Decision comparison
Mage vs Dagster
Mage and Dagster both serve modern data teams building Python-based pipelines, but they take fundamentally different approaches. Mage prioritizes accessibility with its notebook UI, AI-assisted development, and modular block-based architecture, making it a strong choice for teams that want to move fast with an interactive development experience. Dagster focuses on asset-centric orchestration with deep lineage, a built-in data catalog, and enterprise governance, making it the better fit for teams managing complex, interconnected data assets at scale.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are workflow orchestrators.
Quick Comparison
| Decision factor | Mage | Dagster |
|---|---|---|
| Best For | Teams wanting a notebook-style interface for building modular data pipelines with AI-assisted development | Data teams building asset-centric pipelines that need lineage, observability, and a full data catalog |
| Architecture | Modular pipeline runtime with isolated blocks, explicit inputs and outputs, and built-in AI sidekick | Asset-centric orchestrator with declarative workflows, dependency graphs, and built-in data catalog |
| Pricing Model | Mage publishes one self-serve option at $100/month plus usage (one development environment, unlimited users, usage-based infrastructure), with usage at $0.50 per CPU core-hour and $0.50 per 4 GB RAM-hour. The Annual plan is quoted by sales. Open-source mage-ai is Apache-2.0 and free to self-host. | Open-source self-hosted free (Apache-2.0), Solo Plan $10/mo, Starter Plan $100/mo, Starter $1200/mo, Pro and Enterprise Plan contact sales |
| Open Source | Apache-2.0 license with 8,500+ GitHub stars and active Python-based open-source community | Apache-2.0 license with 16,000+ GitHub stars and a large ecosystem of integrations and plugins |
| Learning Curve | Approachable notebook UI lowers the barrier for data engineers familiar with Python and SQL | Steeper initial ramp-up due to asset-centric paradigm, but strong documentation and Dagster University |
| Enterprise Readiness | SOC2 Type II certified with hybrid cloud, private cloud, and on-premises deployment options | SOC 2 Type II and HIPAA compliant with SSO, RBAC, SCIM, audit logs, and multi-tenant deployments |
Mage
- Best For:
- Teams wanting a notebook-style interface for building modular data pipelines with AI-assisted development
- Architecture:
- Modular pipeline runtime with isolated blocks, explicit inputs and outputs, and built-in AI sidekick
- Pricing Model:
- Mage publishes one self-serve option at $100/month plus usage (one development environment, unlimited users, usage-based infrastructure), with usage at $0.50 per CPU core-hour and $0.50 per 4 GB RAM-hour. The Annual plan is quoted by sales. Open-source mage-ai is Apache-2.0 and free to self-host.
- Open Source:
- Apache-2.0 license with 8,500+ GitHub stars and active Python-based open-source community
- Learning Curve:
- Approachable notebook UI lowers the barrier for data engineers familiar with Python and SQL
- Enterprise Readiness:
- SOC2 Type II certified with hybrid cloud, private cloud, and on-premises deployment options
Dagster
- Best For:
- Data teams building asset-centric pipelines that need lineage, observability, and a full data catalog
- Architecture:
- Asset-centric orchestrator with declarative workflows, dependency graphs, and built-in data catalog
- Pricing Model:
- Open-source self-hosted free (Apache-2.0), Solo Plan $10/mo, Starter Plan $100/mo, Starter $1200/mo, Pro and Enterprise Plan contact sales
- Open Source:
- Apache-2.0 license with 16,000+ GitHub stars and a large ecosystem of integrations and plugins
- Learning Curve:
- Steeper initial ramp-up due to asset-centric paradigm, but strong documentation and Dagster University
- Enterprise Readiness:
- SOC 2 Type II and HIPAA compliant with SSO, RBAC, SCIM, audit logs, and multi-tenant deployments
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Mage | Dagster |
|---|---|---|
| Docker Hub pulls(Product adoption) | 3.7M | Not available |
| GitHub commits, 90d(Product adoption) | 16 | 265 |
| GitHub stars(Product adoption) | 8,500+ | 16,000+ |
| Search interest(Market interest) | Not available | 1 |
| Product Hunt comments(Community interest) | 748 | 11 |
| Product Hunt rating(Community interest) | 5.0/5 | 5.0/5 |
| Product Hunt reviews(Community interest) | 8 | 1 |
| Product Hunt votes(Community interest) | 595 | 112 |
| PyPI weekly downloads(Product adoption) | 3.1k | 1.8M |
| Docker Hub pulls(Developer adoption) | Not available | 6.2M |
| Hacker News mentions, 90d(Community interest) | Not available | 3 |
| Stack Overflow questions(Community interest) | Not available | 171 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Mage
