Part 5 of the “Orchestrating dbt without dbt Cloud” series. This is the comparison article. If you want the full setup guide for any specific tool, the individual articles are linked throughout.
Why This Series Exists
dbt Cloud is a good product. It handles scheduling, observability, CI, and artifact management in one place, and for teams that can justify the cost it removes a lot of operational overhead.
But dbt Cloud is also expensive at scale, and it creates a level of vendor lock-in that many teams are not comfortable with. The pricing model has changed multiple times over the years, and teams that built their entire orchestration story on top of it have found themselves renegotiating contracts from a weak position.
The good news is that the open-source ecosystem around dbt orchestration has matured significantly. You can get most of what dbt Cloud offers, for free, by combining dbt Core with one of the orchestrators covered in this series. The tradeoff is that you are responsible for running and maintaining the infrastructure yourself.
This article is the summary of that series. We ran the same dbt project through four different orchestrators, covering the same four scenarios each time: a daily full build, an hourly smoke check, a source freshness check, and a manual full refresh of an incremental model. Here is what we found.
The companion repository with all the code is at github.com/p-munhoz/dbt-orchestrator-comparison.
The Four Orchestrators at a Glance
Before getting into the details, here is a high-level summary of what each tool brings to the table.
Dagster treats dbt models as first-class Software-Defined Assets. Every dbt node becomes a Dagster asset with its own run history, metadata, and lineage. You get the deepest dbt integration of any open-source orchestrator, at the cost of a steeper learning curve and more infrastructure to run.
Airflow with Cosmos is the most widely deployed option in the industry. Astronomer Cosmos turns each dbt node into an Airflow task, giving you model-level visibility inside a tool most data engineers already know. The tradeoff is real infrastructure overhead and a DAG parsing model that can cause issues at scale.
Prefect is the lowest-friction path to a proper orchestrator. You write Python functions, decorate them with @flow, and get scheduling, retries, and a UI without the complexity of Airflow or Dagster. The tradeoff is that it gives you less dbt-native visibility, and its execution model requires a long-running process that you are responsible for keeping alive.
GitHub Actions is not really an orchestrator. It is a CI/CD platform that happens to support cron scheduling. For small teams with simple pipelines it can be genuinely sufficient, and it requires zero infrastructure. For anything beyond a daily dbt build, its limitations show quickly.
Side by Side Comparison
| Dagster | Airflow + Cosmos | Prefect | GitHub Actions | |
|---|---|---|---|---|
| dbt integration depth | Native assets per node | Task per node via Cosmos | Flow level, subprocess | Step level, subprocess |
| Per-model observability | Yes | Yes | No | No |
| Infrastructure to run | Medium | High | Low | None |
| Learning curve | High | Medium | Low | Very low |
| Scheduling reliability | Daemon handles it | Scheduler handles it | Requires running process | GitHub handles it |
| Retry granularity | Per op / per asset | Per task | Per flow | Per job |
| Python-first | Yes | Partial | Yes | No |
| Slim CI support | Via asset selection | Via cosmos select | Via CLI | Native with state |
| Self-host complexity | Medium | High | Low | None |
| Best team size | Any | Medium to large | Small to medium | Solo or small |
| Community maturity | Growing fast | Very mature | Growing | Very mature |
| Cost | Free | Free | Free or Cloud free tier | Free within limits |
dbt Integration Depth
This is where the tools diverge most significantly and where your choice will have the most day-to-day impact.
Dagster: models as assets
Dagster is the only tool in this list where dbt models are genuinely first-class objects. When you use @dbt_assets, Dagster parses your manifest and creates one asset per dbt node. From the Dagster UI you can see the full lineage graph, click on any model and see its last materialization time, inspect per-model logs, and selectively re-materialize parts of the graph without writing any new code.
This is the closest open-source equivalent to the dbt Cloud IDE experience. If your team lives in dbt and model-level observability is a priority, Dagster is the clear winner here.
Airflow + Cosmos: tasks with lineage
Cosmos gives you something very similar in practice: one Airflow task per dbt node, with the dependency graph correctly wired from the dbt manifest. In the Airflow UI you can see which specific model failed, click into its task logs, and retry just that task.
