Dagster is a data orchestration platform designed for complex workflow management and data pipelines with Python-first development, while Airbyte is an ELT platform focused on moving data from sources to destinations with low-code connectors. Dagster excels at orchestration complexity; Airbyte excels at rapid connector-based data movement.
Dagster vs Airbyte — Side-by-side comparison on A Versus B
Python-first data orchestration platform for complex workflow and asset management
Open-source ELT platform with 450+ pre-built connectors for rapid data integration
Primary Use Case. Pre-built Connectors. Development Approach. Orchestration Capabilities. Data Transformation Focus. Learning Curve for Developers. Connector Development Effort
Choose Dagster if you need sophisticated workflow orchestration, complex data transformations, or dynamic pipeline logic with strong Python integration and dbt support. Choose Airbyte if your priority is rapidly deploying pre-built ELT connectors with minimal coding, moving data from 450+ sources to cloud data warehouses, and you need a low-code solution for data engineers with varying technical backgrounds.
Dagster: Automatic and built-in | Airbyte: N/A
Dagster: Yes - Dagster asset checks | Airbyte: N/A
Dagster: 12+ (K8s, Docker, ECS, serverless) | Airbyte: N/A
Dagster: Free for self-hosted, $99/month for Dagster+ | Airbyte: N/A
Time to First Working Pipeline (typical)
Dagster: Enterprise-grade built-in | Airbyte: N/A
Dagster: 6+ (K8s, Docker, serverless, hybrid) | Airbyte: N/A
Dagster: Native asset-level (100%) | Airbyte: N/A
Dagster: 100% of features | Airbyte: N/A
Dagster: 2 CPU / 4GB RAM minimum | Airbyte: N/A
Dagster: ~15+ features (full compliance suite) | Airbyte: N/A
Dagster: Built-in Dagster Types with validation | Airbyte: N/A
Dagster: Yes, native support | Airbyte: N/A
Dagster: 8-24 hours | Airbyte: 1-4 hours
Dagster: Enterprise-grade (dynamic, conditional) | Airbyte: Basic (scheduled, triggered)
Dagster: Advanced (Python/software engineering) | Airbyte: Beginner-Intermediate (UI-based)
Dagster: 9/10 (advanced custom logic) | Airbyte: 3/10 (normalization only)
Dagster: ~12,000 stars | Airbyte: ~13,000 stars
Dagster: Usage-based (compute units) | Airbyte: Seat + sync volume-based
Dagster: Native, built-in with asset graph | Airbyte: Basic (connection-level monitoring)
Dagster: Yes - native DAG, scheduling, dynamic branching | Airbyte: N/A
Dagster: $0.04-0.06 per compute hour (~$29-43/month at 40 hrs/week) | Airbyte: N/A
Dagster: End-to-end pipeline orchestration and execution | Airbyte: Data Integration (ELT)
Dagster: 5 languages (Python, Rust, Golang, SQL, Bash) | Airbyte: N/A
Dagster: Cloud-agnostic; works with 15+ systems | Airbyte: N/A
Dagster: 40-60 hours (requires Python learning) | Airbyte: N/A
Dagster: 2,000+ (estimated from public case studies) | Airbyte: N/A
Dagster: Native assertions & sensors | Airbyte: N/A
Dagster: Full type hints, static validation | Airbyte: N/A
Dagster: Asset-centric with lineage tracking | Airbyte: N/A
Estimated Learning Curve (Hours to Productivity)
Dagster: Strong (compile-time + runtime) | Airbyte: N/A
Dagster: Full asset-level lineage | Airbyte: N/A
Dagster: First-class type definitions | Airbyte: N/A
Dagster: 3-4 weeks to proficiency | Airbyte: N/A
Dagster: Dagster Cloud (official) | Airbyte: N/A
Dagster: 100K+ (typical deployments) | Airbyte: N/A
Dagster: 50+ connectors | Airbyte: 450+ connectors
Dagster: 7/10 (Python required) | Airbyte: 3/10 (UI-driven)
Dagster: 24-48 hours (development needed) | Airbyte: 1-2 hours (pre-built connector)
Dagster: Full native integration | Airbyte: Limited via custom connectors
Dagster: 5-10 days | Airbyte: 2-3 days (visual builder)
Dagster: 9,200+ stars | Airbyte: 13,500+ stars
Dagster: N/A | Airbyte: $0 (open-source)
Dagster: N/A | Airbyte: $0 (self-hosted) or $100+
Dagster: N/A | Airbyte: SQL, dbt, Python (limited)
