Prefect vs Dagster

Python in, Python out

Engineers are choosing Prefect for Python-first simplicity, 60-70% cost savings, and days to value—without restructuring code into asset paradigms. Your workflows run in your infrastructure.

Start free Book a demo

From Dagster's CEO

"Prefect is the best workflow engine out there"

— Pete Hunt, CEO, Dagster

Why teams switch from Dagster

Simplicity, predictability, and flexibility

Prefect

Write Python as-is

Add decorators to existing Python functions. No asset restructuring, no IO managers. Your code runs in your infrastructure immediately.

Prefect

Days to value, not months

Teams deploy production workflows in days. No paradigm shift required—just Python orchestration in your environment.

Prefect

Predictable pricing

Transparent user-based pricing. No per-asset costs that scale with your data. Your compute runs in your infrastructure.

Dagster

Built around Software-Defined Assets requiring teams to restructure workflows into asset-centric paradigms. As of May 2026, Dagster+ removed included credits from Solo and Starter plans, charging $0.035-$0.040 per asset materialization with no credits included. Teams report months of learning curve to adapt to the asset-first model.

Developer experience

No asset costume required

Dagster forces every workflow into its Software-Defined Asset model with @asset decorators, IO managers, and repository restructuring. Prefect runs your Python functions as-is in your infrastructure—add a @flow decorator and you're done.

No code restructuring required

Days to production, not months

Works with your existing infrastructure

"Prefect is way more accessible than Dagster."

— Braun Reyes, Senior Data Engineer, Clearcover

Prefect

from prefect import flow, task
@task
def extract():
    return get_data()
@task
def transform(data):
    return clean(data)
@flow
def my_workflow():
    data = extract()
    result = transform(data)
    return result

Dagster

from dagster import asset, Definitions
from dagster import IOManager
@asset
def extract_data(context):
    return get_data()
@asset
def transform_data(context, extract_data):
    return clean(extract_data)
# Requires IO managers, repositories
defs = Definitions(
    assets=[extract_data, transform_data],
    resources={"io_manager": IOManager()}
)

Dagster Cost

Prefect Cost

Pricing ModelPay per user, not per run

Pricing transparency

No per-asset tax

Dagster's credit-based model means every new asset drives up your bill. As your data platform grows, budget becomes unpredictable. Prefect charges by users and workspaces—your workflows execute in your infrastructure with zero marginal cost.

60-70% typical cost savings

Free tier: 2 users, 5 workflows

Scale infrastructure without budget surprises

View pricing details

Security & control

Your workflows run in your infrastructure

Both Prefect and Dagster offer cloud orchestration, but Prefect's hybrid architecture separates orchestration from execution. Workers poll via outbound-only connections—your code, data, and compute stay in your environment.

Deploy on Kubernetes, ECS, Docker

Zero code or data egress

SOC 2 Type II, GDPR, HIPAA ready

Learn about hybrid architecture

Prefect Cloud

Your Network

Control Plane

Metadata

Workers

Data

Prefect Cloud hosts the Control Plane & Metadata. You host execution & data.

ETL pipelines

ML training workflows

Real-time streaming

Event-driven automation

Infrastructure provisioning

Workflow variety

Orchestrate any workflow type

Dagster's asset-centric model works well for batch data transformation but forces other workflow types into unnatural patterns. Prefect orchestrates ETL, ML, streaming, and automation equally well—all running in your infrastructure.

Explore workflow patterns

Data assets

Data assets, hold the cruft

Both platforms support data assets and lineage tracking. Dagster requires Software-Defined Assets as the foundation—every workflow must be an asset with IO managers and repositories. Prefect takes a task-first approach where assets are outcomes you track when needed via @materialize decorators. Your workflows run in your infrastructure either way.

