Choose Airflow if you need a mature, widely adopted scheduler for time-based batch pipelines and have platform engineers to run it. Choose Prefect if your team writes plain Python and wants dynamic workflows with little ceremony. Choose Dagster if you think in data assets and want lineage and testing built in. Google Cloud Dataflow is a different kind of tool: it processes data, and it does not orchestrate other systems.
The rest of this guide explains why, with the trade-offs that matter in production. It also covers a question most comparisons skip: what to do when the pipeline is only part of a larger, regulated business process.
TL;DR
- Airflow models work as a DAG of tasks on a scheduler. It suits time-partitioned batch jobs and has the broadest integration catalog, but self-hosting it means real operational overhead.
- Prefect runs ordinary Python as flows and builds the execution graph at runtime. It is the lightest to adopt and the best fit for dynamic, event-shaped work.
- Dagster is asset-oriented: tables, files and models are first-class, with lineage and freshness rules. It rewards teams that care about what data exists, not just which tasks ran.
- Dataflow is a managed Apache Beam service for batch and streaming processing. Teams often pair it with an orchestrator rather than choosing between them.
- If the real goal is automating a regulated business process (claims, KYC, audit workflows), the orchestrator is one component. The system around it matters more.
Table of Contents
- The short comparison
- How each tool thinks about work
- Airflow vs Prefect in practice
- Prefect vs Dagster
- What each one costs to run
- A decision guide by team type
- Our perspective
- When the orchestrator is not the whole answer
- Build it as a system you own
- FAQ
- Sources
The short comparison
| Airflow | Prefect | Dagster | Dataflow | |
|---|---|---|---|---|
| Core model | DAG of tasks | Python functions as flows | Software-defined assets | Beam pipelines |
| Graph defined | Statically, in DAG files | At runtime | From asset definitions | In the pipeline code |
| Strongest at | Scheduled batch, huge integration catalog | Dynamic Python workflows, fast start | Lineage, testing, observability | Large-scale batch and streaming processing |
| Hosting | Self-host or managed (Astro, Cloud Composer) | Open source or Prefect Cloud | Open source or Dagster+ | Fully managed on Google Cloud |
| Main cost driver | Infrastructure and engineer time | Task runs, compute, seats | Infrastructure or managed plan | Processing resources |
How each tool thinks about work
The biggest difference is not features. It is the unit each tool treats as primary, and that shapes everything from debugging to testing.
Apache Airflow: the DAG and the scheduler
Apache Airflow is a community-built platform to author, schedule and monitor workflows. Pipelines are defined in Python, with Jinja templating, and a modular architecture uses a message queue to coordinate any number of workers. It ships operators for GCP, AWS, Azure and many third-party services, which is a big reason it became the default. The project is licensed under Apache License 2.0, and its GitHub repository has been public since 2015.
Airflow represents a workflow as a directed acyclic graph of tasks. It controls when tasks run, how they depend on each other and what happens when one fails. According to ZenML’s comparison, it does not automatically turn datasets or models into versioned artifacts, so ML teams usually bolt on a tracker or registry. Its mature scheduling is well suited to time-partitioned batch jobs.
Prefect: plain Python, graph built at runtime
Prefect describes itself as workflow orchestration for data, ML and agents: you write workflows as plain Python, and it runs them with scheduling, retries, caching and recovery. Its open-source framework and Prefect Cloud share the same core engine.
Prefect’s own comparison page says it treats workflows as decorated functions that build execution graphs at runtime, which makes task mapping and branching on live data straightforward. Airflow, by contrast, expects you to restructure code into DAG objects and operators. Keep in mind that this is Prefect’s description of the difference, and teams with deep Airflow experience will weigh it against their own habits.
If your workflows branch on what the data looks like at run time, or you want to wrap existing scripts quickly, this is where Prefect feels lighter. For a deeper look at flexible execution versus rigid DAGs in the wider sense of business workflows, this guide on mapping a workflow and where AI fits is a good companion.
Dagster: assets first
Dagster inverts the question. Instead of asking which tasks should run, you declare which data products should exist. In ZenML’s description, an asset can be a table, file, dataset, feature set or model, and its definition says what should exist and how to produce it. Checks, partitions, metadata, owners and freshness rules are supported.
