as of 2026)
type: PropertyValue
value reference: Community Adoption

Time to First Working Pipeline (typical)
type: PropertyValue
value reference: Developer Experience

Multi-Tenancy Support
type: PropertyValue
value reference: Features

Python Version Support
type: PropertyValue
value reference: Compatibility

Supported Deployment Platforms
type: PropertyValue
value reference: Capability

Documentation Quality (Page Count)
type: PropertyValue
value reference: Learning Resources

Time to First Workflow
type: PropertyValue
value reference: Developer Experience

Minimum Code for Basic Workflow
type: PropertyValue
value reference: Developer Experience

Asset Lineage Tracking Coverage
type: PropertyValue
value reference: Data Governance

Self-Hosted Feature Parity
type: PropertyValue
value reference: Deployment

Startup Overhead for Self-Hosted
type: PropertyValue
value reference: Infrastructure

Enterprise Governance Features
type: PropertyValue
value reference: Enterprise

Community GitHub Stars
type: PropertyValue
value reference: Community

First Release Year
type: PropertyValue
value reference: Maturity

Available Integrations
type: PropertyValue
value reference: Ecosystem

Minimum Python Version
type: PropertyValue
value reference: Requirements

Type Safety Feature
type: PropertyValue
value reference: Code Quality

Automatic Lineage Detection
type: PropertyValue
value reference: Features

Managed Cloud SLA
type: PropertyValue
value reference: Production Readiness

Pre-built Connectors
type: PropertyValue
value reference: Integration

Minimum Time to First Data Pipeline
type: PropertyValue
value reference: Implementation Speed

Orchestration Complexity Support
type: PropertyValue
value reference: Features

Required Technical Skill Level
type: PropertyValue
value reference: Ease of Use

Data Transformation Capabilities
type: PropertyValue
value reference: Transformation

Cloud Platform Pricing Model
type: PropertyValue
value reference: Cost Structure

Asset Lineage & Observability
type: PropertyValue
value reference: Monitoring

Built-in Orchestration Engine
type: PropertyValue
value reference: Architecture

Cloud Pricing (per compute unit/seat)
type: PropertyValue
value reference: Cost

Primary Use Case
type: PropertyValue
value reference: Application Domain

Data Warehouse Integrations (Native)
type: PropertyValue
value reference: Integration

Time to Competency (for SQL analysts)
type: PropertyValue
value reference: Learning

GitHub Community Stars
type: PropertyValue
value reference: Community

Enterprise Adoption (tracked companies)
type: PropertyValue
value reference: Adoption

GitHub Stars (Community Size)
type: PropertyValue
value reference: Community & Adoption

Time Since First Release
type: PropertyValue
value reference: Maturity

Built-in Data Quality Testing
type: PropertyValue
value reference: Features

Data Lineage Model
type: PropertyValue
value reference: Architecture

Estimated Learning Curve (Hours to Productivity)
type: PropertyValue
value reference: Onboarding

Active Contributors (Monthly)
type: PropertyValue
value reference: Community Activity

Production Deployments (Estimated)
type: PropertyValue
value reference: Adoption

Provider/Integration Count
type: PropertyValue
value reference: Ecosystem

Type Safety Support
type: PropertyValue
value reference: Developer Experience

Community Slack Members
type: PropertyValue
value reference: Community Support

Built-in Provider Integrations
type: PropertyValue
value reference: Ecosystem

First Official Release
type: PropertyValue
value reference: Maturity

Native Asset Lineage Tracking
type: PropertyValue
value reference: Data Governance

Type Safety Features
type: PropertyValue
value reference: Developer Experience

Learning Curve Time (Average)
type: PropertyValue
value reference: Developer Experience

Enterprise SaaS Option Available
type: PropertyValue
value reference: Deployment

Maximum Daily Task Executions (Tested)
type: PropertyValue
value reference: Performance & Scale

Pre-built Data Connectors
type: PropertyValue
value reference: Integration

Minimum Learning Curve (1-10 scale)
type: PropertyValue
value reference: Usability

Time to Deploy First Integration
type: PropertyValue
value reference: Speed

Native dbt Integration
type: PropertyValue
value reference: Transformation

Typical Time to Build Custom Connector
type: PropertyValue
value reference: Development

Number of Integrations
type: PropertyValue
value reference: Functionality

Learning Curve (Developer Hours)
type: PropertyValue
value reference: Ease of Use

Native Scheduling Support
type: PropertyValue
value reference: Operations

Native Monitoring & Alerting
type: PropertyValue
value reference: Operations

Supported Programming Languages
type: PropertyValue
value reference: Compatibility

Average Time to Deploy First Pipeline
type: PropertyValue
value reference: Productivity

dbt Package Ecosystem Size
type: PropertyValue
value reference: Ecosystem

Initial Release
type: PropertyValue
value reference: Maturity

Production Organizations (Reported)
type: PropertyValue
value reference: Adoption

Available Providers/Operators
type: PropertyValue
value reference: Integration Ecosystem

Native Data Lineage Support
type: PropertyValue
value reference: Features

Built-in Testing Framework
type: PropertyValue
value reference: Developer Experience

Python Type Safety Support
type: PropertyValue
value reference: Code Quality

Learning Curve Steepness
type: PropertyValue
value reference: Ease of Use