Dagster

Asset-based data orchestration platform with a GraphQL API for managing software-defined assets, pipeline runs, sensors, and schedules. Available open-source (self-hosted) or as Dagster Cloud managed service.

Evaluated Mar 06, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ Developer Tools dagster orchestration data-assets graphql python etl dbt data-engineering sensors schedules open-source asset-graph
⚙ Agent Friendliness
68
/ 100
Can an agent use this?
🔒 Security
82
/ 100
Is it safe for agents?
⚡ Reliability
82
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
82
Error Messages
78
Auth Simplicity
78
Rate Limits
72

🔒 Security

TLS Enforcement
95
Auth Strength
80
Scope Granularity
75
Dep. Hygiene
82
Secret Handling
80

Dagster Cloud: API token + org token. Self-hosted: configurable. SOC2 Type II for Dagster Cloud. Data pipeline assets may process sensitive data — pipeline code security matters.

⚡ Reliability

Uptime/SLA
85
Version Stability
82
Breaking Changes
80
Error Recovery
80
AF Security Reliability

Best When

Your data platform uses software-defined assets with lineage tracking, and you need an agent to trigger materializations or query asset health via API.

Avoid When

You need a simple task scheduler, your team is unfamiliar with asset-based thinking, or you want a REST API as the primary programmatic interface (GraphQL is required for most Dagster operations).

Use Cases

  • Managing and materializing software-defined data assets with lineage tracking and freshness policies
  • Triggering pipeline runs and querying asset materialization status from AI agents via the GraphQL API
  • Orchestrating dbt, Spark, and custom Python transformations as part of a unified asset graph
  • Building sensor-driven pipelines that trigger on external events (new S3 objects, database changes)
  • Observing data freshness and partition materialization state across a complex data platform

Not For

  • Teams who want a task-centric (not asset-centric) mental model — Prefect or Airflow may be a better fit
  • Simple cron scheduling without data lineage concerns — Dagster's power comes at setup complexity cost
  • Non-Python teams — Dagster is Python-first with no native support for other orchestration languages

Interface

REST API
Yes
GraphQL
Yes
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: api_key user_token
OAuth: No Scopes: No

Dagster Cloud uses agent tokens (for deployment agents) and user tokens for API access. Set via DAGSTER_CLOUD_API_TOKEN environment variable. Tokens are workspace-scoped. Enterprise plans add SSO and RBAC. Self-hosted deployments can use any auth layer in front of the Dagit GraphQL endpoint.

Pricing

Model: open-source
Free tier: Yes
Requires CC: No

The open-source self-hosted path is production-capable. Dagster Cloud adds managed infrastructure, CI/CD branching deployments, and enterprise auth. Most API features are identical between OSS and Cloud.

Agent Metadata

Pagination
cursor
Idempotent
Partial
Retry Guidance
Documented

Known Gotchas

  • GraphQL is the primary programmatic interface — agents must construct GraphQL queries rather than simple REST calls
  • Asset keys use a path structure (e.g., ["schema", "table_name"]) that must be known in advance to target specific assets
  • Run tags and config must be passed as serialized YAML or JSON within the GraphQL mutation — not as typed fields
  • Sensor and schedule ticks are managed by Dagster's daemon process — external triggers go through launchRun mutation directly
  • Partitioned asset materializations require specifying the partition key — missing or wrong keys silently skip the partition
  • GraphQL schema changes between Dagster versions — pin version and regenerate clients when upgrading

Alternatives

Full Evaluation Report

Detailed scoring breakdown, competitive positioning, security analysis, and improvement recommendations for Dagster.

$99

Scores are editorial opinions as of 2026-03-06.

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