Best software · Developer & data
10 Best Workflow Orchestration Tools of 2026
This list is for data and platform engineers who schedule, run and monitor pipelines and jobs: ELT and dbt runs, machine learning training, batch jobs on Kubernetes and long-running business processes. It covers managed data orchestrators (Dagster, Prefect, Astronomer, Mage), open-source engines you host yourself (Apache Airflow, Argo Workflows, Kestra, Windmill), a durable execution platform (Temporal) and a serverless cloud service (AWS Step Functions). We compared documented features and published prices for 10 tools. Scores weigh features at 40%, ease of use at 30% and value at 30%.
By the Mojolin research desk · Updated November 12, 2026 · 10 tools compared · Prices checked on vendor pages, latest November 12, 2026
Questions or corrections: editorial@mojolin.com
Mojolin is self-funded How we score
Dagster is our top pick for data teams: it models pipelines as assets with lineage, asset checks and freshness policies, and its Solo plan starts at $10 a month plus $0.040 per credit with a 30-day free trial. Prefect is the closest alternative for Python teams, with a free Hobby tier and a $100 a month Starter plan; Prefect announced in July 2026 that it is acquiring Dagster Labs, and Dagster says it continues under its own brand. Apache Airflow remains the free default with the largest operator ecosystem, and Astronomer is the managed way to run it, with deployments from $0.35 an hour. Kestra and Windmill are strong open-source options for YAML and script-first teams, Argo Workflows fits Kubernetes platform teams, and Temporal and AWS Step Functions fit application workflows more than data pipelines.
Top picks
Best overall
Dagster
8.9 / 10
Data engineers building warehouse, dbt and ML pipelines who want lineage and data quality in the orchestrator
Read the review →Runner-up
Prefect
8.8 / 10
Python teams that want a free managed tier and simple flat monthly plans
Read the review →Also great
Apache Airflow
8.7 / 10
Teams that want the standard open-source scheduler and can run it themselves
Read the review →Comparison table
| # | Tool | Pricing | Score | |
|---|---|---|---|---|
| 1 | Dagster | Data engineers building warehouse, dbt and ML pipelines who want lineage and data quality in the orchestrator | Solo from $10 per month plus $0.040 per credit; Starter from $100 per month plus $0.035 per credit; Pro is custom; 30-day free trial | 8.9 |
| 2 | Prefect | Python teams that want a free managed tier and simple flat monthly plans | Free Hobby tier for 2 users and 5 deployments; Starter $100 per month; Team $100 per user per month; Enterprise is custom | 8.8 |
| 3 | Apache Airflow | Teams that want the standard open-source scheduler and can run it themselves | Free and open source; you pay only for the infrastructure you run it on, or for a managed service | 8.7 |
| 4 |  Astronomer | Teams standardized on Airflow that want it managed with support and observability | Usage-based: Developer deployments from $0.35 per hour, Team from $0.42 per hour, workers from $0.13 per hour; Business and Enterprise by quote | 8.6 |
| 5 |  Kestra | Platform and data teams that prefer declarative YAML workflows across languages | Open Source edition is free with unlimited flows and executions; Enterprise Edition is an annual per-instance subscription by quote | 8.5 |
| 6 |  Windmill | Engineering teams that want scripts, workflows and internal apps in one self-hosted tool | Free open-source Community Edition with unlimited executions; self-hosted Enterprise from $120 per month, with builders at $20 per month and workers at $50 per month | 8.5 |
| 7 |  Temporal | Backend and platform engineers building long-running application workflows | Temporal Cloud pay as you go from $50 per million actions, down to $25 with volume; $150 in free credits for 90 days; Business support from $500 per month | 8.4 |
| 8 |  Argo Workflows | Platform teams running batch, ML and CI jobs on Kubernetes | Free and open source; you pay only for the Kubernetes cluster it runs on | 8.3 |
| 9 |  AWS Step Functions | Teams on AWS that want serverless orchestration with no servers to run | Standard Workflows $0.000025 per state transition in US East (N. Virginia) after 4,000 free transitions a month; Express Workflows $1.00 per million requests plus duration | 8.2 |
| 10 |  Mage | Small data teams that want pipeline building, orchestration and monitoring in one managed tool | Starter $29 per month plus $0.0167 per execution minute; Team $100 per month plus execution minutes; Enterprise custom; 7-day free trial | 8.2 |
Scoring weights
Every tool gets three editorial scores out of 10, from vendor documentation, product pages and the published price. The overall score is the weighted average below, the same method on every list. Full method
The full ranking
Rank 1 · Best overall
Dagster
Asset-based data orchestrator with a built-in catalog, data quality checks and a managed Dagster+ service.
