Apache Airflow is still the best data orchestration tool for most teams in 2026, because every major cloud and Astronomer sell it managed and its open-source core is free. Dagster is the better pick for asset-centric dbt and warehouse teams, and Prefect suits Python-first teams that want flat, seat-based pricing. For 50 daily pipelines, managed plans in our scenario run from about $119 to $950 a month, while the open-source editions cost nothing in licences but need servers and an engineer to keep them running.
Airflow, Dagster, Prefect and six other orchestrators compared on licence, managed pricing and what 50 daily pipelines cost to run.
Data orchestration tools schedule your pipelines, run their steps in the right order, retry what fails and tell you when something breaks. The open-source cores are free, so the real decision is who operates the scheduler and how the managed version bills you. Our view: pick the orchestrator your team can operate, then compare managed plans on the billing unit that grows fastest in your workload (credits, execution minutes, seats or always-on hours).
One ownership change matters this year. Prefect announced on July 13, 2026 that it is acquiring Dagster Labs, and both teams state that Dagster keeps its name, open-source licence and hosted product. Two of the top three picks now sit inside one company, which is worth knowing before you sign a multi-year contract.
Toolradar data: our ETL and data pipelines category tracks 113 tools, and 61% of them offer a free or freemium plan. Orchestrators are one slice of that wider category, which also holds connectors, ELT services and transformation tools; the category statistics cover all of it.
Methodology: we compared nine orchestrators from the 113 pipeline tools we track, on licence, deployment model and vendor-published managed pricing, all read on vendor pages on October 29, 2026. No tool paid for its place in this ranking. We did not run the tools ourselves; the cost scenario below is arithmetic on printed prices.
Top Picks
Selected by comparing open-source licences, vendor-published managed pricing and fit for a team running about 50 scheduled data pipelines. How we rate
The open-source standard for scheduling data pipelines, sold managed by Astronomer, AWS and Google.
Starting price
Free; managed from $0.35/hr
4.4 (129 reviews)
Best for
Most data teams that want a free core and a choice of three managed providers.
An asset-based orchestrator that models the tables and files your pipelines produce, with built-in lineage.
Starting price
Free OSS; Dagster+ from $10/mo
Best for
Analytics engineering teams built around dbt and a cloud warehouse.
Python-first orchestration with a free hosted tier and flat seat-based pricing that ignores run volume.
Starting price
Free Hobby; paid from $100/mo
4.5 (125 reviews)
Best for
Python teams that want predictable bills regardless of how many runs they schedule.
| Tool | Starting price | Rating | Best for |
|---|---|---|---|
| Apache Airflow | Free; managed from $0.35/hr | 4.4 129 reviews | Most data teams that want a free core and a choice of three managed providers. |
| Dagster | Free OSS; Dagster+ from $10/mo | n/a | Analytics engineering teams built around dbt and a cloud warehouse. |
| Prefect | Free Hobby; paid from $100/mo | 4.5 125 reviews | Python teams that want predictable bills regardless of how many runs they schedule. |
| Kestra | Free OSS; Cloud by quote | n/a | Mixed teams where analysts and engineers both edit pipelines. |
| Mage | Free OSS; Starter $29/mo + usage | n/a | Small teams and solo data engineers who want managed hosting on a small budget. |
| Flyte | Free OSS; Union from $950/mo | 3.8 16 reviews | ML platform teams that already run Kubernetes and need reproducible training pipelines. |
| Argo Workflows | Free (Apache 2.0) | n/a | Platform teams that run everything on Kubernetes and want highly parallel batch jobs. |
| Temporal | Free OSS; Cloud usage-based | 4.3 18 reviews | Engineering teams orchestrating application workflows that also move data. |
| Windmill | Free; Enterprise from $120/mo | n/a | Ops and data teams that mix scheduled jobs with internal tools and scripts. |
Apache Airflow
The open-source standard for scheduling data pipelines, sold managed by Astronomer, AWS and Google.
