Prefect
by Prefect
Orchestrate workflows and build AI applications with open-source foundations and production-ready platforms Prefect is a Python-first workflow orchestration platform used to schedule, observe, and recover data pipelines and operational jobs. Founded in 2018 by Jeremiah Lowin, a former Apache Airflow committer, the company is based in Washington DC and has raised roughly $50M across Series A and B rounds (third-party trackers differ on the exact total) and announced its acquisition of Dagster Labs on 13 July 2026.
Performance Scores
6 rankings evaluated
Score range: 7.5 – 8.2
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#2Best Durable Workflow Engines for Production in 2026
Score: 8.2 · Best for: Data engineering and ML teams running Python pipelines that need event triggers and durable retries
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#3Best Open-Source Workflow Engines for Engineers in 2026
Score: 8.0 · Best for: Python-centric data and ML teams that need dynamic workflow shapes and a lower learning curve than Airflow
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#4Best Process Orchestration Platforms 2026
Score: 7.6 · Best for: Python data teams wanting dynamic workflows with rollback hooks
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#4Best Automation Tools for Data Teams in 2026
Score: 7.5 · Best for: Python-native workflows with hybrid cloud execution
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#5Best ETL & Data Pipeline Tools 2026
Score: 7.5 · Best for: Data engineering teams using Python that want a modern alternative to Airflow with less configuration overhead
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#9Best Open Source Automation Platforms 2026
Score: 7.5 · Best for: Python teams wanting a modern alternative to Airflow with better developer ergonomics and hybrid cloud execution
Key Facts
pricing
| Attribute | Value | As of | Source |
|---|---|---|---|
| Prefect pricing (July 2026) | Prefect Cloud pricing (verified July 2026): Hobby (Free, 2 users, 500 serverless min/mo), Starter $100/month flat (up to 3 users, 75 serverless hrs), Team $100/user/month with a 4-user minimum ($400/month floor), and Pro + Enterprise (custom). Billing is seat-based with included serverless-compute hours (overage $0.005/min) and explicitly no per-task or per-run charges. Self-hosted Prefect Server remains free and open source. | Jul 2026 | Prefect pricing |
| Pro plan (May 2026) | Pro is quote-based as of July 2026; the $100/month flat published plan is Starter (up to 3 users), with Team at $100/user/month (4-user minimum) | Jul 2026 | Prefect pricing page |
General
| Attribute | Value | As of | Source |
|---|---|---|---|
| Current version | Prefect 3.7.8 (released 9 July 2026); Prefect 3.0 GA September 2024 | Jul 2026 | PyPI |
| Dagster acquisition | Announced acquisition of Dagster Labs on 13 July 2026 (terms undisclosed); both products continue, combined company operates under the Prefect name from August 2026 | Jul 2026 | Prefect |
| Ease of Use | Workflows are written as standard Python using @flow and @task decorators; Prefect builds the task dependency graph automatically and can resume failed runs from the point of failure. | Jul 2026 | Prefect Docs — Quickstart |
| Intuitive API | Prefect 3.x (v3.7.8 as of July 2026, per PyPI) builds, tests, and runs dataflows from plain Python via @flow/@task decorators; the official integrations catalog lists 20 integrations, 17 of them maintained by Prefect, including Databricks and Snowflake. | Jul 2026 | Prefect Docs — Integrations |
| Cost-Effectiveness | Prefect Cloud has a free Hobby tier (2 users, 5 deployments, 500 serverless minutes/month); a vendor case study reports Endpoint cut orchestration invoice costs 73.78% vs Astronomer. | Jul 2026 | Prefect Pricing |
| Trusted in Production | Used in production by Cash App (fraud-model ML workflows) and Progressive Insurance; Prefect states its platform automates 200+ million data tasks monthly (vendor claim, July 2026). | Jul 2026 | Prefect Blog — Cash App case study |
| Open Source Foundation | Built on open-source Python frameworks with Apache 2.0 licensing, allowing developers to experiment and scale from scripts to production. | Apr 2026 | Research |
| Enterprise Features | Prefect Cloud offers SSO (SAML 2.0/OIDC), object-level RBAC, SCIM directory sync, audit logs, and IP allowlisting, and is SOC 2 Type II audited; HIPAA-ready for enterprise plans. | Jul 2026 | Prefect Security |
| GitHub Stars | Over 23,000 GitHub stars on the PrefectHQ/prefect repository (23,466 as of 23 July 2026) | Jul 2026 | GitHub API |