The difference from Dagster is that Airflow tasks are ephemeral: once a DAG run finishes, you are looking at run history rather than a live asset graph. There is no concept of “when was this model last materialized successfully” in the way Dagster tracks it. For most teams this distinction does not matter much in practice, but it is worth being aware of.
Prefect and GitHub Actions: flow level only
Both Prefect and GitHub Actions treat a dbt run as a single unit. You get a pass or fail for the entire invocation, and you need to read through logs to understand which model failed. This is a real limitation for larger projects.
With the subprocess approach used in the Prefect flows in this series, you do get real-time log streaming in the Prefect UI, which helps. But it is still fundamentally different from seeing a graph of individual model statuses.
Infrastructure and Operational Overhead
GitHub Actions: zero
Nothing to run, nothing to maintain. Push a workflow file to your repository and GitHub handles the rest. Scheduling, secrets management, run history, and failure notifications are all built in.
The catch is that GitHub Actions is not in your control. If GitHub has an outage, your pipeline does not run. Scheduled workflows can also be delayed under high load, and they are silently disabled after 60 days of repository inactivity.
Prefect: low, with a catch
Starting the Prefect server and writing your first flow takes about 15 minutes. The UI is clean, the API is intuitive, and the Python-first model means there is almost nothing new to learn if your team already writes Python.
The catch is the execution model. Unlike Dagster and Airflow, the Prefect server does not execute anything on its own. There is no daemon that picks up scheduled runs automatically. You need a serve() process or a worker running and connected to the server before your 6am run will actually happen. Stop that process and your schedules go dark. This is the most common operational surprise teams hit with Prefect, and it is worth being explicit about before you commit to it.
Prefect Cloud removes this concern for the control plane side, but you still need a running worker or serve() process on your own infrastructure for execution.
Airflow: high
Airflow requires a scheduler process, a webserver, and a metadata database (SQLite works locally, but Postgres is strongly recommended for production). In a production setup you will likely also want a Celery or Kubernetes executor for parallel task execution, which adds more moving parts.
The operational surface area is real. Airflow is well understood and well documented, but it demands more from the team that runs it than any other option in this series.
Dagster: medium
Dagster needs a webserver and a daemon. The daemon is what makes scheduling reliable: it runs in the background and picks up scheduled jobs automatically without you doing anything extra, which is a meaningful advantage over Prefect. In production you would also want a persistent database for run storage rather than the default SQLite.
The footprint is smaller than Airflow and larger than Prefect, but the daemon-based execution model means you get Airflow-level scheduling reliability without the full Airflow infrastructure burden.
Scheduling Reliability
This is a dimension that does not get enough attention in tool comparisons.
| Tool | What handles scheduling | What happens if it goes down |
|---|---|---|
| GitHub Actions | GitHub infrastructure | Run does not happen if GitHub is down |
| Dagster | Dagster daemon | Schedules pause until daemon restarts |
| Airflow | Airflow scheduler | Schedules pause until scheduler restarts |
| Prefect | serve() process or worker | Schedules pause until process restarts, runs may be missed |
The key difference with Prefect is the “runs may be missed” part. Dagster and Airflow both have catch-up mechanisms: when the daemon or scheduler restarts after a period of downtime, it knows which scheduled runs it missed and can execute them in order. Prefect’s behavior here depends on your configuration and whether you have catchup enabled on your deployments. By default, missed runs are not guaranteed to be picked up.
For a daily dbt build this may be acceptable. For a pipeline that feeds a live dashboard or a financial report, it matters.
The Decision Framework
Rather than prescribing a single answer, here is a set of questions that should point you toward the right tool for your context.
Start here: how complex is your pipeline?
If your pipeline is essentially a daily dbt build with no dependencies on external events, no complex retry logic, and no need for per-model observability, GitHub Actions is worth trying first. It is free, it requires nothing new, and you can always migrate to a proper orchestrator later when you actually need one.
Does your team already use Airflow?
