Buying snapshot
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| Decision point | What Dagster states |
|---|---|
Open-source license | Apache 2.0. |
Python support | Python 3.9 through 3.14. Install from PyPI, for example with uv add dagster dagster-webserver dagster-dg-cli. |
Dagster+ deployment | Serverless, or Hybrid on ECS, Kubernetes, Docker, Azure or local machines. With Hybrid, compute runs in your infrastructure and Dagster manages the control plane. Terraform and Pulumi providers are listed on every plan. |
Integrations | Native integrations with dbt, Snowflake and Fivetran; works with Python scripts, Spark, Databricks, Azure and AWS. Dagster Pipes adds observability and metadata tracking for jobs that run in external systems. |
Compliance | SOC 2 Type II, GDPR and data portability are listed on every Dagster+ plan. |
Our take
Choose Dagster when your team wants to manage tables, dbt models and machine learning models as code-defined assets, with lineage and health checks in one place, and is ready to write its pipelines in Python.
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Popular comparisons
See 20 Dagster comparisons
Where it stands out
Assets, not just tasks. Dagster attaches lineage, quality signals and dependency context to every asset, so when something fails it can show what broke, why and what depends on it. This suits teams whose real question is the state of a table or model, not only whether a job finished.
Data quality and catalog on every plan. Asset checks, freshness policies, dbt test integration, asset health status, asset metadata and asset-level lineage are listed for all Dagster+ plans. Column-level lineage needs Starter or Pro.
Safer changes before production. Dagster is built for every stage from local development and tests to staging and production. Branch deployments check pipeline changes in a production-like environment before they touch real data, and the dg CLI, reusable components, declarative automation, and partitions and backfills are listed on every plan.
Product screenshot
Trade-offs to weigh
Enterprise controls need Pro, which is sold through sales. The plan comparison lists SSO, SAML, SCIM provisioning for Okta and Entra ID, audit logs, HIPAA, EU data residency, SIEM log export, multi-region failover through agent queues, and webhook, PagerDuty and Datadog alerts only for Pro.
Bills follow pipeline activity, not data volume. Because every materialization and every op counts, frequent schedules and many small assets or ops raise usage even when little data moves.
Fine-grained roles need Pro. Starter scopes roles per deployment and includes one deployment, so a role applies across that whole deployment; granular roles per project come with Pro. Solo has a single Admin role and no role-based access control.
Pricing and plan conditions
Solo and Starter charge a monthly fee plus usage; Pro is priced through sales. Usage is counted in credits: each asset materialization is one credit, and each op that runs is one credit, whether in a job or inside an asset. On Solo and Starter, compute is charged only if you use Serverless; Hybrid deployments, where compute runs in your infrastructure, have no compute charge.
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| Plan | Price | Usage rates | What the plan includes |
|---|---|---|---|
Solo | $10 per month | $0.040 per credit; serverless compute $0.010 per minute | 1 user, 1 code location, 1 deployment. 30-day free trial. |
Starter | $100 per month | $0.035 per credit; serverless compute $0.010 per minute | Up to 3 users, 5 code locations, 1 deployment, catalog search. 30-day free trial. |
Pro | Contact Sales | Custom serverless compute | Unlimited code locations and deployments, cost tracking and insights, personalized onboarding, a private Slack channel, uptime SLAs and custom security questionnaires. Custom purchase structures, including discounts for annual payment or buying credits, plus custom invoicing and ACH. |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
340 GitHub commits 90d16.4k GitHub stars0 vulnerabilities across 1 packageOpenSSF score 4.8/10
See all signals from 9 sources
PyPI · dagster@1.13.29
Alternatives to Dagster
The reviewed substitutes for Dagster among the workflow orchestrators, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Apache Airflow
- Apache Airflow and Dagster are open-source orchestrators that schedule data pipelines and handle task dependencies and failures. Airflow is the long-established option with Python DAGs built from tasks and a very large ecosystem, while Dagster organizes pipelines around the data assets they produce, with lineage, observability and testing built in.Applies to: Choosing between two products of the same kind for one job.
- Prefect
- Dagster and Prefect are Python-based orchestrators for data pipelines, ML workflows and dbt runs. Prefect keeps its focus on flexible workflows built from plain Python functions, while Dagster puts data assets at the center and adds lineage and observability for every table and model.Applies to: Choosing the orchestrator that will schedule and recover data pipelines.