Write tasks, add materialization when needed

Automatic dependency inference from task graphs

Track external assets without restructuring

Learn about Prefect assets

Prefect: Task-First with Optional Materialization

Flexible

from prefect import flow, task
from prefect.assets import materialize
@task
def process_data():
    # Just a task - works perfectly
    return transform(data)
# Add materialization when you need lineage
@materialize("s3://lake/processed.csv")
def tracked_process():
    return transform(data)
@flow
def my_flow():
    # Mix materialized and regular tasks
    result = process_data()
    tracked_process()

Dagster: Asset-Centric Foundation

Required

from dagster import asset, Definitions
@asset  # Everything must be an asset
def process_data(context):
    # Requires asset paradigm
    # Must define IO managers
    return transform(data)
# Needs repository structure
defs = Definitions(
    assets=[process_data],
    resources={"io_manager": io_manager}
)

Why Prefect wins

Simplicity without sacrificing power

Tasks share memory

Native data passing between tasks in your infrastructure. Dagster siloes steps like Airflow requiring IO managers.

Test your logic, not framework

Test functions directly. Dagster requires 50+ lines of boilerplate to mock framework concepts.

Infrastructure that fits your workflow

No control plane required. Lightweight workers or hosted execution in your environment—your choice.

Dynamic by default

Build graphs at runtime based on data. Dagster freezes graphs at import time breaking dynamic workflows.

No asset costume required

Optional asset tracking. Dagster forces every workflow into the Software-Defined Asset model.

Python in, Python out

Write standard Python running in your infrastructure. No DSL replica or framework-specific patterns required.

Feature comparison

See how Prefect compares across key capabilities

Development Experience

Feature	Prefect	Dagster
No rewrites needed
Minimal learning curve		Months
Test without framework mocking
Handle large in-memory data		Siloed

Workflow Capabilities

Feature	Prefect	Dagster
Dynamic DAGs at runtime		Frozen at import
All workflow types supported		Asset-centric only

Data Assets & Lineage

Feature	Prefect	Dagster
Data assets & lineage
Asset tracking approach	Tasks-first, optional	Asset-centric, mandatory

Teams trust Prefect for production workloads

From high-growth startups to Fortune 500

Clearcovertestimonial

Prefect is way more accessible than Dagster.

Braun Reyes

Senior Data Engineer

Endpoint

73.78% infrastructure cost reduction

Tripled production while dramatically reducing spend with infrastructure control

Read story →

Growing community

Prefect community momentum

GitHub stars20.1k

Monthly downloads7.5M

Slack members31k

Growth (YoY)+500%

Dagstertestimonial

Prefect is the best workflow engine out there.

Pete Hunt

CEO

Kraft Analytics Grouptestimonial

Prefect gives us the granular flexibility to build a custom platform that would work for our entire organization, without needing a bloated infra architecture.

Data Platform Engineer

EF Education Tourstestimonial

Our job is to provide data analysts and data scientists the data they need to create data products that drive business value. And beyond that, we focus on enabling our data scientists by removing roadblocks and giving them powerful tools that make their jobs easier. Prefect is allowing us to achieve these objectives.

Mike Grabbe

Principal Data Engineer

dbt Labstestimonial

We use Prefect to orchestrate dbt Cloud jobs alongside other data tools. It brings visibility to our entire pipeline and streamlines deployments.

Alex Welch

Head of Data

How should I choose?

Recommended

Choose Prefect for

Python-first development without code restructuring in your infrastructure
Days to production value vs months of learning curve
Transparent user-based pricing with compute in your environment
All workflow types: ETL, ML, streaming, automation
Free tier (2 users, 5 workflows) running in your infrastructure

Asset-centric

Choose Dagster if

Your team is committed to the Software-Defined Asset paradigm
You primarily build batch data transformation pipelines
You can invest months in paradigm adoption and learning curve
Credit-based pricing model fits your budget planning

Self-serve pricing compared

Dagster+ removed included credits from Solo and Starter plans in May 2026. Full breakdown

	Dagster+ Solo	Dagster+ Starter	Prefect Hobby	Prefect Starter	Prefect Team
Base price	$10/mo	$100/mo	Free	$100/mo	$100/user/mo
Usage charges	$0.040/credit	$0.035/credit	None	None	None
Included serverless	None	None	500 min	75 hrs	225 hrs
Serverless overage	$0.010/min	$0.010/min	N/A	$0.005/min	$0.005/min
Users	1	Up to 10	2	3	4-8

What does it actually cost?