September 19, 2026Package vulnerabilities
PyPI · mage-ai@0.9.79
0 vulnerabilities
across 1 package
Repository security score
Not available
Dagster
September 21, 2026Package vulnerabilities
PyPI · dagster@1.13.23
0 vulnerabilities
across 1 package
Repository security score
github.com/dagster-io/dagster
5.1/10
Interface Preview
Mage
Dagster
Feature Comparison
| Feature | Mage | Dagster |
|---|---|---|
| Pipeline Development | ||
| Pipeline Authoring Interface | Interactive notebook UI with visual block-based editing and AI-assisted code generation | Code-first Python API with declarative asset definitions and software-defined assets |
| dbt Integration | Built-in dbt support for running dbt models as pipeline blocks within workflows | Native dbt integration with asset mapping, automatic lineage tracking, and orchestration |
| Testing and CI/CD | Built-in testing and validation within the platform for pipeline correctness | First-class unit testing, local development, branch deployments, and CI/CD-native workflow |
| Orchestration and Scheduling | ||
| Orchestration Model | Task-based modular pipeline execution with isolated blocks and targeted failure recovery | Asset-centric orchestration with dependency graphs, partitions, and incremental materialization |
| Failure Recovery | Targeted reprocessing with replay capability and preserved execution state for fast recovery | Fault-tolerant execution with automatic retries, partial re-runs, and dependency-aware recovery |
| Streaming Support | Native batch, sync, and streaming ingestion with schema-aware validation | Primarily batch-oriented with sensor-driven scheduling for near-real-time use cases |
| Observability and Monitoring | ||
| Data Lineage | Pipeline-level lineage through explicit block inputs and outputs within workflows | Full asset-level lineage with dependency graphs, cross-pipeline tracking, and auto-generated documentation |
| Data Catalog | No built-in data catalog; relies on external tools for asset discovery | Integrated data catalog with asset metadata, ownership, search, and auto-generated documentation |
| Monitoring and Alerting | Execution history and run state tracking with centralized observability across workspaces | Built-in alerting via Slack, health metrics dashboards, freshness tracking, and AI-powered debugging |
| Deployment and Infrastructure | ||
| Deployment Options | Managed cloud, hybrid cloud, private cloud, and on-premises with zero-infrastructure managed option | Self-hosted open-source, managed Dagster Cloud, hybrid deployments, and Kubernetes support |
| Multi-Tenancy | Multi-tenant workspaces with environment isolation for collaborative team development | Multi-tenant code deployments with data isolation, unlimited deployments on higher tiers |
| Scalability | Horizontal scaling with multiple clusters and configurable compute resources per plan tier | Kubernetes-native scaling with unlimited code locations and deployments on Pro and Enterprise plans |
| AI and Integrations | ||
| AI Capabilities | Built-in AI sidekick for code generation, debugging, and natural language workflow creation with up to 2M tokens | AI-driven data engineering workflows, Compass for AI analytics, and support for ML pipeline orchestration |
| Ecosystem Integrations | Integrations with databases, warehouses, lakes, SaaS tools, and APIs with built-in connector library | Native integrations with Snowflake, BigQuery, dbt, Databricks, Fivetran, Spark, and Dagster Pipes for external systems |
| Security and Compliance | SOC2 Type II certified with enterprise-grade security across all deployment configurations | SOC 2 Type II, HIPAA compliant with SSO, RBAC, SCIM provisioning, and audit log retention policies |
Pipeline Development
Pipeline Authoring Interface
dbt Integration
Testing and CI/CD
Orchestration and Scheduling
Orchestration Model
Failure Recovery
Streaming Support
Observability and Monitoring
Data Lineage
Data Catalog
Monitoring and Alerting
Deployment and Infrastructure
Deployment Options
Multi-Tenancy
Scalability
AI and Integrations
AI Capabilities
Ecosystem Integrations
Security and Compliance
Which to choose
Mage and Dagster both serve modern data teams building Python-based pipelines, but they take fundamentally different approaches. Mage prioritizes accessibility with its notebook UI, AI-assisted development, and modular block-based architecture, making it a strong choice for teams that want to move fast with an interactive development experience. Dagster focuses on asset-centric orchestration with deep lineage, a built-in data catalog, and enterprise governance, making it the better fit for teams managing complex, interconnected data assets at scale.
