Dagster
Dagster is a San Francisco data orchestration platform offering open-source and managed cloud tools for building and monitoring data pipelines. Its Dagster+ SaaS serves enterprise customers in finance, retail, life sciences, and technology, including DoorDash, Kraft Heinz, and Bayer.
- Company typePrivate
- Founded2018
- HeadquartersSan Francisco, United States
- Headcount51–100
- GTM typeB2B
- OfferingSoftware
What Dagster does
Dagster is a data orchestration platform company founded in 2018 by Nick Schrock as Elementl (rebranded to Dagster Labs in 2023) and headquartered in San Francisco, California. Its core product is an open-source Python-based orchestrator built around an asset-centric architecture—pipelines are defined by the data assets they produce rather than by task sequences—which enables automatic lineage tracking, data quality signaling, and dependency mapping across the modern data stack. The commercial offering, Dagster+, is a managed cloud SaaS built on top of the open-source core with a freemium model featuring a free tier and quote-based enterprise pricing.
Dagster+ adds enterprise capabilities including branch deployments, a hybrid deployment architecture that lets compute run in customer infrastructure while Dagster manages the control plane, role-based access control, cost insights, and built-in observability. First-class native integrations with dbt, Snowflake, and Fivetran eliminate the need for custom glue code. The platform serves enterprise customers across finance, software/technology, retail/e-commerce, and life sciences, with named logos including DoorDash, Kraft Heinz, Bayer, Vanta, AMD, Flexport, Signify Health, Fanatics, and PostHog.
The company employs 51-100 people and has raised $47 million total across a $14 million Series A led by Index Ventures (November 2021) and a $33 million Series B led by Georgian (May 2023). Dagster pursues a hybrid go-to-market combining open-source product-led growth with enterprise field sales for large deployments. Recent product launches include Compass, a platform for building governed AI data agents on operational context, and Dagster+ AI, an AI assistant that operates on accumulated metadata. The platform is SOC 2 Type II certified and HIPAA compliant, enabling regulated workloads.
Dagster firmographics
Firmographics- Name
- Dagster
- Legal name
- Elementl, Inc.
- Website
- https://dagster.io
- Company type
- Private
- Founded year
- 2018
- Operating status
- Operating
- Headcount range
- 51–100 employees
- Short description
- Dagster is a San Francisco data orchestration platform offering open-source and managed cloud tools for building and monitoring data pipelines. Its Dagster+ SaaS serves enterprise customers in finance, retail, life sciences, and technology, including DoorDash, Kraft Heinz, and Bayer.
- Ownership category
- akta.pro rank
Dagster industry classification
Industry- Product category
- Data Orchestration Platform
- NAICS
- Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (51821), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518210)
- SIC
- Services-Computer Programming, Data Processing, Etc. (7370), Services-Prepackaged Software (7372)
- akta.pro primary industry
- Data Lake Platforms (HDAEABAC)
- akta.pro secondary industries
- Data Platforms & Modern Data Stack Services (Lakehouse, DW, MDM) (BPAEAHAC), Digital Asset Management (DAM) (HDAEAGAE)
Keywords
Where Dagster is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Offices1 record
Markets served
Dagster business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Infrastructure, Marketing or Sales, Operations
Revenue model
- Dagster+ SaaS Subscription: Managed cloud offering built on top of the open-source Dagster. Includes enterprise features like branch deployments, hybrid deployment, role-based access control, cost insights, and built-in observability. Free tier available to get started.
- Enterprise Support Contracts: Enterprise tier with dedicated support, SLA guarantees, and additional enterprise features beyond the managed SaaS.
- Open Source Core: Dagster's core orchestration framework is open source and available on GitHub. The open-source model drives community adoption and potential conversion to paid Dagster+ tiers.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Monthly | Free tier for individual developers and small teams to get started with Dagster+ |
| Subscription | Monthly | Dagster+ Solo and Starter plans |
| Subscription | Annual | Enterprise tier with advanced features |
Go-to-market motion3 records
Distribution channels4 records
Marketing channels12 records
Dagster product offering
Product offeringCore offering
Dagster is an AI-native DataOps platform that structures how data is built, observed, and delivered through asset-centric data orchestration. It offers an open-source orchestration framework (Dagster) and a managed cloud SaaS (Dagster+) that provide automatic lineage tracking, data quality signals, dependency context, and built-in observability for data and AI pipelines. Customers include data engineering, AI/ML, and analytics teams across enterprise and mid-market organizations.