- 4.4 on G2 (129 reviews)
- Starting price
- Free; managed from $0.35/hr
- Free plan
- No
- Rating
- 4.4 (129 reviews)
Best for: Most data teams that want a free core and a choice of three managed providers.
- Pro: Free open-source core under Apache 2.0, now on the 3.3 release line.
- Pro: Version 3.3 adds stateful tasks and multi-language support; 3.2 added asset partitioning.
- Pro: Three large managed options: Astronomer, Amazon MWAA and Google Cloud.
- Con: Self-hosting means running a scheduler, metadata database and workers around the clock.
- Con: Managed plans bill always-on hours: a small MWAA environment is $0.49 an hour in US East even when idle.
The pricing for Apache Airflow is incredibly fair and generous, especially considering the robust open-source option.
Watch out
AWS MWAA data transfer and storage fees
- Starting price
- Free OSS; Dagster+ from $10/mo
- Free plan
- No
Best for: Analytics engineering teams built around dbt and a cloud warehouse.
- Pro: Asset checks, freshness policies and asset-level lineage on every Dagster+ plan.
- Pro: Hybrid deployments on ECS, Kubernetes or Docker carry no compute charge.
- Pro: 30-day free trial of Dagster+ before any payment.
- Con: Credits meter every asset materialization and op: 15,000 a month add $525 on Starter.
- Con: Starter caps you at 3 users and one deployment; more needs the quote-only Pro plan.
Dagster's pricing model is fair and generous, especially with its robust Open Source offering and significant free credits in the Solo and Starter tiers.
- Starting price
- Free Hobby; paid from $100/mo
- Free plan
- Yes
- Rating
- 4.5 (125 reviews)
Best for: Python teams that want predictable bills regardless of how many runs they schedule.
- Pro: Hobby is free for 2 users and 5 deployments with 500 serverless minutes a month.
- Pro: No charge per run or task: the bill follows users and workspaces.
- Pro: Team includes 225 hours of Prefect Serverless compute each month.
- Con: Deployment caps of 20 on Starter and 100 on Team force an upgrade as pipelines multiply.
- Con: SSO and role-based access sit on the quote-only Enterprise tier.
Prefect's pricing is quite generous for individual users and small teams with its robust Free tier offering unlimited runs and cloud orchestration.
- Starting price
- Free OSS; Cloud by quote
- Free plan
- Yes
Best for: Mixed teams where analysts and engineers both edit pipelines.
- Pro: Open source edition lists unlimited flows and executions under Apache 2.0.
- Pro: More than 2,400 plugins and business logic in any language.
- Pro: Enterprise runs on cloud, on-premises or air-gapped infrastructure.
- Con: Neither Kestra Cloud nor Enterprise publishes a price; both start with a sales call.
- Con: SSO, RBAC and audit logs are Enterprise features, not in the free edition.
Kestra offers a generous free open-source edition, making it highly accessible for individual developers and small teams.
- Starting price
- Free OSS; Starter $29/mo + usage
- Free plan
- No
Best for: Small teams and solo data engineers who want managed hosting on a small budget.
- Pro: Starter is billed monthly and comes with a 7-day free trial, no card required.
- Pro: Team raises the seat count to 10 developers on the same per-minute meter.
- Pro: Execution minutes are only billed while work runs, on a 1 CPU and 4 GB standard size.
- Con: Every execution minute is metered: 7,500 minutes a month add $90.
- Con: Larger compute sizes consume proportionally more minutes.
Mage AI's pricing is on the higher side, especially for smaller teams or individuals.
Flyte
A Kubernetes-native orchestrator for data and ML pipelines, with typed data, caching and retries.
- 4.7 on G2 (11 reviews)
- 2.5 on SourceForge (5 reviews)
- Starting price
- Free OSS; Union from $950/mo
- Free plan
- Yes
- Rating
- 3.8 (16 reviews)
Best for: ML platform teams that already run Kubernetes and need reproducible training pipelines.
