Dagster
Continuous delivery tools
Continuous integration tools
DevOps software
CI/CD tools
- Features
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- Ease of management
- Quality of support
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$10 per month
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What is Dagster
Dagster is an open-source data orchestration platform used to define, schedule, and monitor data pipelines as code. It targets data engineering and platform teams that need reliable execution, observability, and dependency management across batch and event-driven workloads. Dagster emphasizes software-defined assets, type-aware inputs/outputs, and built-in tooling for testing and debugging pipelines. It is commonly deployed alongside data warehouses, transformation tools, and cloud compute services rather than as a general-purpose application CI/CD system.
Strong pipeline observability
Dagster provides a web UI for run history, logs, asset lineage, and materialization status, which helps teams diagnose failures and understand downstream impact. It captures structured metadata about pipeline steps and assets, improving traceability compared with generic job runners. Alerting and run monitoring features support operational ownership of data workflows. This focus on data pipeline visibility differs from deployment-focused DevOps tools that center on application releases.
Software-defined data assets
Dagster models pipelines around assets and dependencies, which can make data platform changes easier to reason about than task-only DAGs. Asset-based definitions support incremental builds, partitioning, and selective re-execution based on what changed. This approach helps teams manage complex dependency graphs across many datasets. It also supports code review and version control practices for pipeline logic.
Extensible integrations and execution
Dagster integrates with common data ecosystem components (e.g., warehouses, transformation frameworks, and compute backends) through libraries and community packages. It supports multiple executors and deployment patterns, including containerized and Kubernetes-based execution. Teams can extend it with custom resources, IO managers, and sensors to fit internal platforms. This flexibility is useful when standard CI/CD tools do not address data-specific runtime needs.
Not a CI/CD replacement
Dagster does not primarily manage application build, test, and deployment pipelines in the way CI/CD suites do. Teams typically still need separate tooling for source builds, artifact management, and release orchestration. Using Dagster for general software delivery can create gaps in features expected by application engineering teams. Its strengths are concentrated in data workflow orchestration rather than continuous delivery.
Operational overhead at scale
Running Dagster in production requires managing its control plane components, storage for run metadata, and worker execution infrastructure. High-volume workloads can increase demands on databases, logging, and Kubernetes operations. Organizations without a platform team may find the operational burden non-trivial compared with fully managed deployment platforms. Governance around upgrades and dependency compatibility also becomes important over time.
Learning curve for new concepts
Dagster introduces concepts such as assets, resources, IO managers, and sensors that may be unfamiliar to teams coming from simpler schedulers. Building idiomatic projects often requires Python engineering practices and disciplined repository structure. Migration from existing orchestration patterns can take time, especially when pipelines are tightly coupled to legacy scripts. Teams may need training to use the asset model effectively.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Solo | $10 per month | 7.5k credits/month; 1 user; 1 code location; 1 deployment; serverless compute billed at $0.005 per compute minute; 30-day free trial |
| Starter | $100 per month | 30k credits/month; up to 3 users; 5 code locations; 1 deployment; catalog search; 30-day free trial |
| Pro / Enterprise | Contact Sales | Unlimited code locations; unlimited deployments; cost tracking & insights; personalized onboarding support; private Slack channel; uptime SLAs; custom security questionnaires; custom pricing (contact sales) |
Additional notes: Overages are charged at $0.03 per credit beyond contracted credits. Pricing is credit-based (credits = asset materializations + ops executed).
Seller details
Dagster Labs, Inc.
San Francisco, CA, USA
2019
Private
https://dagster.io/
https://x.com/dagster
https://www.linkedin.com/company/dagster-labs/