Product overview
Dagster is an AI-native DataOps platform structured as a platform-plus-modules architecture. The core offering consists of the open-source Dagster orchestration framework (available on GitHub) and Dagster+, a managed cloud SaaS product built on top of it. The platform is organized around four core capabilities: Data Orchestration (for building and running asset-based pipelines), Data Catalog (for asset discovery and management), Data Quality (for monitoring and testing), and Cost Insights (for operational cost visibility). Additional modules include Compass (for AI-driven analytics and data agents) and Dagster+ AI (for diagnostic assistance using operational context). Dagster University provides educational resources. Deployment options include Hybrid (run in customer infrastructure) and Serverless (fully managed).
Differentiator
Problem solved
Functional benefit
Brands
- Dagster+: Managed cloud offering that adds enterprise features like branch deployments, hybrid deployment, role-based access control, cost insights, and built-in observability with a free tier.
- Dagster University
- Compass
Products and services
- Dagster (Open Source) An open-source Python-based data orchestrator that lets customers develop, run, and monitor data pipelines using an asset-centric approach. The core orchestration framework is freely available on GitHub for self-hosted deployments, driving community adoption.
- Dagster+ The managed cloud offering built on top of the open-source Dagster framework. Adds enterprise features including branch deployments, hybrid deployment, role-based access control, cost insights, and built-in observability. Available with a free tier, plus Solo, Starter, and Enterprise plans.
- Compass An AI-powered analytics offering that enables teams to build and ship governed data agents on top of trusted data workflows. Uses Dagster's operational context (assets, runs, lineage, freshness, failures) to drive AI-driven analysis and process automation.
- Dagster+ AI An AI assistant within Dagster+ that operates on operational context including assets, runs, lineage, freshness, failures, and automation history. Enables teams to diagnose issues, explain pipeline behavior, and take action faster using natural language.
- Dagster+ Hybrid Deployment A deployment option for Dagster+ that allows customers to run compute in their own infrastructure (cloud, on-premises, or hybrid) while Dagster manages the control plane. Supports AWS ECS, Kubernetes, Docker, Azure, and local agents, with AWS PrivateLink for strict network isolation.
- Dagster+ Serverless A fully managed serverless deployment option for Dagster+ where Dagster Labs operates the control plane and compute on behalf of the customer.
Quantifiable outcome
- 14x faster data freshness: Vanta achieved business-critical data freshness improved from 7 hours to 30 minutes
- +5 more outcomes
Companies that use Dagster
Customer profileNamed customers20 records
Segments6 records
Ideal customer profiles3 records
Dagster technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration3 records
AI capability4 records
Feature10 records
Dagster partnerships and signals
Strategic signalPartnerships
Eight partnerships are on record, tiered core.
- dbtcoreFirst-class native integration with dbt. Existing dbt models can be orchestrated, monitored, and cataloged within Dagster without custom glue code.
- SnowflakecoreFirst-class native integration with Snowflake. Snowflake assets are orchestrated, monitored, and cataloged natively within Dagster.
- FivetrancoreFirst-class native integration with Fivetran for data ingestion. Supported within Dagster's unified operational view.
- Amazon Web Services (AWS)coreAWS hosts all Dagster+ data. AWS data centers do not allow Dagster Labs employees physical access. Supports AWS PrivateLink for strict network isolation in hybrid deployments.
- KubernetescoreDagster+ supports Kubernetes agent for container orchestration in hybrid deployments. Recommended as the default agent for most production deployments.
- Microsoft AzurecoreDagster+ supports Azure agent for hybrid deployments on Microsoft Azure cloud infrastructure.
- DockercoreDagster+ supports Docker agent for containerized hybrid deployments.
- AWS ECScoreDagster+ supports AWS ECS agent for container orchestration on Amazon ECS in hybrid deployments.
Scale indicators10 records
Recent moves6 records
Expansion highlights6 records
Dagster competitors and assessment
Company assessmentDirect peers
- Apache Airflow: The dominant open-source task-centric data orchestrator and Dagster's primary head-to-head competitor. Comparison pages on Dagster's site (Dagster vs Airflow) frame the positioning debate, and both target data engineering teams building pipelines at scale.