- Astronomer
- Dagster and Astronomer each run and observe scheduled data pipelines in production. Astronomer's Astro is a managed home for Apache Airflow, while Dagster is its own open-source orchestrator built around data assets, with lineage, dbt integration and a managed cloud of its own.Applies to: Choosing the orchestrator that will schedule and recover data pipelines.
- Mage
- Dagster and Mage are open-source tools for building and running data pipelines that load and transform data. Dagster emphasizes data assets, lineage and testable code, while Mage emphasizes an interactive editor where each pipeline step can be run and checked as it is written.Applies to: Choosing between two products of the same kind for one job.
- Y42
- Dagster and Y42 orchestrate data pipelines and the dbt transformations within them. Dagster is an open-source orchestrator that teams extend in Python, while Y42 is a turnkey managed platform that also includes built-in BI for reporting.Applies to: Choosing between two products of the same kind for one job.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Kestra
- Both are workflow orchestrators with a managed tier; Dagster is asset-centric with lineage, Kestra is task-declarative.Applies to: Orchestrating data pipelines with a managed control plane.
- ZenML
- Dagster and ZenML can both define and run machine learning pipelines with tracked outputs. ZenML is built for ML teams, with pluggable stack components and artifact versioning that move a pipeline from a laptop to Kubernetes, while Dagster is a general data orchestrator that treats every table, file and model as an asset.Applies to: Deciding how the stack is shaped, where both products can be part of the answer.
- Matillion
- Dagster and Matillion can both decide when data pipelines run and which steps wait on which. Matillion handles this inside its commercial visual ETL platform, while Dagster is an open-source orchestrator that models pipelines as data assets, with lineage and testing built in.Applies to: Whether pipelines are scheduled inside the ETL suite or coordinated by a separate orchestrator.
- Temporal
- Dagster and Temporal both orchestrate long-running, multi-step work that must survive failures. Dagster is built around data assets, with lineage and data quality checks for pipelines; Temporal keeps the state of application workflows written in ordinary code, which suits business processes and services.Applies to: Choosing an orchestrator for long-running work that must survive failure.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Meltano
- Meltano is an open-source, CLI-first tool that runs extract and load jobs built from Singer taps and targets. Dagster orchestrates those jobs as assets, so Meltano gets the data in while Dagster schedules the loads and the transformations and models that come after them.Applies to: Whether these two do the same job, or different jobs in one pipeline.
- Fivetran
- Fivetran loads data from SaaS applications and databases into the warehouse through managed connectors. Dagster is an asset-centric orchestrator that can treat each Fivetran sync as an upstream asset and then schedule and track the dbt models and ML jobs that depend on it.Applies to: Whether these two do the same job, or different jobs in one pipeline.
- Hevo Data
- An ELT tool lands data; an orchestrator decides what runs, in what order, and what happens when a step fails. The documented pattern runs both — Fivetran ships an Airflow provider, and code-first movement libraries are written to run inside an orchestrator — so the pair is a pipeline-and-scheduler pairing rather than a substitution. The question readers actually ask is whether both are needed.Applies to: Whether a managed or code-first ingestion tool removes the need for an orchestrator, or runs inside one.
Head to head
See detailed alternatives analysis
Dagster is a data-aware orchestrator for data and AI pipelines. Its model centers on data assets: teams define assets and their dependencies, then use Dagster to run work and keep assets up to date. The project is open source under the Apache 2.0 license, and its repository describes a Python-based, declarative approach to building data assets such as tables, datasets, machine-learning models, and reports.
Dagster’s product pages describe an operational layer for building, observing, and delivering data. The platform attaches lineage, quality signals, dependency context, run history, freshness, failures, and automation history to data work. Its integrated catalog centralizes discovery, workflows, metadata, lineage, and operational metadata. Built-in validation, automated testing, freshness checks, and observability are part of the data-quality tooling.
For implementation, Dagster supports Python 3.9 through Python 3.14. Its official integration material names Python scripts, dbt, Snowflake, Spark, Databricks, Azure, and AWS. Dagster Pipes adds observability and metadata tracking for jobs that run in external systems. Managed deployment options include serverless deployments and hybrid deployments using ECS, Kubernetes, Docker, Azure, or local infrastructure; hybrid operation can keep compute in an organization’s own cloud, on-premises, or hybrid environment while Dagster manages the control plane.
