Dagster vs Prefect in 2026: After the Acquisition
Prefect acquired Dagster in 2026, but both orchestrators continue. Which one fits your workload, what changed, pricing, and whether you need to migrate.
TL;DR
- On July 13, 2026, Prefect announced it is acquiring Dagster Labs — the two orchestrators are now one company, not rivals. Terms were not disclosed.
- Both products continue. Dagster, Dagster+ and the open-source project stay supported; Prefect's own announcement says "Dagster is core to the strategy of the new combined company," and Dagster's customer FAQ says there is no plan to merge or discontinue it.
- Choose on the execution model, not the logos: Dagster is asset-centric (software-defined assets, lineage, catalog); Prefect is imperative Pythonic flows/tasks built for dynamic and ML/agentic workloads.
- Current releases: Dagster 1.13.26 and Prefect 3.9.0 (both October 2026). Development is active on both.
- Most pages ranking for this query predate the deal and still frame these as independent competitors. They aren't anymore.
Prefect acquired Dagster in July 2026, so the two leading Python orchestrators now share one owner — but both remain fully supported, separate products. Pick Dagster if you want an asset- and lineage-first data platform; pick Prefect if you want flexible, imperative Python flows for dynamic or ML/agentic pipelines. Neither is being sunset, and no migration is required.
Did Prefect really acquire Dagster, and what changed?
Yes. On July 13, 2026, Prefect (Prefect Technologies, Inc.) announced it is acquiring Dagster Labs, and Dagster's site now carries a "Part of Prefect" label. Prefect founder and CEO Jeremiah Lowin framed the goal in the Prefect acquisition announcement as building "the most innovative automation company for the age of AI." Financial terms were not disclosed.
This is the single biggest fact in the Dagster vs Prefect debate, and almost none of the older comparison articles and forum threads account for it. The two projects — both founded in 2018 — spent nearly a decade as direct competitors. Now they're one portfolio.
On the people side, the Dagster announcement confirms about 40 Dagster team members are joining Prefect, and that Dagster founder Nick Schrock is stepping away from the project and company. Schrock's own note captures the logic: "Rather than continuing to converge on each other's strengths from opposite directions, we're combining them."
A note on neutrality: the current page-one results for this query are dominated by parties with a commercial stake — Prefect's own acquisition page, plus orchestration vendors selling competing products. This piece sells neither. Where a claim comes from Prefect or Dagster directly, we've labeled it as their statement.
Is Dagster still being developed, or is it a dead end?
It's actively developed. The Dagster acquisition FAQ is explicit: Dagster will continue as a supported product, there are no plans to merge Dagster into Prefect, and there is no plan to discontinue it. Prefect says Dagster's open source will be "actively developed, not just maintained," and that Dagster+ continues under the Dagster+ brand.
The release cadence backs this up. As of this writing the latest stable Dagster is 1.13.26 (October 2026) and the latest stable Prefect is 3.9.0 (October 2026) — both shipping regular releases. If you read an older post claiming one tool is stagnating, check it against the actual releases pages; a stale docs page is not a stale project.
The honest caveat: the near-term priority for Dagster is stability for current customers, and long-term product direction beyond that is not yet defined in public. Prefect says it plans to bring Prefect's dynamic and agentic strengths into the combined platform over time, but no timelines or milestones have been published.
Dagster vs Prefect: the real technical difference
Ownership aside, these tools still embody genuinely different philosophies — and that difference should drive your choice.
| Dagster | Prefect | |
|---|---|---|
| Core abstraction | Software-defined assets (the data object is the unit) | Flows and tasks (the computation is the unit) |
| Execution model | Asset-based, declarative; lineage/dependencies are first-class | Imperative, dynamic Python; branching/loops at runtime |
| Built-in features | Data catalog, lineage, partitioning, backfills, asset checks | Retries, caching, concurrency limits, event-driven triggers |
| Best fit | Analytics/data platforms, dbt-heavy stacks, strong governance | ML pipelines, dynamic/agentic workloads, "just wrap my Python" |
| Learning curve | Steeper — you model your data assets up front | Gentler — decorate existing Python functions |
| Latest stable | 1.13.26 (Oct 2026) | 3.9.0 (Oct 2026) |
| Cloud product | Dagster+ (Solo $10/mo, Starter $100/mo, Pro custom) | Prefect Cloud (Hobby free, Starter $100/mo, Team $100/user/mo, Enterprise custom) |
| Self-host | Dagster OSS (open source) | Prefect OSS (open source) |
Developer experience. Prefect wins for speed-to-first-pipeline: add @flow and @task decorators to functions you already have and you're running. Dagster asks you to think in assets first, which is more upfront work but pays back in a navigable lineage graph and catalog once the project grows.