- Prefect: Direct commercial competitor offering a Python-based data workflow orchestration platform with managed cloud and open-source tiers. Dagster explicitly publishes a Dagster vs Prefect comparison and serves overlapping data engineering personas.
- Astronomer: Commercial wrapper and managed service for Apache Airflow with enterprise features and Astro Cloud. Competes for the same data orchestration budget as Dagster+ but with the incumbent task-centric paradigm.
Emerging players
- Temporal: Workflow orchestration platform originally focused on microservices but increasingly used for data and AI pipelines. Competes for engineering mindshare on durable execution and is a credible alternative for teams building AI-driven data workflows.
- Mage: Open-source data pipeline tool with notebook-style developer experience and managed cloud. Competes for AI/ML-adjacent data engineering teams that Dagster also serves, though with a different core UX.
- Kestra: Open-source declarative data orchestration platform with a managed cloud offering. Targets overlapping use cases (ETL/ELT, data pipelines) and represents a newer entrant in the same category as Dagster.
Broad incumbents
- Azure Data Factory: Microsoft's cloud-native data integration and orchestration service. Dagster targets Azure enterprises with hybrid deployment and a 'Dagster vs Azure Data Factory' comparison page, competing for the same orchestration budget.
- dbt Labs: While a native integration partner, dbt Cloud is a major adjacent platform in the modern data stack. dbt expanding further into orchestration/monitoring would directly overlap with Dagster's value proposition.
- GCP Cloud Composer: Google Cloud's managed Apache Airflow service. Provides another hyperscaler-bundled alternative to Dagster for GCP-native customers and increases competitive pressure on the multi-cloud orchestration story.
- AWS Step Functions: Hyperscaler-managed workflow orchestration service bundled into the AWS ecosystem. Dagster explicitly competes here with a dedicated 'Dagster vs AWS Step Functions' comparison page for cloud-native enterprises.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat5 records
Key risks6 records
Key highlights7 records
Customer concentration
Dagster social profiles
Digital presenceDagster compliance and trust
Trust signalCompliance2 records
Dagster financial estimates
Financial estimateRevenue estimate
Valuation estimate
Dagster leadership team
Management profileNumber of profiles
Profiles4 records
Dagster funding detail
Funding detailFunding overview
Funding rounds3 records
Investors14 records
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Dagster M&A and investment
M&A and investmentM&A1 record
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Dagster
What does Dagster do?
Dagster is an AI-native DataOps platform that structures how data is built, observed, and delivered through asset-centric data orchestration. It offers an open-source orchestration framework (Dagster) and a managed cloud SaaS (Dagster+) that provide automatic lineage tracking, data quality signals, dependency context, and built-in observability for data and AI pipelines. Customers include data engineering, AI/ML, and analytics teams across enterprise and mid-market organizations.
Is Dagster a public or private company?
Dagster is a private company. It is classified as venture growth investor backed and is currently operating.
When was Dagster founded?
Dagster was founded in 2018. It employs 51 to 100 people.
Where is Dagster based?
Dagster is headquartered in San Francisco, United States, in the North America region.
How does Dagster make money?
Three revenue lines are on record. Dagster+ SaaS Subscription is the primary driver. The others are enterprise Support Contracts and open Source Core.
Who are Dagster's main competitors?
Direct peers on record are Apache Airflow, Prefect and Astronomer. Emerging players are Temporal, Mage and Kestra. Broad incumbents are Azure Data Factory, dbt Labs, GCP Cloud Composer and AWS Step Functions.
Does Dagster have an API?
Yes. Dagster+ includes a GraphQL API that the Dagster+ agent communicates with to retrieve job definitions, evaluate schedules and sensors, and stream run metadata. User code communicates with the agent API over GraphQL over HTTPS. The API is used to load definitions and metadata (UI browsing, asset graphs), evaluate sensors/schedules, launch and monitor runs, and stream logs and materialization events. Developer documentation is at docs.dagster.io.
What industry is Dagster in?
Dagster's product category is Data Orchestration Platform. Its primary akta.pro industry code is HDAEABAC, Data Lake Platforms, with a secondary code of BPAEAHAC, Data Platforms & Modern Data Stack Services (Lakehouse, DW, MDM). Its NAICS code is 5182 and its SIC code is 7370.