Dagster: N/A | Airbyte: Unlimited (self-hosted dependent)
Dagster: N/A | Airbyte: Yes (ELv2 license)
Dagster: N/A | Airbyte: $0 (open-source) / $500 (cloud)
Dagster: N/A | Airbyte: Real-time (CDC) or 1+ min
Dagster: N/A | Airbyte: 30-120 (self-hosted) / 5-15 (cloud)
Dagster: N/A | Airbyte: Self-hosted, Cloud, Hybrid
Dagster: N/A | Airbyte: dbt + Python/SQL + APIs
Dagster: N/A | Airbyte: Paid add-on (community default)
Dagster: N/A | Airbyte: SOC 2 (cloud), depends on self-hosting
Dagster: N/A | Airbyte: Proprietary core + BSL source-available
Dagster: N/A | Airbyte: Self-hosted, cloud, hybrid
Dagster: N/A | Airbyte: Yes (dedicated + SLA)
Dagster: N/A | Airbyte: Free (Community Edition)
Dagster: N/A | Airbyte: 100+ custom connectors
Starting Monthly Cost (Single Connector)
Dagster: N/A | Airbyte: $0 (self-hosted), $50-150 (cloud)
Dagster: N/A | Airbyte: Community (best-effort), paid enterprise optional
Dagster: N/A | Airbyte: Low-code Python/Java SDK
Dagster: N/A | Airbyte: Open-source (Apache 2.0)
Dagster: N/A | Airbyte: Available in cloud/enterprise plans
Dagster: N/A | Airbyte: $0 (self-hosted)
Dagster: N/A | Airbyte: No (requires Collibra, Alation, etc.)
Dagster: N/A | Airbyte: Yes (100% open source)
Dagster: N/A | Airbyte: Yes (AWS, GCP, Azure, on-prem)
Dagster: N/A | Airbyte: 3-4 (self-hosted) / 2 (cloud)
How to choose between Dagster and Airbyte
Dagster vs Airbyte — Structured Comparison Data
Machine-readable comparison dataset for Dagster vs Airbyte. Contains 108 attribute comparisons, 5 FAQ pairs.
Dagster vs Airbyte — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Dagster vs Airbyte.
Use Dagster when you need sophisticated orchestration, complex multi-step transformations, conditional logic, or dynamic pipelines with full Python control. Use Airbyte when your primary goal is rapidly moving data from external sources to your warehouse using pre-built connectors with minimal transformation complexity.
Yes. Many teams use Airbyte for ELT data movement (Extract-Load) and Dagster for orchestrating downstream transformations, quality checks, and complex workflows. Dagster can orchestrate Airbyte jobs as tasks within larger pipelines.
How many connectors does each platform support?
Airbyte offers 450+ certified connectors covering major SaaS and data platforms. Dagster has ~50 built-in integrations but emphasizes custom connector development via Python, supporting any data source through code.
Which is better for non-technical users?
Airbyte is significantly more accessible for non-technical users with its visual UI and no-code connector configuration. Dagster requires Python programming knowledge and is designed for data engineers and software engineers.
Can I do complex transformations in Airbyte?
Airbyte focuses on ELT and provides basic normalization. For complex transformations, integrate it with dbt, SQL, or Python transformation layers. Dagster has advanced transformation capabilities built-in via Python and dbt integration.
How to Choose Between Dagster and Airbyte
Primary Use Case: Dagster has Data orchestration & workflow management, while Airbyte has ELT data integration & movement.
Pre-built Connectors: Dagster has 50+ connectors, while Airbyte has 450+ connectors.
Development Approach: Dagster has Python-first, code-based, while Airbyte has Low-code UI with YAML config.
Orchestration Capabilities: Dagster has Enterprise-grade with dynamic pipelines, while Airbyte has Basic scheduling and triggering.
Data Transformation Focus: Dagster has Advanced (dbt integration, custom logic), while Airbyte has Limited (normalization only).
Learning Curve for Developers: Dagster has Moderate (requires Python knowledge), while Airbyte has Shallow (UI-driven, no-code option).
Connector Development Effort: Dagster has High (requires custom Python coding), while Airbyte has Low (visual connector builder).