dagster.io8.9
out of 10
Best for: Data engineers building warehouse, dbt and ML pipelines who want lineage and data quality in the orchestrator
Dagster treats a pipeline as the set of data assets it produces, such as tables, files and models, rather than as a list of tasks. That design gives you asset-level lineage, asset checks, freshness policies and dbt test integration on every plan, which is why it leads this list for data teams that care about what their pipelines produce. The dg CLI and Components help with local development, and branch deployments let you test changes before they reach production.
Pricing on Dagster+ is pay as you go. Solo costs $10 a month plus $0.040 per credit for 1 user, 1 code location and 1 deployment; Starter costs $100 a month plus $0.035 per credit for up to 3 users and 5 code locations. Serverless compute is $0.010 per minute, and Hybrid, where work runs on your own infrastructure, has no compute charge. Pro adds unlimited deployments, cost tracking for BigQuery and Snowflake, and uptime SLAs. In July 2026 Dagster announced that Prefect is acquiring Dagster Labs, and said Dagster continues under its own brand with continued open-source investment.
Standout feature
Dagster is the best choice for data engineers who want quality checks in the scheduler because asset checks, freshness policies and asset lineage are included on every plan.
Use cases
Analytics engineers
Orchestrate dbt models and ingestion as one asset graph
Outcome: Each table shows its upstream sources, freshness and check results
Solo data engineers
Run a side project or small pipeline on the Solo plan
Outcome: A managed orchestrator from $10 a month plus credits
Platform teams
Run Dagster+ in Hybrid mode on their own Kubernetes cluster
Outcome: Code and data stay in their network with no Dagster compute charge
Key features
- Asset-based and workflow-based orchestration with partitions and backfills
- Asset checks, freshness policies and integration with dbt tests
- Asset-level lineage and asset metadata in the UI on every plan
- Branch deployments for testing pipeline changes
- Serverless compute at $0.010 per minute, or Hybrid with no compute charge
Pros
- Solo plan starts at $10 a month with a 30-day free trial
- Data quality and lineage are built in rather than sold as add-ons
- Hybrid deployments carry no compute charge from Dagster
Cons
- Credit-based billing needs an estimate of your run volume
- Column lineage, catalog search and cost tracking are not on Solo
- Starter caps you at 3 users and 1 deployment
Rank 2 · Runner-up
Prefect
Python workflow orchestration with a managed Prefect Cloud, serverless compute and bring-your-own-compute work pools.
prefect.io8.8
out of 10
Best for: Python teams that want a free managed tier and simple flat monthly plans
Prefect turns Python functions into scheduled, observable workflows and runs them on Prefect Serverless or on compute you control through work pools. It suits teams who write pipelines, ML jobs and automation scripts in plain Python and want a managed control plane without operating a scheduler and metadata database.
The pricing is easy to read. Hobby is free forever with 2 users, 5 deployments, 500 minutes of serverless compute and 7 days of run retention. Starter costs $100 a month for 3 users, 20 deployments, 75 hours of serverless compute and bring-your-own compute. Team costs $100 per user per month for 4 to 8 users, 100 deployments, 225 serverless hours, service accounts and a 24 hour audit log. Enterprise adds SSO, SCIM, object-level RBAC, IP allowlisting, PrivateLink and a 99.9% uptime SLA. The weak spots are short run retention on lower plans and governance locked to Enterprise. Prefect announced in July 2026 that it is acquiring Dagster Labs, so the two products now share an owner.
Standout feature
Prefect is the best choice for teams that want a free managed orchestrator because its Hobby tier is free forever with 500 minutes of Prefect Serverless.