If yes, adding Cosmos to your existing Airflow deployment is almost certainly the right call. The integration is mature, the operational cost of adding dbt support is low, and you avoid introducing a second orchestration tool into your stack. If no, think carefully before adopting Airflow from scratch: the infrastructure overhead is real and there are lighter options available.
Do you need per-model observability?
If yes, the choice is between Dagster and Airflow with Cosmos. Dagster gives you a more native experience (models as assets, live lineage graph, selective re-materialization). Airflow gives you per-task visibility within a tool that may already exist in your organization. If you are starting fresh and observability is a priority, Dagster is the stronger choice.
How much infrastructure can you realistically maintain?
Be honest about this. A sophisticated orchestrator that nobody has time to maintain is worse than a simple one that just works. If your team is small or infrastructure ownership is unclear, Prefect or GitHub Actions are more forgiving choices.
Is dbt the center of your pipeline, or one part of a larger workflow?
If dbt is one part of a larger workflow that also includes API calls, file transfers, ML model training, and so on, you want an orchestrator with a broad ecosystem of integrations. Airflow wins here, with the widest selection of operators and hooks for external systems. Dagster is a reasonable second. Prefect can handle Python-based tasks well but has a smaller ecosystem of pre-built integrations.
Summary Recommendations
Use GitHub Actions if you are a solo practitioner or small team, your pipeline is simple, and you want to get something working today without learning new tooling.
Use Prefect if you want a proper orchestrator with a UI and scheduling but want to minimize infrastructure overhead. Be prepared to manage the execution process carefully, especially around the “no daemon” limitation.
Use Airflow with Cosmos if Airflow is already in your stack, or if you need the broadest ecosystem of integrations alongside dbt. Accept the infrastructure overhead as a known cost.
Use Dagster if dbt is central to your stack and you want the best possible native integration, per-model observability, and a software engineering approach to your data platform. Accept the learning curve as an investment.
Other Ways to Orchestrate dbt
This series covered four tools in depth. It is not exhaustive. Here is an honest map of the rest of the landscape, organized by how likely each option is to be relevant to a typical dbt user.
Mage
Mage is an open-source orchestrator that positions itself as an all-in-one platform: data ingestion, transformation, and orchestration in a single tool. It has native dbt support built in, meaning you can run individual dbt models as blocks inside a Mage pipeline and mix them with Python, SQL, or R blocks in the same workflow.
The appeal is obvious if your team is tired of stitching together separate tools for ELT, orchestration, and monitoring. The catch is that Mage OSS (the self-hosted version) is more of a development environment than a production orchestrator, and the richer features live behind Mage Pro (the paid product). The open-source project also has fewer active contributors than Airflow, Dagster, or Prefect, which is worth factoring into a long-term adoption decision.
Worth evaluating if: you want a single platform that handles ingestion and orchestration together, and you are comfortable with the OSS vs Pro boundary.
Kestra
Kestra is a newer declarative orchestrator that defines workflows in YAML rather than Python. It has a dbt plugin that lets you run dbt commands as tasks in a Kestra flow. The UI is clean and it has gained meaningful traction, particularly among teams that prefer a low-code or YAML-first approach over writing Python DAGs.
One caveat worth noting: some community feedback suggests the open-source version is less fully featured than the paid offering, and community support is more limited than the major tools. Worth investigating the gap between OSS and cloud before committing.
Worth evaluating if: your team prefers YAML-based pipeline definitions and you want something lighter than Airflow without going full Python.
Argo Workflows
Argo Workflows is a Kubernetes-native workflow engine. Pipelines are defined as YAML manifests and executed as Kubernetes pods. It has good dbt support: you run dbt commands inside a container, and Argo handles scheduling, dependency ordering, and retries.
The integration is straightforward in principle, but the operational surface area is significant. You need a Kubernetes cluster, you need to understand Kubernetes CRDs, and your dbt project needs to be containerized. For teams already running Kubernetes in production, Argo is a natural fit. For teams that are not, the overhead is hard to justify just to orchestrate dbt.
Worth evaluating if: you are already running Kubernetes and want to unify your orchestration layer across dbt and other workloads.