That is why Dagster’s own comparison argues that asset definitions show lineage from raw data to a report, and that dagster dev starts a local instance so you can define and test workflows without external dependencies. Airflow’s task focus, it says, gives less visibility into lineage. Treat that as the vendor’s framing, though the asset-versus-task split is real and widely reported.
Google Cloud Dataflow: processing, not orchestration
Dataflow is a fully managed service built on Apache Beam. It provides unified batch and stream processing at scale, with autoscaling and exactly-once processing by default. It is not a competitor to Airflow in the usual sense. Dataflow does the heavy data transformation, and an orchestrator decides when it runs and what follows. Dataflow is often integrated with Airflow for overall job coordination in GCP-centered projects.
So if you searched for Airflow versus Dataflow, the useful answer is usually “both”: Dataflow for scalable processing, an orchestrator for dependencies, scheduling and retries across systems.
Airflow vs Prefect in practice
Development experience
Prefect’s pitch is that your pipeline is your code: add decorators and the function becomes a flow. Airflow asks for more structure up front, which pays off in big teams with conventions, and costs time for small ones. Dagster sits in between, with a stronger opinion about assets but good local testing.
Dynamic workflows
Prefect supports creating tasks at runtime based on data and conditions, while Airflow has traditionally relied on static DAGs, per Prefect’s comparison. If your number of tasks depends on how many files arrived this morning, Prefect’s model matches the problem. If your job runs the same twelve steps every night, Airflow’s static graph is a feature, because the schedule is predictable and reviewable.
Monitoring and failure handling
Prefect advertises built-in retries, caching and recovery. Airflow has retries too, but running it well typically involves schedulers, workers, metadata databases and external monitoring tools. Dagster adds asset-level observability on top.
One caveat on Prefect: its core record is flow and task run state, with logs, artifacts and events. According to DeployOpen, lineage between data objects is not inferred by Prefect, and exists only to the extent your team records it. If lineage is a compliance requirement, plan for it from day one, or lean toward Dagster.
Integrations and ecosystem
Airflow’s operator catalog is its moat. If you need a prebuilt connector for an obscure system, Airflow probably has one. Prefect and Dagster cover the mainstream well and rely on Python for the rest, which is rarely a problem for engineering teams.
Prefect vs Dagster
The two are closer than either is to Airflow, since both are modern, Python-native and friendly to local development. The deciding question is what you want the tool to know about.
- Pick Prefect when the unit of work is a run: call an API, process a batch, trigger an agent, retry on failure. It is flexible and has a gentle learning curve.
- Pick Dagster when the unit of work is a dataset or model that downstream users depend on, and you want freshness rules, owners and lineage in the same tool. Dagster’s daemon and webserver are lighter than Airflow’s scheduler stack, though its asset materialization model can surprise teams who expect imperative scheduling.
Teams with a small platform group and many one-off or event-driven jobs tend to enjoy Prefect. Analytics-engineering-heavy teams with a layered warehouse tend to enjoy Dagster.
What each one costs to run
All three have open-source cores, so the license is free. The real cost is who operates it.
Airflow. Self-hosting means running a scheduler, webserver, Postgres metadata database, a message broker such as Redis or RabbitMQ, and a worker pool on Celery or Kubernetes, plus SSL, log storage, DAG deployment tooling and upgrade discipline. The metadata database is a common bottleneck at high DAG counts. Managed options include Astronomer’s Astro and Google Cloud Composer. If you are weighing running this yourself against a managed service, the logic is the same as in this piece on on-premises versus cloud servers.
Prefect. Per Prefect’s pricing page at the time of writing, Prefect Cloud has a free Hobby tier (2 users, up to 5 deployments), a Starter plan at $100/month (3 users, up to 20 deployments), a Team plan at $100/user/month (up to 100 deployments, service accounts, 24-hour audit log) and a custom Enterprise plan with SSO, RBAC, PrivateLink and a 99.9% uptime SLA. Verify current limits on that page before budgeting.
Dagster. The managed option is Dagster+, and Astro and Cloud Composer cover Airflow, per this overview of managed options. Check each vendor’s current pricing page, since models are consumption-based and change.