Use cases
ML engineers
Schedule training and evaluation scripts written in Python
Outcome: Retries, logs and alerts without running their own scheduler
Small data teams
Move from cron jobs to Prefect Cloud on the Hobby tier
Outcome: Up to 5 deployments monitored at no cost
Platform teams
Run flows on their own Kubernetes or cloud compute via work pools
Outcome: Prefect manages orchestration while compute stays in their account
Key features
- Hobby tier free forever with 2 users, 5 deployments and 500 minutes of Prefect Serverless
- Bring your own compute through custom work pools from Starter
- Automations that react to workflow events, from 5 on Hobby to 500+ on Enterprise
- Webhooks and service accounts for CI/CD on paid plans
- SOC 2 Type II, HIPAA and GDPR, with hybrid and VPC options
Pros
- Free Hobby tier includes managed serverless minutes
- Starter is a flat $100 a month for 3 users and 20 deployments
- Eligible startups get Team plan credits through the Prefect Startup Program
Cons
- Run retention is 7 days on Hobby and Starter
- SSO, SCIM and object-level RBAC need Enterprise
- Team is priced per user, so costs climb past 4 users
Rank 3 · Also great
Apache Airflow
Open-source Apache Software Foundation platform to author, schedule and monitor workflows defined in Python.
airflow.apache.org8.7
out of 10
Best for: Teams that want the standard open-source scheduler and can run it themselves
Apache Airflow is the open-source scheduler most data teams already know. You define pipelines in Python, which allows dynamic pipeline generation, and you monitor runs, schedules and task logs in its web UI. A modular architecture with a message queue lets it orchestrate any number of workers, and its plug-and-play operators cover Google Cloud, AWS, Azure and many third-party services.
The project keeps moving. The Airflow site announced version 3.4.0 on November 6, 2026 with task loops, Dags in any language and AI agents, and the Common AI provider for LLMs and agents reached 1.0 in October 2026. Airflow itself costs nothing, which is why it scores highest on value here. The cost is operational: you run the scheduler, webserver, metadata database and workers, handle upgrades and debug them without a vendor. Teams that want Airflow without that work usually buy a managed service such as Astronomer.
Standout feature
Apache Airflow is the best choice for teams on a zero license budget because the full scheduler is free and open source under the Apache Software Foundation.
Use cases
Data engineering teams
Schedule nightly ingestion and transformation Dags
Outcome: A free, Python-defined scheduler with a UI for run history and logs
Platform teams
Offer Airflow as a shared internal service on Kubernetes
Outcome: Many teams run pipelines on one scheduler with no license fees
ML teams
Add LLM and agent steps with the Common AI provider
Outcome: AI tasks run inside existing Airflow pipelines
Key features
- Pipelines defined in Python, with dynamic pipeline generation
- Web UI to monitor, schedule and manage workflows and read task logs
- Plug-and-play operators for Google Cloud, AWS, Azure and other services
- Modular architecture with a message queue and any number of workers
- Apache Airflow 3.4.0 adds task loops, Dags in any language and AI agents
Pros
- No license cost
- Large operator ecosystem and community, run by the Apache Software Foundation
- Offered as a managed service by several vendors, including Astronomer
Cons
- Self-hosting means your team owns upgrades, scaling and the metadata database
- No vendor support unless you buy a managed service
- Task-centric model gives less built-in data lineage than asset-based tools
Rank 4
Astronomer
Astro, a fully managed Apache Airflow platform with usage-based pricing, observability and a private cloud option.
astronomer.io8.6
out of 10
Best for: Teams standardized on Airflow that want it managed with support and observability
Astronomer's Astro runs Apache Airflow as a managed service, so teams keep their existing Dags and drop the work of hosting the scheduler and workers. Each deployment includes the scheduler, webserver and triggerer, and workers scale to zero when idle, which Astronomer contrasts with other managed Airflow providers.
Pricing is usage-based. Developer deployments start at $0.35 an hour and Team deployments at $0.42 an hour, with workers billed separately from $0.13 an hour across 7 sizes. Standard multi-tenant clusters are included; dedicated single-tenant clusters start at $2.40 an hour. Team adds end-to-end observability, network isolation, high availability and 24x5 support; Business adds SSO enforcement and 24x7 support with a 1 hour SLA; Enterprise adds custom RBAC, SCIM and remote execution agents. Astro Observe is priced as a percentage of compute, and the Otto agent starts at $1.25 per million tokens. The tradeoff is cost predictability, since several meters add up, but for Airflow shops it removes most of the operational load.