UI and observability. Both ship strong web UIs. Dagster's is organized around the asset graph — materialize, inspect lineage, view freshness and checks. Prefect's centers on flow-run history, logs and real-time states. If "what is the current state of my data?" matters more than "what is the current state of my run?", Dagster's model fits better.
Deployment, self-host and cloud. Both are open source and self-hostable, and both offer a managed cloud. Per the current Prefect pricing page: Hobby (free, 2 users), Starter ($100/mo), Team ($100/user/mo), Enterprise (custom, adds SSO/RBAC/SCIM). Per the Dagster+ pricing page, Dagster+ is usage-based on credits (one credit per asset materialization or op execution): Solo $10/mo, Starter $100/mo, Pro custom. Both include serverless compute billed per minute; Dagster+ hybrid deployments carry no compute charge.
What this means for you
If you're an analytics or data-engineering team (dbt, warehouses, governance): Dagster's asset model, lineage and catalog are the stronger fit, and the acquisition FAQ says pricing, contracts and Dagster+ operation are unchanged. Build on Dagster with confidence; just don't bank on a specific long-term roadmap yet.
If you're an ML or platform team with dynamic/agentic workloads: Prefect's imperative, runtime-dynamic model is the more natural fit for ML pipelines and MLOps stacks, and it's now the parent company. Prefect says its next-generation agent orchestration platform will be built on the combined technology.
If you're starting a greenfield project in 2026: Choose on the execution model, not the deal. Prototype both over a weekend — the decorator-and-go vs asset-graph difference is obvious within hours. Dagster's FAQ explicitly tells prospective customers not to delay evaluations.
If you already run one in production: Do nothing urgent. Both vendors confirm no migration is required and no action is needed now. Keep an eye on roadmap announcements over the next few quarters.
Where Airflow fits (dagster vs prefect vs airflow): Airflow remains the incumbent for schedule-driven, DAG-centric batch workflows with the largest plugin ecosystem. Both Dagster and Prefect were built as more Pythonic, modern answers to Airflow's boilerplate. If you're already deep in Airflow and it works, neither acquisition forces a move; if you're replacing it, this comparison is the one that matters.
Frequently Asked Questions
What is the difference between Dagster and Prefect?
Dagster is asset-centric: you define data assets, and it manages lineage, partitioning and a catalog around them. Prefect is computation-centric: you decorate Python functions as flows and tasks that run dynamically at runtime. Dagster suits governed data platforms; Prefect suits flexible, dynamic or ML pipelines.
Did Prefect acquire Dagster?
Yes. Prefect announced on July 13, 2026 that it is acquiring Dagster Labs, confirmed on both companies' official sites. Financial terms were not disclosed. About 40 Dagster staff are joining Prefect, and Dagster founder Nick Schrock is stepping away from the company as part of the transition.
Is Dagster still being developed after the Prefect acquisition?
Yes. Dagster's customer FAQ states it will continue as a supported product, with no plan to merge or discontinue it, and Prefect says Dagster's open source will be "actively developed, not just maintained." Both ship regular releases; Dagster's latest stable is 1.13.26 as of October 2026.
Which is better for data orchestration, Dagster or Prefect?
Neither is universally better. Dagster is better for analytics and data-platform teams that need lineage, a catalog and strong governance. Prefect is better for ML, platform and agentic workloads needing dynamic, imperative Python. Match the tool's execution model to your workload rather than following the acquisition.
Should you use Dagster or Prefect in 2026?
Choose on fit, not the deal — both are supported and actively developed. Pick Dagster for asset- and lineage-driven data platforms, Prefect for dynamic or ML/agentic pipelines. Existing users need no migration. For greenfield projects, prototype both briefly; the design difference is obvious within hours.