Estimated monthly costs for common pipeline configurations. Dagster+ credits are calculated at one credit per asset materialization.

Small dbt project

50 models, once daily. ~1,500 credits/mo.

Dagster+ Solo$70

Dagster+ Starter$153

Prefect Hobby$0

Prefect Starter$100

Growing startup

200 models 4x daily + 20 jobs. ~24,600 credits/mo.

Dagster+ Solo$994

Dagster+ Starter$961

Prefect Starter$100

Prefect Team$400

Active production team

500 models 4x daily + 50 jobs + events. ~65,000 credits/mo.

Dagster+ Starter$2,375

Prefect Team$400

Start orchestrating in your infrastructure today

Start free with 2 users and 5 workflows. No asset restructuring required. Your code and data stay in your environment.

Start for free Book a demo

From Dagster's CEO

"Prefect is the best workflow engine out there"

— Pete Hunt, CEO, Dagster

Why teams switch from Dagster

Simplicity, predictability, and flexibility

Prefect

Write Python as-is

Add decorators to existing Python functions. No asset restructuring, no IO managers. Your code runs in your infrastructure immediately.

Prefect

Days to value, not months

Teams deploy production workflows in days. No paradigm shift required—just Python orchestration in your environment.

Prefect

Predictable pricing

Transparent user-based pricing. No per-asset costs that scale with your data. Your compute runs in your infrastructure.

Dagster

Developer experience

No asset costume required

No code restructuring required

Days to production, not months

Works with your existing infrastructure

"Prefect is way more accessible than Dagster."

— Braun Reyes, Senior Data Engineer, Clearcover

Prefect

from prefect import flow, task
@task
def extract():
    return get_data()
@task
def transform(data):
    return clean(data)
@flow
def my_workflow():
    data = extract()
    result = transform(data)
    return result

Dagster

from dagster import asset, Definitions
from dagster import IOManager
@asset
def extract_data(context):
    return get_data()
@asset
def transform_data(context, extract_data):
    return clean(extract_data)
# Requires IO managers, repositories
defs = Definitions(
    assets=[extract_data, transform_data],
    resources={"io_manager": IOManager()}
)

Dagster Cost

Prefect Cost

Pricing ModelPay per user, not per run

Pricing transparency

No per-asset tax

60-70% typical cost savings

Free tier: 2 users, 5 workflows

Scale infrastructure without budget surprises

View pricing details

Security & control

Your workflows run in your infrastructure

Deploy on Kubernetes, ECS, Docker

Zero code or data egress

SOC 2 Type II, GDPR, HIPAA ready

Learn about hybrid architecture

Prefect Cloud

Your Network

Control Plane

Metadata

Workers

Data

Prefect Cloud hosts the Control Plane & Metadata. You host execution & data.

ETL pipelines

ML training workflows

Real-time streaming

Event-driven automation

Infrastructure provisioning

Workflow variety

Orchestrate any workflow type

Explore workflow patterns

Data assets

Data assets, hold the cruft

Write tasks, add materialization when needed

Automatic dependency inference from task graphs

Track external assets without restructuring

Learn about Prefect assets

Prefect: Task-First with Optional Materialization

Flexible

from prefect import flow, task
from prefect.assets import materialize
@task
def process_data():
    # Just a task - works perfectly
    return transform(data)
# Add materialization when you need lineage
@materialize("s3://lake/processed.csv")
def tracked_process():
    return transform(data)
@flow
def my_flow():
    # Mix materialized and regular tasks
    result = process_data()
    tracked_process()

Dagster: Asset-Centric Foundation

Required

from dagster import asset, Definitions
@asset  # Everything must be an asset
def process_data(context):
    # Requires asset paradigm
    # Must define IO managers
    return transform(data)
# Needs repository structure
defs = Definitions(
    assets=[process_data],
    resources={"io_manager": io_manager}
)

Why Prefect wins

Simplicity without sacrificing power

Tasks share memory

Native data passing between tasks in your infrastructure. Dagster siloes steps like Airflow requiring IO managers.