Standout feature
Astronomer is the best choice for Airflow teams that want managed hosting because its workers scale to zero and deployments start at $0.35 an hour.
Use cases
Airflow teams
Move self-hosted Airflow to a managed service
Outcome: Same Dags with no scheduler or worker operations
Regulated enterprises
Run Astro Private Cloud in their own environment
Outcome: Airflow as a service with air-gapped support
Data platform leads
Use Astro Observe to trace failures and costs
Outcome: Root cause analysis and lineage across Airflow pipelines
Key features
- Managed Airflow deployments with scheduler, webserver and triggerer included
- Workers that scale to zero when idle, in 7 sizes from A5 to A160
- Hibernating deployments and deployment rollbacks on Developer
- Astro Observe for cost and performance metrics, root cause analysis and lineage
- Otto, an AI agent for Airflow, from $1.25 per million tokens
Pros
- Standard multi-tenant clusters are included at no cost on all plans
- Sold through AWS, Azure and Google Cloud marketplaces
- Astro Private Cloud supports air-gapped deployments
Cons
- Several meters (deployments, workers, clusters, Observe, AI tokens) make bills harder to forecast
- Dedicated clusters start at $2.40 an hour
- 24x7 support and SSO enforcement start on the Business plan
Rank 5
Kestra
Open-source, declarative and event-driven orchestration platform with YAML flows and 2,600+ plugins.
kestra.io8.5
out of 10
Best for: Platform and data teams that prefer declarative YAML workflows across languages
Kestra defines workflows in declarative YAML rather than in Python, which lets data engineers, software engineers and platform engineers share one orchestrator regardless of the language each task runs. A code editor and a guided form editor stay in sync, so less technical users can edit flows too. Event-driven triggers sit alongside schedules, and the platform runs its queue and repository on PostgreSQL or MySQL with nothing else to operate.
The Open Source edition is generous: unlimited flows and executions, most of the 2,600+ plugins, draft revisions, AI agents, an AI Copilot on Gemini and an MCP server that exposes flows as tools for AI agents. Enterprise Edition is an annual per-instance subscription with no published price and adds SSO, LDAP, SCIM, RBAC, audit logs, multi-tenancy, worker groups and task runners on Kubernetes, cloud batch services and VMs. Upgrading is a matter of adding a license key. The missing piece is public pricing, so Enterprise budgets need a sales call.
Standout feature
Kestra is the best choice for declarative, multi-language orchestration because its free Open Source edition has unlimited flows and executions and 2,600+ plugins.
Use cases
Platform engineers
Orchestrate infrastructure automation and data jobs in one tool
Outcome: Declarative YAML flows across shell, Python, SQL and cloud plugins
Data teams
Trigger pipelines from file arrivals and events instead of fixed schedules
Outcome: Event-driven runs with no extra queue to operate
Enterprises
Run Kestra air-gapped with RBAC and audit logs
Outcome: Governed orchestration on an annual per-instance license
Key features
- 2,600+ plugins, most available in both editions
- Code and no-code editors for YAML flows that stay in sync
- Event-driven scheduling and triggers
- AI agents, an AI Copilot and an MCP server in the Open Source edition
- Runs on PostgreSQL or MySQL with nothing else to operate
Pros
- Open Source edition has no limit on flows or executions
- Upgrading to Enterprise only needs a license key and a restart
- Runs in the cloud, on premises or air-gapped
Cons
- No published Enterprise price
- RBAC, SCIM, audit logs and task runners on Kubernetes or VMs need Enterprise
- Cloud Edition is request-access only
Rank 6
Windmill
Open-source platform that turns scripts into flows, schedules, triggers and internal apps, self-hosted or in the cloud.
windmill.dev8.5
out of 10
Best for: Engineering teams that want scripts, workflows and internal apps in one self-hosted tool
Windmill turns scripts into scheduled jobs, multi-step flows and internal apps, which makes it a fit for engineering teams that want orchestration and internal tooling in one place. The open-source edition allows unlimited executions, up to 50 users and 3 workspaces, Postgres and WebSocket triggers, deploy from GitHub and Git sync for up to 2 users, and runs on Fargate, Docker or Kubernetes.