Flyte
Flyte is a Kubernetes-native orchestrator built primarily for ML and data science workflows. It has a dbt plugin and good support for type safety and reproducible executions. Its strength is in compute-intensive, ML-heavy pipelines where you need strong isolation between tasks and reliable GPU scheduling.
For pure dbt orchestration, Flyte is significant overkill. The Kubernetes requirement, the strongly-typed task definitions, and the ML-first design philosophy make it a poor fit unless dbt is one small part of a larger ML pipeline that Flyte is already managing.
Worth evaluating if: dbt is one step in a larger ML pipeline and your team is already using Flyte or considering it for model training.
Luigi
Luigi is the oldest Python orchestrator in common use, originally developed at Spotify. It works by defining task classes with explicit dependency declarations. dbt can be invoked from a Luigi task with a simple subprocess call.
The honest assessment is that Luigi has largely been superseded. It has no active development to speak of, its UI is minimal, and it lacks the retry semantics, observability, and ecosystem integrations of modern tools. If you encounter it in a legacy codebase, you know why it is there. Starting a new project with Luigi today is hard to justify.
Worth evaluating if: you are maintaining a legacy pipeline that already uses Luigi and migration cost is not justified.
Cloud-Native Schedulers
A few cloud-specific options are worth mentioning for teams already deep in a particular cloud ecosystem.
Google Cloud Workflows and Cloud Composer are the GCP-native options. Cloud Composer is managed Airflow, which means you get Airflow’s feature set without running it yourself. Cloud Workflows is a lighter serverless option for simpler pipelines. If your entire stack lives in GCP, both are worth pricing out before spinning up self-hosted infrastructure.
AWS Step Functions can orchestrate dbt by invoking ECS tasks or Lambda functions that run dbt commands. It is fully serverless and integrates naturally with the rest of the AWS ecosystem. The trade-off is that Step Functions workflows are defined in JSON or YAML state machines, which is more verbose and less ergonomic than writing Python. It is a reasonable option for AWS-first teams with simple dbt pipelines but becomes unwieldy for complex dependency graphs.
Azure Data Factory is Microsoft’s managed orchestration service. It has a dbt integration and works well for teams already committed to the Azure ecosystem. The visual pipeline editor can appeal to less technical stakeholders, though it adds a layer of indirection that data engineers often find frustrating.
Worth evaluating if: you are already heavily invested in one cloud provider and want to minimize the number of tools you self-host.
Managed Versions of the Tools We Tested
All four tools covered in this series have managed or partially managed offerings that reduce the operational burden of self-hosting:
Astronomer is the managed Airflow platform from the team behind Cosmos. You get Airflow with reduced infrastructure overhead, enterprise support, and a better deployment experience. If Airflow is the right fit but self-hosting is a concern, Astronomer is worth pricing out.
Dagster Cloud follows the same model as Prefect Cloud: managed control plane, your execution infrastructure. The free tier is generous for small teams and the developer experience is identical to self-hosted.
Prefect Cloud as discussed in Part 3, removes the need to self-host the Prefect server entirely. The free tier covers most individual and small-team use cases.
The Repository
All four orchestrators are implemented against the same dbt project in a single monorepo. The dbt project uses DuckDB so you can run everything locally without any cloud credentials. Each orchestrator folder has its own README with setup instructions.
dbt-orchestrator-comparison/
├── dbt_project/ # shared dbt project (DuckDB)
├── orchestrators/
│ ├── dagster/ # Part 1
│ ├── airflow/ # Part 2
│ ├── prefect/ # Part 3
│ └── github-actions/ # Part 4
└── Makefile
Clone it, run make build from the root to verify the dbt project works, then follow the README in whichever orchestrator folder matches your interest.
github.com/p-munhoz/dbt-orchestrator-comparison
This wraps up the series. If you found it useful, the newsletter covers similar deep dives on data engineering tooling every two weeks.
Series: Orchestrating dbt without dbt Cloud Part 1: Dagster · Part 2: Airflow + Cosmos · Part 3: Prefect · Part 4: GitHub Actions