Test your logic, not framework

Test functions directly. Dagster requires 50+ lines of boilerplate to mock framework concepts.

Infrastructure that fits your workflow

No control plane required. Lightweight workers or hosted execution in your environment—your choice.

Dynamic by default

Build graphs at runtime based on data. Dagster freezes graphs at import time breaking dynamic workflows.

No asset costume required

Optional asset tracking. Dagster forces every workflow into the Software-Defined Asset model.

Python in, Python out

Write standard Python running in your infrastructure. No DSL replica or framework-specific patterns required.

Feature comparison

See how Prefect compares across key capabilities

Development Experience

Feature	Prefect	Dagster
No rewrites needed
Minimal learning curve		Months
Test without framework mocking
Handle large in-memory data		Siloed

Workflow Capabilities

Feature	Prefect	Dagster
Dynamic DAGs at runtime		Frozen at import
All workflow types supported		Asset-centric only

Data Assets & Lineage

Feature	Prefect	Dagster
Data assets & lineage
Asset tracking approach	Tasks-first, optional	Asset-centric, mandatory

Teams trust Prefect for production workloads

From high-growth startups to Fortune 500

Clearcovertestimonial

Prefect is way more accessible than Dagster.

Braun Reyes

Senior Data Engineer

Endpoint

73.78% infrastructure cost reduction

Tripled production while dramatically reducing spend with infrastructure control

Read story →

Growing community

Prefect community momentum

GitHub stars20.1k

Monthly downloads7.5M

Slack members31k

Growth (YoY)+500%

Dagstertestimonial

Prefect is the best workflow engine out there.

Pete Hunt

CEO

Kraft Analytics Grouptestimonial

Prefect gives us the granular flexibility to build a custom platform that would work for our entire organization, without needing a bloated infra architecture.

Data Platform Engineer

EF Education Tourstestimonial

Mike Grabbe

Principal Data Engineer

dbt Labstestimonial

We use Prefect to orchestrate dbt Cloud jobs alongside other data tools. It brings visibility to our entire pipeline and streamlines deployments.

Alex Welch

Head of Data

How should I choose?

Recommended

Choose Prefect for

Python-first development without code restructuring in your infrastructure
Days to production value vs months of learning curve
Transparent user-based pricing with compute in your environment
All workflow types: ETL, ML, streaming, automation
Free tier (2 users, 5 workflows) running in your infrastructure

Asset-centric

Choose Dagster if

Your team is committed to the Software-Defined Asset paradigm
You primarily build batch data transformation pipelines
You can invest months in paradigm adoption and learning curve
Credit-based pricing model fits your budget planning

Self-serve pricing compared

Dagster+ removed included credits from Solo and Starter plans in May 2026. Full breakdown

	Dagster+ Solo	Dagster+ Starter	Prefect Hobby	Prefect Starter	Prefect Team
Base price	$10/mo	$100/mo	Free	$100/mo	$100/user/mo
Usage charges	$0.040/credit	$0.035/credit	None	None	None
Included serverless	None	None	500 min	75 hrs	225 hrs
Serverless overage	$0.010/min	$0.010/min	N/A	$0.005/min	$0.005/min
Users	1	Up to 10	2	3	4-8

What does it actually cost?

Estimated monthly costs for common pipeline configurations. Dagster+ credits are calculated at one credit per asset materialization.

Small dbt project

50 models, once daily. ~1,500 credits/mo.

Dagster+ Solo$70

Dagster+ Starter$153

Prefect Hobby$0

Prefect Starter$100

Growing startup

200 models 4x daily + 20 jobs. ~24,600 credits/mo.

Dagster+ Solo$994

Dagster+ Starter$961

Prefect Starter$100

Prefect Team$400

Active production team

500 models 4x daily + 50 jobs + events. ~65,000 credits/mo.

Dagster+ Starter$2,375

Prefect Team$400

Start orchestrating in your infrastructure today

Start free with 2 users and 5 workflows. No asset restructuring required. Your code and data stay in your environment.

Start for free Book a demo