The pricing page is unusually transparent. Self-hosted Enterprise starts from $120 a month, priced from builder seats at $20 a month, non-builders at $10 a month and standard workers at $50 a month, and a calculator shows the total for your worker setup. Enterprise adds audit logs, SAML and SCIM, Kafka, NATS and SQS triggers, OpenTelemetry tracing, Prometheus metrics, schedule error handlers and 24/7 priority support with a 3 hour response time. Non-profits and universities get 40% off. The free edition is limited on observability and event sources, so production data teams will likely need Enterprise.
Standout feature
Windmill is the best choice for self-hosting teams on a budget because its open-source edition has unlimited executions and Enterprise starts from $120 a month.
Use cases
Platform engineers
Replace a set of cron scripts with scheduled Windmill flows
Outcome: One UI for schedules, run history and retries on self-hosted infrastructure
Data engineers
Run Python and SQL pipelines with Postgres triggers
Outcome: Event-driven jobs without a separate scheduler
Operations teams
Expose a workflow as an internal app for colleagues
Outcome: Non-engineers run approved jobs from a form
Key features
- Unlimited executions on the free open-source edition
- Deployment on Fargate, Docker or Kubernetes
- Postgres, WebSocket and email triggers, plus Kafka, NATS and SQS on Enterprise
- Git sync and deploy from GitHub
- Public apps and up to 100 free guest app users per 30 days
Pros
- Free edition covers up to 50 users, 3 workspaces and 10 users with SSO
- Transparent seat and worker pricing with a public cost calculator
- Non-profits and universities get 40% off Enterprise
Cons
- Audit logs, OpenTelemetry tracing and Prometheus metrics need Enterprise
- Free edition keeps job run details for 30 days or less
- Kafka, SQS and cloud event triggers are not in the free edition
Rank 7
Temporal
Durable execution platform for long-running, failure-proof workflows, available as Temporal Cloud or self-hosted open source.
temporal.io8.4
out of 10
Best for: Backend and platform engineers building long-running application workflows
Temporal is a durable execution engine: workflows written in ordinary code capture their state at every step and resume exactly where they left off after a crash or deploy, with no checkpointing code of your own. It suits long-running processes such as order fulfillment, provisioning and AI agents rather than nightly table builds. Built-in failure handling covers retries with backoff, four timeout classes, heartbeating and saga compensation, and durable timers can sleep for months.
Temporal Cloud has no base fee and no plan tiers for features. Pay as you go starts at $50 per million actions, falling to $25 per million with volume, plus $0.042 per GB-hour of active storage and $0.00105 per GB-hour retained. New accounts get $150 in credits for 90 days. Business support starts at $500 a month or 10% of usage. SDKs cover Go, Java, Python, TypeScript.NET, PHP, Ruby and Rust. For data teams, Temporal lacks asset lineage and data quality features, which is why it ranks below the data orchestrators here.
Standout feature
Temporal is the best choice for long-running application workflows because its durable execution keeps workflow state through crashes and deploys.
Use cases
Backend engineers
Run order and payment workflows that span many services
Outcome: Workflows resume after crashes with no manual recovery
Platform teams
Automate infrastructure provisioning with long waits and approvals
Outcome: Durable timers and signals handle multi-day processes
AI teams
Run OpenAI Agents SDK or Google ADK agents as durable Activities
Outcome: LLM and tool calls are retried and recorded
Key features
- Durable execution: workflow state survives crashes and deploys
- Automatic retries with backoff, four timeout classes and saga compensation
- Durable timers, cron schedules with backfill and Continue-As-New
- SDKs for Go, Java, Python, TypeScript.NET, PHP, Ruby and Rust
- Worker Versioning and replay tests for safe deploys
Pros
- No base monthly fee and no features locked behind plans
- $150 in free credits for 90 days
- 99.9% SLA, or 99.99% with high availability
Cons
- Built for application code, not for data pipeline scheduling with lineage
- Action-based billing takes work to estimate
- SCIM is an add-on
Rank 8
Argo Workflows
Open-source, Kubernetes-native workflow engine where every step runs as a container.
argoproj.github.io8.3
out of 10
Best for: Platform teams running batch, ML and CI jobs on Kubernetes
Argo Workflows is a container-native workflow engine that runs on Kubernetes as a custom resource definition. Each step is a container, and you model work as a sequence of steps or as a directed acyclic graph. That design makes it a strong fit for platform teams that already run Kubernetes and need highly parallel batch processing, ML pipelines, infrastructure automation or CI/CD without another server to host.