Top Alternatives Overview
A useful evaluation starts by identifying which parts of the data platform need orchestration, operational visibility, or managed deployment. Dagster is designed around asset-based orchestration rather than merely recording that a job ran. Its official product material describes pipelines in terms of the assets they produce, with lineage, quality signals, dependencies, and health context attached to those assets. The integrated catalog provides a shared view of data assets, workflows, and metadata, including asset-level lineage and metadata in the UI.
Teams assessing an alternative can use this model as a requirements checklist. Confirm whether the target can model dependencies between tables, datasets, models, and reports; represent partitions and incremental runs; support fault tolerance; and expose lineage and operational metadata. Dagster documents declarative automation, partitions and backfills, branch deployments, asset checks, freshness policies, dbt-test integration, and asset-health status. The managed plan comparison also lists workflow-based orchestration, a local development workflow through the dg CLI, and components.
Integration and execution boundaries are another practical dimension. Dagster names dbt, Snowflake, Spark, Databricks, Azure, and AWS among supported tools, while Dagster Pipes is intended to provide first-class observability and metadata tracking for external-system jobs. The pricing page lists serverless deployments as well as hybrid deployments for ECS, Kubernetes, Docker, Azure, and local environments. For teams whose requirements include cost visibility, the Pro plan lists Cost Tracking and Insights, and the plan comparison identifies cost tracking for BigQuery and Snowflake.
The open-source framework and the managed service should be evaluated separately. The core project is Apache 2.0 licensed. The managed offering has Solo, Starter, and Pro plans, with plan-specific deployment, access, support, catalog, and insights features.
Architecture and Approach Comparison
Dagster uses an asset-oriented, code-native approach. In the repository’s example, Python functions declare data assets, and asset arguments express dependencies. Dagster then helps run those functions at the appropriate time and keep the resulting assets current. This model applies to tables, datasets, machine-learning models, and reports. The platform overview similarly describes Dagster as a data-aware orchestrator that models data assets, understands dependencies, and provides visibility across the platform.
The asset model is paired with declarative orchestration. Dagster documents dependency handling, partitions, incremental runs, and fault tolerance. For development workflows, its repository describes use from local development and unit tests through integration tests, staging environments, and production. The managed feature comparison includes a local development workflow through the dg CLI, components, declarative automation, partitions and backfills, and branch deployments. Dagster’s product page describes branch deployments as a way to validate pipeline changes in a production-like environment before they affect production data.
Observability is part of the same operating model. Dagster’s integrated catalog is described as a unified view of assets, workflows, and metadata, with data discovery, lineage tracking, and operational metadata. The managed comparison lists asset metadata in the UI, asset-level lineage, data column lineage, catalog search, and BI tool cataloguing. The platform also describes validation, automated testing, freshness checks, and observability tools for data quality. The plan comparison identifies asset checks, freshness policies, dbt-test integration, and asset-health status.
Deployment can be separated from the control plane. Dagster documents serverless deployment and hybrid deployment, with hybrid options for ECS, Kubernetes, Docker, Azure, and local infrastructure. Its product page states that hybrid operation can run compute in an organization’s cloud, on-premises, or hybrid infrastructure while Dagster manages the control plane. Dagster Pipes extends the architecture to jobs running in external systems by providing observability and metadata tracking.
Pricing Comparison
Dagster’s pricing page lists three managed plans: Solo, Starter, and Pro. Solo is listed at $10 per month for personal projects and simple pipelines. It includes 1 user, 1 code location, and 1 deployment. Starter is listed at $100 per month for small teams running production pipelines. It includes up to 3 users, 5 code locations, 1 deployment, and Catalog Search. These are monthly plan prices; the page also lists usage charges, so they are not complete monthly-spend estimates.
Solo uses pay-as-you-go pricing of $0.040 per credit, and Starter uses $0.035 per credit. Dagster defines a credit as the sum of asset materializations and ops executed: each materialized asset counts as 1 credit, and each executing op counts as 1 credit. Serverless compute is listed at $0.010 per minute for Solo and Starter. The pricing FAQ states that hybrid use has no compute charge. The current Solo and Starter monthly plan details do not list included-credit allowances, so buyers should not treat either listed monthly amount as a bundled-credit price.
