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.
MLflow vs Dagster — Side-by-side comparison on A Versus B
Open-source ML experiment tracking and model management platform by Databricks.
Modern asset-oriented data orchestration platform with first-class data lineage and testability.
Primary Purpose. Learning Curve. Orchestration Capabilities. Data Lineage Tracking. Community Adoption. Open Source Maturity. Enterprise Support
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.
MLflow: Yes (full control) | Dagster: N/A
Pricing (Base Monthly Cost for 5-Person Team)
MLflow: $0/month (self-hosted) or $200-300 (managed option) | Dagster: N/A
MLflow: 120-240 minutes (self-hosted) | Dagster: N/A
MLflow: Unlimited (self-hosted) | Dagster: N/A
MLflow: OpenML/OpenAI compliant standards; fully portable | Dagster: N/A
MLflow: Production-ready since 2020; 6+ years, more basic feature set | Dagster: N/A
MLflow: 18,000+ stars (mlflow/mlflow repo) | Dagster: N/A
MLflow: Full control (on-premise, private cloud) | Dagster: N/A
MLflow: Yes, built-in web UI with metrics, parameters, artifacts | Dagster: N/A
MLflow: Production-grade with staging, annotations, aliases | Dagster: N/A
MLflow: Limited; basic artifact tracking | Dagster: N/A
MLflow: 5+ (S3, Azure, GCS, HDFS, local) | Dagster: N/A
MLflow: Python, R, Java, .NET, REST API | Dagster: N/A
MLflow: Limited; separate from Git workflows | Dagster: N/A
MLflow: Optional (not required) | Dagster: N/A
MLflow: No (requires external tools) | Dagster: N/A
MLflow: Manual configuration required | Dagster: N/A
MLflow: Basic registry only | Dagster: N/A
MLflow: 50+ frameworks/tools | Dagster: N/A
MLflow: Beginner (Level 1-2) | Dagster: N/A
MLflow: 20+ native integrations | Dagster: N/A
MLflow: 17,500+ stars | Dagster: 9,200+ stars
MLflow: 50-200ms (deployment-dependent) | Dagster: N/A
MLflow: AWS, Azure, GCP, on-premises | Dagster: N/A
MLflow: $0 (free open-source) | Dagster: N/A
MLflow: 24-72 hours (self-hosted) | Dagster: N/A
MLflow: 3-4 core services (tracking, registry, projects) | Dagster: N/A
MLflow: $500-2000 (infrastructure estimate) | Dagster: N/A
MLflow: Version control, staging transitions, metadata storage | Dagster: N/A
MLflow: 1 (grid search via plugins) | Dagster: N/A
MLflow: 5-10 minutes (pip install mlflow) | Dagster: 15-20
MLflow: 0 USD (runs locally or 20-50 USD for cloud hosting) | Dagster: $200-500
MLflow: 3-10 lines (Python API calls) | Dagster: N/A
MLflow: 10-20 jobs (single machine limited) | Dagster: N/A
MLflow: REST API, Docker, Spark, cloud-native formats (Azure, AWS SageMaker, GCP Vertex) | Dagster: N/A
Time to First Successful Experiment Tracking
MLflow: 5-10 minutes | Dagster: 40-90 minutes
MLflow: Versioning, staging transitions, production promotion, metadata tracking | Dagster: Manual tracking via assets; no built-in staging workflow
MLflow: Run-based tracking; requires manual correlation | Dagster: Native asset graphs with automatic lineage, impact analysis, and upstream/downstream visibility
MLflow: Python 3.7+ | Dagster: Python 3.8+
Maximum Concurrent Runs (single instance)
MLflow: 100-500 (before metadata DB bottleneck) | Dagster: 500+ (optimized for high-volume asset materialization)
MLflow: Manual logging of types; no enforcement | Dagster: Built-in Python type hints with automatic validation and schema enforcement
MLflow: 100+ | Dagster: Framework-agnostic via Python ops; requires custom integration
MLflow: 2 (basic sharing, artifacts) | Dagster: N/A
MLflow: N/A (community only) | Dagster: N/A
MLflow: N/A | Dagster: Automatic and built-in
MLflow: N/A | Dagster: Yes - Dagster asset checks
MLflow: N/A | Dagster: 12+ (K8s, Docker, ECS, serverless)
MLflow: N/A | Dagster: Free for self-hosted, $99/month for Dagster+
Time to First Working Pipeline (typical)
MLflow: N/A | Dagster: Enterprise-grade built-in
MLflow: N/A | Dagster: 6+ (K8s, Docker, serverless, hybrid)
MLflow: N/A | Dagster: Native asset-level (100%)
MLflow: N/A | Dagster: 2 CPU / 4GB RAM minimum