The documentation lists a UI to visualize and manage workflows, artifact support for S3, Artifactory, Azure Blob Storage, Git, GCS and more, workflow templates stored in the cluster, workflow archiving and cron scheduling. Python users can write workflows with the Hera SDK instead of YAML. Argo is a Cloud Native Computing Foundation graduated project and costs nothing beyond your cluster. The tradeoff is ease of use: without Kubernetes experience, installation, RBAC and debugging take real effort, and there is no hosted service from the project.
Standout feature
Argo Workflows is the best choice for Kubernetes-native batch jobs because every step runs as a container and the engine is a free CNCF graduated project.
Use cases
Platform engineers
Run thousands of parallel batch containers on an existing cluster
Outcome: Fan-out jobs scheduled by Kubernetes with no extra server
ML engineers
Chain data prep, training and evaluation containers as a DAG
Outcome: Reproducible pipelines with artifacts stored in S3 or GCS
DevOps teams
Run CI/CD pipelines natively on Kubernetes
Outcome: Builds and tests run as workflow steps inside the cluster
Key features
- Implemented as a Kubernetes custom resource definition
- Steps as containers, modeled as sequences or DAGs
- Scheduled workflows using cron, with cron backfill patterns
- Artifact support for S3, GCS, Azure Blob Storage, Git and more
- UI to visualize and manage workflows, plus the Hera Python SDK
Pros
- No license cost and runs on any Kubernetes cluster
- Cloud Native Computing Foundation graduated project
- Handles highly parallel jobs natively on Kubernetes
Cons
- Requires Kubernetes skills to install and operate
- Workflows are defined as Kubernetes YAML unless you add the Hera SDK
- No vendor-hosted service or support from the project itself
Rank 9
AWS Step Functions
Serverless AWS service that coordinates AWS services and microservices as state machines.
aws.amazon.com8.2
out of 10
Best for: Teams on AWS that want serverless orchestration with no servers to run
AWS Step Functions orchestrates AWS services and microservices as state machines, with states that pass data, handle exceptions, add timeouts, make decisions and run paths in parallel. It is serverless, so there is no scheduler or worker fleet to run, which makes it attractive for teams already deep in AWS that need to chain Lambda, Glue, Batch or ECS jobs.
There are two workflow types. Standard Workflows bill per state transition, including retries, at $0.000025 per transition in US East (N. Virginia), and the free tier of 4,000 state transitions a month does not expire. Express Workflows suit high-volume event processing and bill $1.00 per million requests plus duration. AWS's own example shows about 1 million state transitions costing $24.90 after the free tier. The limits for data teams are lock-in to AWS and the lack of pipeline-specific features such as asset lineage, data quality checks and partition backfills.
Standout feature
AWS Step Functions is the best choice for AWS-native teams because it needs no servers and its 4,000 free state transitions a month never expire.
Use cases
Serverless teams
Chain Lambda functions with retries and error handling
Outcome: A managed state machine with no infrastructure to run
Data engineers on AWS
Coordinate Glue and Batch jobs in a nightly pipeline
Outcome: Pay per state transition with nothing to host
Event-driven apps
Process high volumes of short events with Express Workflows
Outcome: Billing by requests and duration at $1.00 per million requests
Key features
- Standard Workflows billed per state transition, including retries
- Express Workflows billed by requests and duration for high-volume events
- Free tier of 4,000 state transitions a month that does not expire
- States that branch, run paths in parallel, handle errors and add timeouts
- Charges metered daily and billed monthly
Pros
- No servers or schedulers to manage
- Permanent free tier of 4,000 state transitions a month
- Native integration with other AWS services
Cons
- Tied to AWS
- Retries count as billable state transitions
- Not built for data asset lineage or pipeline backfills
Rank 10
Mage
Managed data pipeline platform with a visual and code editor, data integrations, orchestration and AI-assisted operations.
mage.ai8.2
out of 10
Best for: Small data teams that want pipeline building, orchestration and monitoring in one managed tool
Mage combines pipeline building, data integration and orchestration in one managed product. You build pipelines from Python, SQL, R and dbt blocks in a visual editor or a code editor, test blocks interactively, and schedule them with triggers, sensors and backfills. Data validations, freshness and schema checks, and alerts by email, Slack or webhook are part of the feature list.