Dataflow. Billed on the processing resources you use, so cost follows data volume.
A decision guide by team type
- Large data platform team, many scheduled batch pipelines, heavy integration needs: Airflow, ideally managed.
- Small team, Python-fluent, dynamic or event-driven jobs, ML and agent workloads: Prefect.
- Analytics or ML team that cares about lineage, freshness and testing: Dagster.
- Streaming or very large batch transformations on Google Cloud: Dataflow, orchestrated by one of the above.
- Regulated firm that wants the process automated, not just the pipeline scheduled: see the next sections.
Our perspective
We see this comparison from the other side of the table. Most firms that come to us do not have a data engineering problem. They have a law, accounting, insurance, healthcare or advisory practice where skilled people spend their days on repetitive document and data work.
For those firms, picking Airflow, Prefect or Dagster is a useful decision but a small one. The harder questions are what the system is allowed to do, where the data lives, who can audit a decision six months later, and who owns the result. An orchestrator moves tasks. It does not answer those questions for you.
That is why we do not sell AI projects. We invest engineering into established service businesses and co-build an AI-native product with them. You bring the domain expertise and the client base. We automate delivery and design for private, self-hosted deployment so client data stays under your control. Our team comes from regulated backgrounds, including ISO 27001, pharma and finance, where a wrong answer has consequences.
Whichever orchestrator sits underneath, the principle is the same: own the system, do not rent a stack of tools you cannot explain to an auditor.
Matevz
When the orchestrator is not the whole answer
Imagine a claims team at an insurance broker. A scheduler can pull files every night. But deciding which claims need human review, keeping an audit trail, handling a failed step without losing context, and resuming safely all sit above the scheduler. Our write-up on AI workflow orchestration that resumes covers how we think about that layer.
The pattern holds across law firms automating document review, accounting practices automating bookkeeping workflows, and medical billing companies processing claims. The orchestration engine is a choice you can make in a week. The compliance model, data boundaries and workflow design take the real effort.
Build it as a system you own
If you run a services firm and want automation that becomes an asset rather than a subscription, we can help. Our AI OS and the wider set of OS products are built around ownership of the IP, data sovereignty and audit trails. Engagements range from partnership and venture modes to funded builds, and you can see the options on our main site.
We do not publish prices for these engagements, because scope depends on your processes and your regulatory environment. Reach out through the site and we will talk through what a build would involve for your practice.
FAQ
Is Prefect better than Airflow?
It depends on the work. Prefect is lighter to adopt and handles dynamic Python workflows well. Airflow has a larger integration catalog and a long record with scheduled batch pipelines. Small, Python-first teams usually lean Prefect, and large platform teams with many scheduled jobs often stay on Airflow.
Should I choose Dagster or Prefect?
Choose Dagster when datasets and models are the thing you manage and you want lineage and freshness rules built in. Choose Prefect when you mainly need reliable runs of flexible Python code with retries and recovery. Prefect does not infer lineage between data objects, so teams that need it must record it themselves.
How is Airflow different from Dataflow?
Airflow orchestrates: it schedules tasks, manages dependencies and handles retries across systems. Dataflow processes data, running batch and streaming Apache Beam pipelines as a managed service. Many GCP teams use an orchestrator to trigger Dataflow jobs as one step in a larger workflow.
Are Airflow, Prefect and Dagster free?
The open-source cores are free to run, and Airflow is under the Apache License 2.0. You then pay for infrastructure and the engineers who maintain it, or for a managed plan such as Prefect Cloud, Astro or Dagster+. Prefect Cloud has a free Hobby tier with limits.
Sources
- Apache Airflow
- apache/airflow on GitHub
- Prefect: workflow orchestration for data, ML and agents
- Prefect vs Airflow
- Prefect Cloud pricing
- ZenML: Dagster vs Airflow vs ZenML
- Dagster: Dagster vs Airflow feature comparison
- Orchestra: Airflow vs Dagster
- Data Workers: Airflow vs Dagster
- DeployOpen: Dagster vs Prefect
- DataDank: Airflow vs Dagster vs Prefect
- Google Cloud: Dataflow overview
- ModelDock: Building a data platform on GCP
- Perimattic: Managed Apache Airflow hosting