MLflow: N/A | Dagster: ~15+ features (full compliance suite)
MLflow: N/A | Dagster: Built-in Dagster Types with validation
MLflow: N/A | Dagster: Yes, native support
MLflow: N/A | Dagster: Enterprise-grade (dynamic, conditional)
MLflow: N/A | Dagster: Advanced (Python/software engineering)
MLflow: N/A | Dagster: 9/10 (advanced custom logic)
MLflow: N/A | Dagster: Usage-based (compute units)
MLflow: N/A | Dagster: Native, built-in with asset graph
MLflow: N/A | Dagster: Yes - native DAG, scheduling, dynamic branching
MLflow: N/A | Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week)
MLflow: N/A | Dagster: End-to-end pipeline orchestration and execution
MLflow: N/A | Dagster: Cloud-agnostic; works with 15+ systems
MLflow: N/A | Dagster: 40-60 hours (requires Python learning)
MLflow: N/A | Dagster: 2,000+ (estimated from public case studies)
MLflow: N/A | Dagster: Native assertions & sensors
MLflow: N/A | Dagster: Asset-centric with lineage tracking
Estimated Learning Curve (Hours to Productivity)
MLflow: N/A | Dagster: Strong (compile-time + runtime)
MLflow: N/A | Dagster: Full asset-level lineage
MLflow: N/A | Dagster: First-class type definitions
MLflow: N/A | Dagster: 3-4 weeks to proficiency
MLflow: N/A | Dagster: Dagster Cloud (official)
MLflow: N/A | Dagster: 100K+ (typical deployments)
MLflow: N/A | Dagster: 7/10 (Python required)
MLflow: N/A | Dagster: 24-48 hours (development needed)
MLflow: N/A | Dagster: Full native integration
MLflow: N/A | Dagster: 100+ (Python, Spark, K8s, APIs, databases, cloud)
MLflow: N/A | Dagster: 40-80 hours (requires Python, orchestration concepts)
MLflow: N/A | Dagster: Yes - built-in with Dagster Daemon
MLflow: N/A | Dagster: Yes - built-in monitoring, alerting, and error tracking
MLflow: N/A | Dagster: Python, SQL, Spark, Go, shell scripts, APIs
MLflow: N/A | Dagster: 15-25 hours (setup + learning)
MLflow: N/A | Dagster: Limited (community growing)
MLflow: N/A | Dagster: Yes (first-class asset lineage)
MLflow: N/A | Dagster: Yes (comprehensive in-process testing)
MLflow: N/A | Dagster: Strong (enforced type hints)
MLflow: N/A | Dagster: Moderate-to-High (asset-oriented paradigm)
How to choose between MLflow and Dagster
MLflow vs Dagster — Structured Comparison Data
Machine-readable comparison dataset for MLflow vs Dagster. Contains 135 attribute comparisons, 5 FAQ pairs.
MLflow vs Dagster — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for MLflow vs Dagster.
Can MLflow be used for orchestrating data pipelines?
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.
Can Dagster track ML experiments like MLflow does?
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.
Which tool is better for production ML model serving?
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.
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).
What is the cost difference between MLflow and Dagster?
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.
How to Choose Between MLflow and Dagster
Primary Purpose: MLflow has Experiment tracking, model registry, and model serving, while Dagster has Data orchestration, asset management, and workflow automation.
Learning Curve: MLflow has Steep for orchestration, shallow for tracking, while Dagster has Steep for configuration, moderate for usage.
Orchestration Capabilities: MLflow has Limited; basic job scheduling via Projects, while Dagster has Comprehensive; native DAG support with 50+ integrations.
Data Lineage Tracking: MLflow has Minimal; tracks model parameters only, while Dagster has Native asset lineage with full dependency graph visualization.
Community Adoption: MLflow has 10,000+ GitHub stars, ~2,500 companies using, while Dagster has 5,000+ GitHub stars, ~800 companies using.
Open Source Maturity: MLflow has Production-ready since 2018, Apache-licensed, while Dagster has Production-ready since 2021, Elastic-licensed core.
Enterprise Support: MLflow has Databricks-backed; enterprise version available, while Dagster has Elementl-backed; commercial support with tiered plans.