Pricing starts low. Starter costs $29 a month plus $0.0167 per execution minute for 1 developer seat, 1 development environment and 50 AI credits a month, and Mage positions it for solo builders moving from open source to managed Mage. Team costs $100 a month plus execution minutes for up to 10 developer seats and 250 shared AI credits, with lineage and deployment workflows. Enterprise adds private and hybrid deployment, custom governance and SLAs. A 7-day free trial needs no credit card. Mage has a smaller ecosystem than Airflow or Dagster, and execution minutes add a variable cost on top of the plan.
Standout feature
Mage is the best choice for small teams on a budget because Starter costs $29 a month plus $0.0167 per execution minute with a 7-day free trial.
Use cases
Solo data engineers
Move an open-source Mage project to managed hosting
Outcome: Managed orchestration and alerts from $29 a month
Analytics teams
Build SQL and dbt pipelines in a visual editor
Outcome: Scheduled pipelines with validations and alerts
Growing data teams
Share one Team workspace across up to 10 developers
Outcome: Lineage and deployment workflows from $100 a month
Key features
- Visual pipeline editor plus code editor with Python, SQL, R and dbt blocks
- Schedule, webhook and API triggers, sensors and backfills
- Data validations, block and pipeline tests, and validation alerts
- Email, Slack and webhook alerts with run metrics
- AI credits for creating, editing and debugging pipelines
Pros
- Starter is $29 a month for 1 developer seat
- 7-day free trial with no credit card required
- Team covers up to 10 developer seats from $100 a month
Cons
- Execution minutes are billed on top of the plan fee
- Starter includes only 1 development environment
- Private, hybrid and on-prem deployment need Enterprise
Frequently asked questions
+What is the best workflow orchestration tool in 2026?
Dagster is our top pick for data pipelines, because asset checks, freshness policies and asset lineage are included on every plan. Prefect is the closest alternative for Python teams, with a free Hobby tier and a $100 a month Starter plan.
+What is the best free workflow orchestration tool?
Apache Airflow is the best free option for most data teams: it is open source with operators for Google Cloud, AWS and Azure. Argo Workflows is free for Kubernetes teams, and Kestra's Open Source edition and Windmill's Community Edition both allow unlimited executions.
+Is Airflow better than Dagster or Prefect?
Not for every team. Airflow is free and has the largest operator ecosystem, while Dagster adds asset lineage and data quality checks and Prefect offers a free managed Hobby tier. Teams that want Airflow without hosting it can use Astronomer, which starts at $0.35 per deployment hour.
+Did Prefect buy Dagster?
Yes, Prefect announced on July 13, 2026 that it is acquiring Dagster Labs. Dagster's announcement says Dagster continues under the Dagster brand and that investment in Dagster OSS continues.
+How much does managed Airflow cost?
Astronomer's Astro starts at $0.35 per hour for a Developer deployment, plus workers from $0.13 per hour that scale to zero when idle. Team deployments start at $0.42 per hour, and dedicated clusters start at $2.40 per hour.
+What is the difference between Temporal and Airflow?
Temporal runs durable application workflows, while Airflow schedules data pipelines. Temporal keeps workflow state through crashes and supports timers that sleep for months, and Temporal Cloud bills from $50 per million actions; Airflow is a free Python scheduler for batch Dags.
+Which orchestration tool is best for Kubernetes?
Argo Workflows is the best fit for Kubernetes-native teams. It runs as a Kubernetes custom resource, executes each step as a container and is a Cloud Native Computing Foundation graduated project.
About this list
Updated November 12, 2026 by the Mojolin research desk. Mojolin is self-funded
Questions or corrections: editorial@mojolin.com
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