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Dagster

Data Orchestration

Dagster Labs develops an asset-oriented data orchestrator built from first principles for modern data engineering.

Updated 5 days ago · Reviewed by 7wData
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  • Products
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  • Clients
  • Strengths
  • Recent moves
  • Key Info
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Profile

Asset-oriented orchestration platform for building, scaling, and observing data and AI pipelines with built-in lineage tracking and observability.

Dagster Labs develops an asset-oriented data orchestrator built from first principles for modern data engineering. Founded in 2018 by Nick Schrock, a GraphQL co-creator and former Facebook infrastructure engineer, and Pete Hunt, who previously led React at Facebook and founded Smyte, the company takes a fundamentally different approach to pipeline orchestration than Apache Airflow, the industry standard it competes with.

Rather than organizing work around tasks, Dagster centers on data assets—the artifacts that flow through pipelines. This design choice simplifies dependency tracking, improves observability of data lineage, and enables stronger testing and CI/CD patterns. The open-source project has gained significant traction since launch, with visible adoption across finance, retail, life sciences, and technology: Kraft Heinz, Vanta, Bayer, Fanatics, AMD, and PostHog are among named adopters. US Foods, a Fortune 500 company, has achieved 99.996% uptime running Dagster at scale, supporting $24 billion in annual operations.

The company is backed by $49 million in Series B funding (May 2023), led by Georgian and including earlier investors Sequoia and Index. CEO Pete Hunt has explicitly stated the focus on "building out go-to-market organization," signaling a shift from scrappy startup to enterprise sales motion. The team has grown to approximately 97 people as of mid-2025, a near-doubling from Series A.

In 2024-25, Dagster expanded its product surface aggressively. Airlift, launched in 2024, is a toolkit for incremental migration from Airflow—acknowledging that most switching customers come from Airflow ecosystems and reducing switching costs. Components framework and the dg CLI reached general availability in September 2025, standardizing how third-party tools integrate. Compass, launched in November 2025, adds an AI data analyst accessible in Slack, competing directly with semantic layers and BI tools. Expansion into Europe with Dagster+ (September 2025, with Serverless support added in December) signals confidence in international enterprise adoption.

The company faces the classic challenger position: Airflow remains entrenched, open-source adoption is fragmented across use cases, and scaling the sales organization in a crowded orchestration market requires both feature velocity and proof points. Whether Dagster's asset-centric model becomes the industry standard or remains a better fit for new greenfield projects is the open strategic question.

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Products by Dagster

  • Dagster Platform Dagster is an asset-oriented data orchestrator that models data pipelines around software-defined assets—reusable Python functions that define what No reviews yet → Be the first to review →

Who buys this

  • Mid-to-large enterprises building modern data platforms (Kraft Heinz, US Foods, Bayer, Fanatics)
  • Financial services and fintech companies managing real-time data workflows (Petal, Zippi, Vanta)
  • Analytics teams and data engineers migrating from Apache Airflow seeking better asset observability
  • E-commerce and marketplace platforms requiring reliable, observable pipelines (Big Cartel, Mejuri, easyJet Holidays)
  • Organizations deploying AI/ML pipelines requiring orchestration, monitoring, and version control

Publicly disclosed clients

  • US Foods
  • Kraft Heinz
  • Vanta
  • Bayer
  • Fanatics
  • AMD
  • PostHog
  • easyJet Holidays
  • Mejuri
  • Petal
  • Zippi

Strengths and what to watch

Strengths

  • Asset-centric design with superior data lineage visibility and dependency tracking compared to task-based orchestrators like Airflow
  • Developer-friendly local testing and debugging via dagster dev command; strong Python integration and low friction onboarding
  • Comprehensive product suite spanning orchestration, observability (Insights), migration tooling (Airlift), and AI analysis (Compass)

Watch for

  • Heavy bet on Airflow migration (Airlift) may indicate slow organic adoption among net-new users rather than market-share gains; customer concentration risk if Airflow transition momentum slows
  • Compass AI product (launched Nov 2025) unproven at scale; relies on direct warehouse connectivity which raises data residency and compliance concerns for regulated industries
  • Challenger position in crowded market; Airflow's entrenched ecosystem, extensive plugin library, and community inertia create persistent switching costs despite Dagster's superior architecture

Recent moves

  • 5w ago Dagster+ Solo and Starter plans shift to consumption-based pricing model
  • 6mo ago Serverless deployment support added to Dagster+ EU region
  • 7mo ago Compass AI data analyst for Slack launched, enabling natural-language queries against data warehouses
  • 9mo ago Dagster+ becomes available in EU with hybrid and serverless deployment options
  • 9mo ago Components framework and dg CLI reach general availability with YAML-based DSL

Key Information

Industry
Data Orchestration
Founded
2018
Employees
51-200
Headquarters
San Francisco, CA

Connect

LinkedIn @dagsterio

Frequently Asked Questions

What is Dagster?

Dagster is an asset-oriented data orchestration platform that builds, scales, and monitors data and AI pipelines. Unlike traditional task-based systems, it centers on data assets—the artifacts flowing through pipelines—improving dependency tracking, lineage visibility, and testing capabilities for modern data engineering teams.

How does Dagster differ from Airflow?

Dagster organizes work around data assets rather than tasks like Airflow does. This asset-centric approach simplifies dependency tracking, improves data lineage observability, and enables stronger testing patterns. Dagster also emphasizes developer experience with superior local testing via dagster dev command.

