4.5/5
G2 rating
low public review volume
Report
Data orchestrator platform to build, schedule, and monitor reliable data pipelines.
Depends
DependsBuy it if you have a real data engineering team building Python pipelines that need lineage, observability, and scheduling.
Strong Python-first orchestration for real data teams; overkill for simple jobs; merging with Prefect.
4.5/5
G2 rating
low public review volume
~$47M
Funding
private (Elementl, Inc.)
Yes
Free tier
open-source core, self-hosted
Open-source core; free if self-hosted
Local developer experience called best in category
Lineage, observability, catalog, quality built in
SSO/RBAC/audit logs, but one disclosed LFI CVE
$0
Published security page with SSO/RBAC/audit logging; one disclosed 2025 local-file-inclusion CVE in the gRPC server.
Practitioners praise the asset-centric model and local developer experience, with little complaint volume in public sources.
“Dagster's local developer experience is one of the best in the category”
“In a couple days I was able to make a data processing pipeline that read in an Excel file and use the data to create CAD files and update their properties.”
“That rebuild is free, no API cost, it just looks at what changed.”
Compare Dagster with each alternative.
The incumbent scheduler; bigger ecosystem, heavier ops burden.
Pythonic workflows; now absorbing Dagster, so watch this space.
Dagster vs PrefectVisual, flow-based data routing without heavy coding.
Dagster vs Apache NiFiFully managed connectors; skip orchestration for plain ingestion.
Dagster vs FivetranBased on ~15 public sources; pricing and support evidence is thin.
Anonymous. You can change your vote.
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