- Pro: Open-source Flyte is free under Apache 2.0, with caching, retries and type-safe data.
- Pro: Union's managed Team plan credits the monthly minimum back as usage.
- Pro: Self-managed clusters on Union carry no cluster fee.
- Con: Self-hosting requires installing and operating Flyte on your own Kubernetes.
- Con: The managed Team plan's monthly minimum is steep for a team running only small batch workloads.
Flyte offers a very generous entry point with its free Flyte 2.0 and Open-source (Flyte 1) tiers, providing robust AI/ML workflow capabilities at no cost.
Argo Workflows
A container-native workflow engine that runs each step as a container on Kubernetes.
- 4.0 on G2 (1 reviews)
- Starting price
- Free (Apache 2.0)
- Free plan
- Free to use
Best for: Platform teams that run everything on Kubernetes and want highly parallel batch jobs.
- Pro: Free and open source, implemented as a Kubernetes custom resource.
- Pro: Supports both step sequences and DAG dependencies.
- Pro: Runs on any Kubernetes cluster, in any cloud.
- Con: No managed plan from the project itself; you run and upgrade it.
- Con: Requires Kubernetes skills on the team before the first pipeline runs.
Argo's pricing is exceptionally generous, as it is entirely free and open-source.
Watch out
Potential need for commercial support add-ons
- Starting price
- Free OSS; Cloud usage-based
- Free plan
- No
- Rating
- 4.3 (18 reviews)
Best for: Engineering teams orchestrating application workflows that also move data.
- Pro: Open-source server under the MIT licence.
- Pro: Temporal Cloud has no base monthly fee and starts with $150 of credits for 90 days.
- Pro: Self-service volume pricing goes down to $25 per million actions.
- Con: Billing in actions is hard to map to pipelines until you measure a real workload.
- Con: Business support costs the greater of $500 a month or 10% of usage.
Temporal Cloud has no permanent free plan.
Watch out
Storage and support are additional to Actions.
- Starting price
- Free; Enterprise from $120/mo
- Free plan
- Yes
Best for: Ops and data teams that mix scheduled jobs with internal tools and scripts.
- Pro: Community Edition is free to self-host with unlimited executions.
- Pro: Runs on Fargate, Docker or Kubernetes.
- Pro: Enterprise prices builders at $20 a month and non-builders at half that.
- Con: The free edition caps SSO at 10 users.
- Con: Self-hosted Enterprise also bills workers, $50 per standard worker a month.
Windmill's pricing is very generous, offering a fully functional self-hosted Free tier.
Other ETL & Data Pipelines tools worth considering
More published tools from our ETL & Data Pipelines category, ordered by our category ranking. They are not part of the editorial picks above.
Task scheduler, asset orchestrator or durable workflow engine?
Every tool here runs code on a schedule or on an event, but they model the work differently, and that model decides how you debug at 3 a.m.
| Model | Tools | What you define | Fits |
|---|---|---|---|
| Task DAG scheduler | Airflow, Prefect, Kestra, Mage | Tasks and their order | Batch ELT, reports, mixed scripts |
| Asset orchestrator | Dagster (Airflow 3.2 added asset partitioning) | The tables and files a pipeline produces | dbt and warehouse teams that care about lineage |
| Kubernetes-native engine | Argo Workflows, Flyte | Containers per step | ML training, heavy parallel jobs |
| Durable execution engine | Temporal | Long-running code that survives crashes | Application workflows, not reporting pipelines |
| Script and flow platform | Windmill | Scripts, flows and internal apps | Ops teams mixing data jobs with tooling |
Self-hosted or managed is the second axis. All nine have a free open-source edition. Self-hosting means you run the scheduler, a metadata database and the workers, and you own upgrades and backups. Managed plans move that work to the vendor and replace it with a meter.