Both Solo and Starter list a 30-day free trial. Solo is positioned for personal projects and simple pipelines, while Starter is positioned for small teams running production pipelines. The plan comparison lists Catalog Search for Starter; it should not be assumed for Solo. It also lists platform and pipeline metrics with a 30-day lookback for the paid plans, including materialization success rate, failure count, and step execution time. Starter’s listed access roles are View, Catalog Viewer, Launcher, Editor, and Admin, with role-based access control scoped per deployment.
Pro is listed as “Contact Sales” and is described for building and scaling an enterprise data platform. The pricing page does not publish a Pro monthly price, currency amount, included usage allowance, or complete serverless-compute rate. It does list custom serverless compute, unlimited code locations, unlimited deployments, Cost Tracking and Insights, personalized onboarding support, a private Slack channel, uptime SLAs, and custom security questionnaires. Buyers evaluating Pro should request the applicable plan price, currency, contract term, usage or credit terms, compute pricing, deployment scope, support scope, and SLA terms.
The core Dagster project is Apache 2.0 licensed. That licensing fact does not establish a managed-service price or a managed-plan entitlement; managed features and pricing should be checked against Dagster’s current pricing page.
When to Consider Switching
A team considering a change should first decide whether Dagster’s asset-oriented model matches the work it needs to operate. Dagster is designed to model assets and their dependencies, then expose lineage, metadata, freshness, quality signals, and operational context. It is a natural fit for teams that want to manage tables, datasets, machine-learning models, or reports as explicit assets rather than treating pipeline execution as the only object of record.
A different approach may be appropriate when the required operating model does not need asset definitions, an integrated catalog, or asset-level visibility. Before changing platforms, document the exact requirements for dependency modeling, partitions, incremental runs, backfills, data-quality checks, freshness policies, dbt-test integration, alerting, and external-system jobs. Dagster lists alerts for email, Slack, and Microsoft Teams, and its managed comparison also lists alert policies for assets, runs, code locations, automation, and agent downtime.
Deployment requirements can also guide the decision. Dagster supports serverless deployment and hybrid deployment through ECS, Kubernetes, Docker, Azure, and local infrastructure. Hybrid deployment is intended to let teams run compute in their own cloud, on-premises, or hybrid environment while Dagster manages the control plane. Teams should identify whether they need that deployment boundary, multi-region failover, EU data residency, SIEM log export, or particular access and governance capabilities.
For managed purchasing, compare the actual plan boundaries rather than only the published base price. Solo has 1 user, 1 code location, and 1 deployment; Starter has up to 3 users, 5 code locations, 1 deployment, and Catalog Search. Both have usage-based credit charges and serverless-compute charges. Pro has no published monthly price and requires a sales conversation for its commercial terms.
Migration Considerations
Plan a migration around the assets and dependencies currently represented in Dagster. The product is built around a declarative asset model in which Python functions can define assets and dependency relationships. Inventory the tables, datasets, machine-learning models, reports, schedules, partitions, backfills, automation rules, and external-system jobs currently operated through that model. Record the lineage and operational metadata that downstream users rely on, along with asset checks, freshness policies, dbt-test integration, and asset-health status.
Validate the development and deployment path separately from the runtime path. Dagster supports local development, unit tests, integration tests, staging environments, and production. Managed features include branch deployments, which Dagster describes as validating a pipeline change in a production-like environment before it touches production data. A migration plan should identify how changed definitions will be tested, where dependencies will be validated, and how production changes will be introduced. The dg CLI local development workflow and reusable components are relevant where those are part of the existing implementation.
Preserve operational coverage during the transition. Dagster’s catalog centralizes data discovery, lineage, and operational metadata. Its platform material describes observability, validation, automated testing, and freshness checks, while the plan comparison lists metrics such as materialization success rate, failure count, and step execution time. Identify the exact catalog views, lineage records, health signals, alerts, and metrics that need to remain available. Email, Slack, and Microsoft Teams alerts are listed for the managed plans, and the alert-policy list includes asset, run, code-location, automation, and agent-downtime events.
Execution location should be an explicit workstream. Dagster supports serverless deployment and hybrid deployment through ECS, Kubernetes, Docker, Azure, and local environments. Dagster Pipes is relevant for workloads that continue to run in external systems because it provides observability and metadata tracking for those jobs. For managed plans, confirm plan-specific entitlements before cutover: Catalog Search is listed for Starter, not Solo; Solo and Starter have pay-as-you-go credit charges; and Pro commercial terms, including its public price, must be requested from Dagster because no monthly Pro amount is listed.