What is Airlift?

Airlift is Dagster's toolkit for incrementally migrating pipelines from Apache Airflow. Launched in 2024, it acknowledges that most Dagster customers come from Airflow ecosystems and reduces switching friction. The tool helps teams gradually adopt Dagster's asset-oriented model without replacing entire workflows at once.

What industries use Dagster?

Dagster serves finance, retail, life sciences, technology, and e-commerce sectors. Major adopters include Kraft Heinz, Vanta, Bayer, Fanatics, AMD, PostHog, and US Foods—which runs $24 billion in annual operations with 99.996% uptime on Dagster. The platform is particularly popular among financial services firms managing real-time data workflows.

What is Dagster Compass?

Compass is Dagster's AI data analyst launched in November 2025, accessible directly in Slack. It enables natural-language queries against data warehouses like Snowflake, BigQuery, Redshift, Athena, Postgres, Databricks, and MotherDuck. Compass competes with semantic layers and BI tools by democratizing data access for non-technical users.

How much does Dagster cost?

Dagster+ offers three tiers: Solo ($10/month base plus $0.04 per credit), Starter ($100/month base plus $0.035 per credit), and enterprise plans. As of May 2026, Solo and Starter plans use consumption-based pricing. Open-source Dagster is free. Pricing varies by region; EU deployments became available September 2025.

How Dagster compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This company

Dagster

Positioning
Asset-oriented orchestration platform for building, scaling, and observing data and AI pipelines with built-in lineage tracking and observability.
Customer segments
Mid-to-large enterprises building modern data platforms (Kraft Heinz, US Foods, Bayer, Fanatics)
Strengths
Asset-centric design with superior data lineage visibility and dependency tracking compared to task-based orchestrators like Airflow
Watch for
Heavy bet on Airflow migration (Airlift) may indicate slow organic adoption among net-new users rather than market-share gains; customer concentration risk if Airflow transition momentum slows
Recent moves
Dagster+ Solo and Starter plans shift to consumption-based pricing model

Prefect

Positioning
Simplicity-first Python orchestration for cloud-native teams prioritizing lower ops overhead than Airflow, targeting greenfield Python stacks.
Customer segments
Mid-size tech companies, ML practitioners, Python-native data engineering teams avoiding YAML-heavy configuration and Airflow operational complexity.
Strengths
Single-decorator flow definition with hybrid execution plane and autoscaling workers, reducing setup friction for Python-first teams.
Watch for
Scaling ceiling at high concurrency, no native data lineage tracking, enterprise governance features locked behind negotiated contracts.
Recent moves
January 2026: repositioned to AI infrastructure, releasing managed deployment tooling for AI agent workflows alongside core orchestration.

Astronomer

Positioning
Primary commercial Airflow provider selling managed Airflow-as-a-Service to enterprises already invested in Airflow's ecosystem and plugin library.
Customer segments
Enterprise data engineering teams in financial services, healthcare, and manufacturing running production batch and MLOps pipelines at scale.
Strengths
Stewardship of Apache Airflow open-source project, day-one Airflow 3 feature parity, Astro Private Cloud for air-gapped regulated environments.
Watch for
CEO Andy Byron resigned July 2025 amid public scandal. Series D at $775M was a 48% down-round from 2022 peak.
Recent moves
May 2025: raised $93M Series D at $775M valuation, a 48% down-round from 2022, led by Bain Capital Ventures.

dbt Labs

Positioning
SQL transformation layer expanding into pipeline orchestration, data cataloging, and observability for cloud data warehouse teams.
Customer segments
Analytics engineers and data engineers at mid-market to enterprise companies primarily on Snowflake, BigQuery, Redshift, or Databricks.
Strengths
Rust-based Fusion compiler parses up to 30x faster. State-aware orchestration rebuilds only changed models, reducing warehouse compute costs.
Watch for
Pending Fivetran merger (mid-2026 expected close) raises bundled pricing and vendor lock-in concerns among multi-vendor data stacks.
Recent moves
October 2025: all-stock merger with Fivetran announced, combined ARR near $600M, pending regulatory close mid-2026.

Sources

  1. dagster.io — Company positioning, product suite (Dagster, Dagster+, Compass, Insights), customer base (Kraft Heinz, Vanta, Bayer, Fanatics, AMD, PostHog)
  2. dagster.io — Founders Nick Schrock and Pete Hunt, their backgrounds, company history, team composition
  3. techcrunch.com — Series B funding: $33M on May 24, 2023, led by Georgian with investors 8VC, Human Capital, and existing investors Sequoia, Index, Amplify; total funding $48.8M
  4. dagster.io — Positioning relative to Airflow, key differentiators (asset-centric design, developer experience, observability, modern architecture)
  5. dagster.io — Compass launch date (November 13, 2025), features (Slack integration, natural-language queries, support for Snowflake, BigQuery, Redshift, Athena, Postgres, Databricks, MotherDuck)
  6. dagster.io — Components framework and dg CLI general availability (September 18, 2025 with version 1.11.10), YAML-based DSL, ready-made integrations
  7. dagster.io — Dagster+ EU availability (September 19, 2025) with eu-north-1 region, serverless support added December 2025, data residency compliance
  8. support.dagster.io — May 2026 pricing update to consumption-based model for Solo ($10/month base + $0.04 per credit) and Starter ($100/month base + $0.035 per credit) plans
INS7GHTS

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