For moving data between systems rather than scheduling it, see our ETL tools guide; orchestrators call those tools, they do not replace them.
What do 50 daily pipelines cost on each managed plan?
Prices checked on vendor pages on October 29, 2026. Scenario: 50 pipelines run once a day, each with 10 assets or tasks and about 5 minutes of compute, over a 30-day month. That is 15,000 task or asset runs, 7,500 compute minutes (125 hours) and 720 always-on hours. Totals are our arithmetic on list prices, before taxes and cloud networking.
| Plan | Our arithmetic for the scenario | Per month |
|---|---|---|
| Mage Starter (1 seat), $0.012 per execution minute | $29 + 7,500 minutes x $0.012 | $119 |
| Astronomer Astro Developer, entry deployment and worker sizes | 720 hours x $0.35 + 125 worker hours x $0.13 | $268.25 |
| Amazon MWAA, small environment (US East) | 720 hours x $0.49 + 10 GB database x $0.10 | $353.80 |
| Prefect Cloud Team, 4 users, 225 serverless hours included | 4 x $100 per user | $400 |
| Dagster+ Starter, Hybrid (your compute) | $100 + 15,000 credits x $0.035 | $625 |
| Dagster+ Starter, Serverless | $625 + 7,500 minutes x $0.010 | $700 |
| Union (managed Flyte) Team | Monthly minimum, credited to usage | $950 |
| Self-hosted Airflow, Dagster, Prefect or Kestra | No licence fee | Servers and upkeep |
What the numbers mean. Per-credit pricing punishes fine-grained pipelines: model each pipeline as 20 assets instead of 10 and the Dagster+ credit line doubles to $1,050, while Prefect's seat price does not move. Always-on pricing (Astronomer, MWAA) charges the same whether you run 5 pipelines or 50, so it gets cheaper per pipeline as you grow.
Self-hosting is only free in licences. Over a year, the managed range above runs from $1,428 (Mage Starter) to $11,400 (Union's Team minimum), our arithmetic. That annual figure is your budget for the virtual machines, the metadata database and the engineering hours a self-hosted scheduler needs. Price your own servers and upkeep time against it before assuming open source is the cheaper route.
Key Features to Look For
Scheduling, triggers and backfills (Essential)
Cron schedules are table stakes. Check event triggers (a file lands, an upstream table updates) and how the tool reruns a date range after a bug fix, because backfills are where weak orchestrators fall over.
Retries, alerts and observability (Essential)
Per-task retries with backoff, alerts to Slack or PagerDuty, and run history long enough to debug last week. Prefect's free Hobby tier keeps run history for 7 days, Team for 14.
Lineage and data assets (Essential)
Asset-aware tools show which table a failed run left stale and what sits downstream. Dagster builds on this model; Airflow 3.2 added asset partitioning.
Deployment model (Essential)
Decide early between fully managed, hybrid (vendor control plane, your compute) and self-hosted. Hybrid keeps data in your cloud and usually removes the compute charge from the vendor bill.
dbt and warehouse integration (Important)
Most analytics pipelines end in dbt models on Snowflake, BigQuery or Databricks. Native dbt support that maps each model to a task or asset saves hand-written glue code.
Access control and audit logs (Important)
SSO, role-based access and audit logs are often gated to upper tiers. Prefect lists SSO on Enterprise; Astronomer lists SSO enforcement from Business; Kestra lists SSO and RBAC in Enterprise Edition.
How to choose for your team
-
Check deployment caps: Prefect Starter allows 20 deployments and Team 100, so 50 separately deployed pipelines need Team or a different packaging.
-
If your data cannot leave your cloud account, shortlist hybrid options (Dagster+ Hybrid, Prefect with your own workers, Union with your clusters) or self-hosting.
-
Ask who will upgrade a self-hosted scheduler and its database, and price those hours against the managed plan.
