Prefect vs Dagster

Quick verdict

Prefect, from Prefect Technologies, describes itself as "a workflow orchestration framework for building data pipelines in Python" that lets you "elevate a script into a production workflow". You decorate functions with @flow and @task and get scheduling, retries, caching and event-based automations. The open source package includes a self-hostable server and UI; Prefect Cloud is the hosted control plane. The current release is 3.8.8 (6 October 2026), for Python 3.10 to 3.14.

Dagster, from Dagster Labs, describes itself as "a cloud-native data pipeline orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model". Its central abstraction is the software-defined asset, a function decorated with @dg.asset that produces a persistent object; ops and jobs cover task-style work. The current release is 1.13.25 (1 October 2026), for Python 3.10 to 3.14. Dagster+ is the hosted platform.

Both are often compared with Apache Airflow; see Apache Airflow vs Prefect and Apache Airflow vs Dagster. None of the three is a connector-based ingestion tool.

Side by side

Key differences

Flows and tasks versus software-defined assets

The same small pipeline (read some orders, total them, refresh daily at 06:00 UTC) shows the difference. Prefect describes the work: a flow that calls three tasks in order, like ordinary Python. Dagster describes the outputs: two assets, where order_total depends on raw_orders by taking it as an argument, and a schedule that materialises both. Both examples follow the official documentation and were syntax-checked with Python 3.14.

# Prefect 3
from prefect import flow, task


@task(retries=2)
def extract() -> list[dict]:
    return [{"id": 1, "amount": 120.0}, {"id": 2, "amount": 80.0}]


@task
def transform(rows: list[dict]) -> float:
    return sum(r["amount"] for r in rows)


@task
def load(total: float) -> None:
    print(f"Total order value: {total:.2f}")


@flow(log_prints=True)
def daily_orders():
    rows = extract()
    total = transform(rows)
    load(total)


if __name__ == "__main__":
    daily_orders.serve(name="daily-orders", cron="0 6 * * *")

# Dagster 1.13 (software-defined assets)
import dagster as dg


@dg.asset
def raw_orders() -> list[dict]:
    return [{"id": 1, "amount": 120.0}, {"id": 2, "amount": 80.0}]


@dg.asset
def order_total(raw_orders: list[dict]) -> float:
    total = sum(r["amount"] for r in raw_orders)
    print(f"Total order value: {total:.2f}")
    return total


daily_refresh = dg.ScheduleDefinition(
    name="daily_refresh",
    cron_schedule="0 6 * * *",
    target=[raw_orders, order_total],
)

defs = dg.Definitions(
    assets=[raw_orders, order_total],
    schedules=[daily_refresh],
)

Prefect's documentation stresses that flows are not restricted to predefined DAGs: loops, conditions and dynamic fan-out (for example task.map) are ordinary Python, and a flow can be called like a function in a test. Dagster's model trades some of that freedom for a known graph of assets: I/O managers store and load asset values, each materialisation is recorded, and the UI can show when a table was last refreshed and what depends on it. When an asset writes to a database itself, you declare the dependency with deps=[...] instead of passing data.

Scheduling, events and automation

Prefect attaches schedules to deployments: cron, interval and RRule (for calendar rules such as "the last weekday of each month"), set in code, prefect.yaml, the CLI or the UI. Its automations connect triggers to actions. Triggers include flow run state changes, work pool and deployment status, metric thresholds such as duration or lateness, custom events, and the absence of an expected event; actions include running a deployment, cancelling or suspending runs, pausing schedules, and notifications or webhooks. The documentation lists the dedicated email action and incident declaration as Prefect Cloud only.

Dagster offers schedules (UTC by default, or set execution_timezone), sensors for external changes, asset sensors that react to materialisations, GraphQL triggers, and Declarative Automation, where each asset carries a condition such as dg.AutomationCondition.eager() to update whenever dependencies change or dg.AutomationCondition.on_cron("@hourly"). Declarative Automation requires the default automation condition sensor to be enabled in the UI. The difference in emphasis: Prefect reacts to events about runs and systems; Dagster can also reason about whether each asset is up to date relative to its parents.