Best-fit scenarios
Choose Mage if:
We recommend Mage for data teams that value an interactive, notebook-style development experience and want AI-assisted pipeline building out of the box. Mage works well for teams migrating from Jupyter-based workflows who need a production-grade runtime with modular pipelines, visual debugging, and fast iteration cycles. Its streaming support and flexible deployment options also make it a strong pick for teams handling mixed batch and streaming workloads without heavy infrastructure investment.
Choose Dagster if:
We recommend Dagster for data teams that need comprehensive asset management, deep lineage tracking, and a built-in data catalog to maintain visibility across a growing data platform. Dagster is the stronger choice for organizations running complex, multi-team data operations where governance, compliance, and observability are critical requirements. Its free open-source tier, extensive integration ecosystem with Snowflake, dbt, and Databricks, and Dagster University onboarding resources make it particularly compelling for teams scaling from a small setup to a full enterprise data platform.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Is Mage or Dagster better for small data teams just getting started?
For small teams, both tools have accessible entry points, but they differ in approach. Dagster offers a completely free open-source self-hosted option under the Apache-2.0 license, plus a Solo plan at $10/mo for managed cloud, making it extremely affordable to get started. Mage's managed cloud begins at $100/mo plus compute costs. However, Mage's notebook-style UI and AI sidekick can accelerate onboarding for teams that prefer interactive development. If budget is the primary concern, Dagster's free tier gives it a clear advantage. If your team wants a guided, visual development experience from day one, Mage delivers that with minimal setup.
How do Mage and Dagster compare for dbt integration?
Both platforms offer solid dbt integration, but Dagster's implementation is deeper. Dagster maps dbt models directly to Dagster assets, automatically generating lineage graphs and enabling orchestration of dbt runs alongside other data assets in a unified dependency graph. Mage supports dbt as pipeline blocks within its modular workflow system, letting you run dbt models as part of a broader pipeline alongside Python and SQL transformations. For teams where dbt is central to the data stack and lineage visibility is critical, Dagster provides a more tightly coupled experience. For teams using dbt as one of several pipeline components, Mage's block-based integration works well.
Can Mage and Dagster handle real-time streaming data?
Mage has a broader streaming story out of the box. It supports native batch, sync, and streaming ingestion with schema-aware validation and continuous data processing as data arrives. Dagster is primarily designed for batch orchestration and uses sensors and schedules for near-real-time processing, but it is not a streaming-first platform. If your workload requires true streaming ingestion and transformation, Mage offers more built-in capabilities. For teams whose primary needs are batch ETL and ELT with occasional low-latency requirements, Dagster's sensor-driven approach handles those use cases effectively without needing a separate streaming tool.
Which platform has better enterprise security and compliance features?
Dagster has a more comprehensive enterprise security and compliance feature set. It offers SOC 2 Type II and HIPAA compliance, SSO with support for Google, GitHub, and SAML identity providers, RBAC with SCIM provisioning, audit logs with retention policies, and multi-tenant instance isolation. Mage is SOC2 Type II certified and provides enterprise-grade security across managed, hybrid, private, and on-premises deployments. Both platforms support deployment into your own infrastructure for data sovereignty. For organizations in regulated industries like healthcare or finance where HIPAA compliance and fine-grained access controls are mandatory, Dagster's broader compliance certifications make it the safer choice.