-
Weigh the Prefect and Dagster combination if you want two options from different companies for negotiation or risk.
Evaluation Checklist
Port one real pipeline, including its dbt step, to each finalist and compare the code you had to write.
Break a task on purpose and time how long it takes to find the failure, fix it and rerun only what failed.
Run a 30-day backfill and check how the tool parallelises it and what the run costs on the managed meter.
Estimate your monthly credits, execution minutes or always-on hours from current run logs, not from the vendor's examples.
Confirm SSO, role-based access and audit log retention on the exact plan you will buy.
Read the export and migration story: can you leave with your pipeline code, run history and schedules?
Pricing Overview
Open source, self-hosted
Teams with a platform engineer who can run the scheduler, metadata database and workers on their own servers or Kubernetes.
$0 licence
Entry managed plan
A single developer: Dagster+ Solo covers one user and one deployment, Mage Starter one developer seat.
From $10/mo plus usage
Team managed plan
Small data teams of 3 to 10 people running production pipelines with shared alerts and access.
Flat fee or per seat, plus usage
Always-on managed Airflow
Teams that standardise on Airflow and want a hosted scheduler billed by the hour, with workers scaling separately.
From $0.35/hr
Enterprise
SSO, SCIM, audit logs, uptime SLAs, private networking and annual commits with volume discounts.
Contact sales
Pricing Comparison
| Tool | Open-source edition | Managed entry price | What grows the bill |
|---|---|---|---|
Free, Apache 2.0 | Astro Developer from $0.35/hr; MWAA small $0.49/hr (US East) | Always-on hours and worker hours | |
Dagster | Free, Apache 2.0 | Solo $10/mo; Starter $100/mo; both plus per-credit usage | Credits (asset materializations and ops) |
Prefect | Free, Apache 2.0 | Hobby free; Starter from $100/mo; Team billed per user | Seats and deployment caps |
Kestra | Free, Apache 2.0 | Cloud and Enterprise: contact sales | Instances (Enterprise is priced per instance) |
Mage | Free, Apache 2.0 | Starter $29/mo + $0.012 per execution minute | Execution minutes |
Flyte (Union) | Free, Apache 2.0 | Team $950/mo minimum, credited to usage | Actions, vCPU, memory and GPU |
Free, Apache 2.0 | No managed plan from the project | Your Kubernetes cluster | |
Temporal | Free, MIT | Cloud from $50 per million actions, $150 starting credit | Actions and stored history |
Windmill | Community Edition free | Self-hosted Enterprise from $120/mo, monthly billing | Builders, users and workers |
Prices read on October 29, 2026 on Dagster pricing, Prefect pricing, Kestra pricing, Astronomer pricing, Amazon MWAA pricing, Mage pricing, Union pricing, Temporal pricing and Windmill pricing. Open-source licences are those declared in each project's GitHub repository. MWAA rates are for US East (N. Virginia); other regions differ.
Mistakes to Avoid
- ×
Comparing base fees only. Dagster+ Starter's base fee looks lower than four Prefect Team seats, yet its metered credits make it the pricier of the two plans in our scenario.
- ×
Treating self-hosted open source as free. The licence is $0, but the scheduler, metadata database, workers and upgrades are your bill.
- ×
Modelling pipelines too finely on a per-credit or per-action plan, which multiplies the meter without adding value.
- ×
Picking a Kubernetes-native engine (Argo, Flyte) without anyone on the team who runs Kubernetes day to day.
- ×
Using a durable execution engine like Temporal for warehouse reporting, where asset lineage and backfills matter more than crash-proof code.
Expert Tips
- →
Start managed, keep the code portable: all nine open-source editions let you move pipelines in-house later if the bill grows.
- →
On Dagster+, check the 30-day free trial against your real asset count before choosing between Solo and Starter.
- →
On Prefect, package related flows into fewer deployments if you want to stay within the Starter plan's 20-deployment cap.