UI and observability

Prefect's UI, available on the self-hosted server and in Prefect Cloud, covers flow runs and task runs with their states, deployments and schedules, work pools and automations. Prefect Cloud adds workspaces, user management, and RBAC, SSO and audit logs on paid plans, plus AI-generated log summaries in automation templates.

Dagster's UI centres on the asset lineage graph and each asset's materialisation history, with runs, schedules and sensors alongside; locally it starts with dg dev on port 3000. Dagster+ adds Insights for platform trends and cost, alerts to Slack, PagerDuty and email for failures and SLA violations, an asset catalog with advanced search and column lineage, RBAC with audit logs, and branch deployments.

Deployment: self-hosting, Prefect Cloud and Dagster+

Self-hosted Prefect runs with prefect server start, the prefecthq/prefect Docker image or a Helm chart; the docs say PostgreSQL 14.9 or higher is required for multi-server deployments. Process, Docker and Kubernetes work pools run on a self-hosted server through workers. Prefect Cloud hosts the API, database and UI and adds push work pools (AWS ECS, Google Cloud Run, Azure Container Instances, Modal) that need no worker, plus Prefect Managed pools.

Self-hosted Dagster runs a webserver, one daemon (schedules, sensors and run queuing) and code location servers, with SQLite storage by default and PostgreSQL for production; the docs cover Kubernetes, Docker Compose, AWS and Google Cloud. Dagster+ offers Serverless, where your code runs in Dagster's environment, and Hybrid, where Dagster+ runs the control plane and you run the execution layer. Prefect's hybrid work pools follow a similar split: the control plane is hosted, and a worker in your infrastructure runs the flows.

Pricing and licensing

Both open source projects are free under the Apache-2.0 licence and can be self-hosted at the cost of your own infrastructure.

Prefect Cloud is priced by plan. As listed on prefect.io/pricing in October 2026: Hobby is free (2 users, 1 workspace, up to 5 deployments, 500 minutes per month of Prefect Serverless, 7 days of run retention); Starter is USD 100 per month, billed monthly, for 3 users and up to 20 deployments; Team is priced per user per month; Enterprise is custom and billed annually.

Dagster+ is priced by plan plus credits, defined on its pricing page as "the sum of asset materializations and ops executed". As listed on dagster.io/pricing in October 2026: Solo is USD 10 per month (1 user, 1 deployment) with credits at USD 0.040 each; Starter is USD 100 per month (up to 3 users) with credits at USD 0.035; Pro is priced on request. Serverless compute is billed per minute on top, and Solo and Starter have a 30-day trial.

The units differ: Prefect Cloud's cost follows plan, seats and serverless minutes, while Dagster+ also grows with the number of materialisations and op executions. We do not estimate bills; model your own run volumes against each pricing page.

Pricing checked on the vendors' official pages on 7 October 2026. Prices change; confirm before buying.

Where each one leads

Limitations

When to choose each

When neither is right

• You want a managed orchestrator from AWS or Google Cloud, or an Airflow-based estate already exists: look at Apache Airflow; see Apache Airflow vs Prefect and Apache Airflow vs Dagster.
• You mainly need to copy data from SaaS applications and databases into a warehouse: a connector-based ingestion tool may be enough on its own; see Apache Airflow vs Airbyte, Apache Airflow vs Fivetran and the best ETL tools guide.
• Your transformations are a few SQL statements in one database: the database's own scheduler may be simpler; ETL vs ELT and ETL vs data pipeline explain where each step usually lives.

Final recommendation

Frequently asked questions

Sources

• Prefect documentation: Quickstart
• Prefect documentation: Flows
• Prefect documentation: Schedules
• Prefect documentation: Automations
• Prefect documentation: Work pools
• Prefect documentation: Self-hosted server
• Prefect Cloud pricing
• prefect on PyPI
• Dagster documentation: Quickstart
• Dagster documentation: Asset dependencies
• Dagster documentation: Schedules
• Dagster documentation: Declarative Automation
• Dagster documentation: dbt integration
• Dagster documentation: OSS deployment architecture
• Dagster+ documentation
• Dagster+ pricing
• Dagster releases (GitHub)

Checked October 2026.

How we research comparisons: our editorial method.