- →
For Airflow, compare Astronomer's scale-to-zero workers with MWAA's included worker on your actual task pattern, using the Apache Airflow tool page for alternatives.
- →
Pair the orchestrator with a connector tool from our open-source ETL guide rather than writing extraction code in tasks.
Red Flags to Watch For
-
A managed plan whose usage meter you cannot estimate from your current run logs.
-
Run history retention shorter than your incident review cycle (7 days on some free tiers).
-
SSO and audit logs available only on a quote-only tier when your security review requires them.
-
No documented export path for pipeline code, schedules and run metadata.
The Bottom Line
Apache Airflow remains the default choice for most data teams, because it is free to self-host, and sold managed by Astronomer, AWS and Google, so you can switch hosts without rewriting pipelines. Dagster is the better fit when your pipelines are really about dbt models and warehouse tables (Airflow vs Dagster), and Prefect is the better fit when your team writes plain Python and wants a bill that tracks seats rather than runs (Dagster vs Prefect).
If you are moving off a self-hosted Airflow install, our Airflow alternatives page lists more options beyond these nine.
Kestra, Mage and Windmill are worth a trial for smaller teams that want YAML, a notebook-style editor or scripts plus internal apps. Flyte and Argo belong with teams that already run Kubernetes, and Temporal belongs in application code more than in reporting pipelines. For a broader look at automation tools outside data engineering, see our workflow automation guide.
Frequently Asked Questions
What is the best data orchestration tool in 2026?
Apache Airflow is the safest default: free under Apache 2.0, managed by Astronomer, Amazon MWAA and Google Cloud, and on version 3.3 with stateful tasks and multi-language support. Dagster leads for asset-centric dbt teams and Prefect for Python teams that prefer seat-based pricing.
How much does managed orchestration cost for 50 daily pipelines?
In our scenario (10 tasks and 5 compute minutes per pipeline per day), list prices give about $119 a month on Mage Starter, $268.25 on Astronomer's entry Developer sizes, $353.80 on a small MWAA environment, $400 on Prefect Team with four users and $625 on Dagster+ Starter Hybrid. These are our arithmetic, not vendor quotes.
Is Dagster still independent after the Prefect deal?
No. Prefect announced on July 13, 2026 that it is acquiring Dagster Labs. Both companies say Dagster keeps its name, its open-source licence and its hosted product, and that existing deployments, contracts and pricing are unchanged.
Is self-hosted Airflow cheaper than a managed service?
The licence is free, but you pay for the scheduler, metadata database and workers, plus upgrades. A small MWAA environment in US East runs about $352.80 a month in environment hours alone (720 x $0.49), which is the budget a self-hosted setup has to beat once engineering time is counted.
Which orchestrators have a free managed tier?
Prefect Cloud's Hobby tier is free for 2 users and up to 5 deployments, with 500 minutes of serverless compute. Dagster+ offers a 30-day trial, Mage a 7-day trial and Temporal Cloud $150 of credits for 90 days.
Do I need Kubernetes for these tools?
Only for some. Argo Workflows is implemented as a Kubernetes custom resource and open-source Flyte installs on your own Kubernetes. Dagster's hybrid agents also run on ECS or Docker, Windmill self-hosts on Fargate or Docker, and the managed plans remove the cluster question entirely.
Is Temporal a data orchestration tool?
It is a durable execution engine: it keeps long-running application code alive through crashes and retries. Data teams use it, but it has no asset lineage or warehouse backfill model, so most analytics pipelines fit Airflow, Dagster or Prefect better.
Cite this page: Toolradar, "Best Data Orchestration Tools in 2026", updated October 2026, https://toolradar.com/guides/best-data-orchestration-tools
Spotted an outdated price or a wrong fact? Suggest a correction: we check it against the vendor's own page. Work for one of these vendors? Claim your listing to update it yourself.
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