| Cloud Pricing | Prefect Cloud Hobby tier is free for up to 2 users, 5 deployments, and 500 minutes of Prefect Serverless; Starter is $100/month flat for 3 users; Team is $100 per user/month (4-8 users); Pro and Enterprise are custom-quoted (as of July 2026) | Jul 2026 | Prefect pricing page |
| Python-First Approach | Python-native design using decorators (@flow, @task) to convert existing Python functions into observable, schedulable pipeline steps with minimal code changes | Jul 2026 | Prefect documentation (Quickstart) |
| Orchestration Focus | Designed specifically for data workflow orchestration: scheduling, retries, caching, parameterization, and dependency management for ETL/ML pipelines | Jul 2026 | Prefect documentation (Get started) |
Limits & Quotas
| Attribute | Value | As of | Source |
|---|---|---|---|
| Scalability and Integration | Prefect's official integrations catalog lists 20 integrations, 17 maintained by Prefect, including Databricks, Snowflake, Kubernetes, and AWS; a vendor case study (LiveEO, satellite-data processing) reports a 63% reduction in AWS spending after migrating to Prefect. | Jul 2026 | Prefect Blog — LiveEO case study |
community
| Attribute | Value | As of | Source |
|---|---|---|---|
| GitHub stars (May 2026) | 23,500+ stars | Jul 2026 | GitHub API |
technical
| Attribute | Value | As of | Source |
|---|---|---|---|
| Execution model | Control plane hosted by Prefect; workers run in customer infra (Kubernetes, ECS, Cloud Run, local) | Jul 2026 | Prefect docs (work pools concept) |
Strengths
- ●Python-decorator API is the most ergonomic for data and ML teams writing Python
- ●Event-driven flows and durable task results in Prefect 3.x cover modern data patterns
- ●Free Cloud tier suitable for small teams to start without procurement
- ●Strong fit for replacing Airflow on Python-only data pipelines
- ●Dynamic DAGs resolve at runtime, enabling loops and conditional flows not easy in static engines
- ●Python-native API with decorators (@flow, @task) provides a lower learning curve for data engineers
- ●Hybrid execution model keeps workflow code in your infrastructure while the control plane manages scheduling
- ●23,000+ GitHub stars with active commits and a commercial sponsor (Prefect Technologies)
- ●Transactions with on_rollback and on_commit hooks provide real compensation
- ●Flows are plain Python files: unit-testable, diffable and version-controlled
- ●Events and automations support reactive, proactive, metric and composite triggers
- ●Hybrid model runs execution on infrastructure the customer controls
- ●Python-decorator-based task definition feels natural for data engineers
- ●Hybrid execution model keeps data on local infrastructure
- ●Dynamic task generation at runtime without pre-registration
- ●Strong observability with built-in flow run history and alerting
- ●Python-native with decorator-based API for minimal boilerplate
- ●Open-source core with 23,000+ GitHub stars
- ●Prefect Cloud free tier for personal pipeline orchestration
- ●Modern Python-native API with decorators-based workflow definition
- ●Hybrid execution model keeps data in user infrastructure
- ●Free Prefect Cloud tier for small teams
Limitations
- ●Python-only — not suitable for multi-language back-end teams
- ●Smaller production-at-scale references than Temporal or Airflow
- ●Cloud pricing scales with task runs and can rise quickly at high cardinality
- ●Smaller integration ecosystem than Airflow — fewer provider packages
- ●Python-only SDK constrains teams that need workflows in multiple languages
- ●Prefect Cloud free tier is limited; self-hosted Server has fewer features than Cloud
- ●Python-only, with no TypeScript, Go or Java SDK
- ●No deterministic replay; a crashed worker yields zombie runs rather than resuming mid-function
- ●Run retention caps at 7 to 30 days by tier and audit-log retention at 24 to 72 hours
- ●Prefect Cloud published quotas but no prices as of 28 July 2026
- ●The Dagster acquisition announced 13 July 2026 adds roadmap uncertainty
- ●Smaller community and connector ecosystem than Airflow
- ●Cloud pricing increases significantly at enterprise scale
- ●Migration from Prefect 1 to Prefect 2 required significant rework
- ●Fewer managed service options than Airflow
- ●Python-only — no support for other languages natively
- ●Smaller ecosystem of pre-built integrations than Airflow
- ●Enterprise Cloud pricing can be significant at scale
- ●Smaller plugin ecosystem than Apache Airflow
- ●Newer project with less battle-tested production track record
- ●Self-hosted server requires PostgreSQL and additional infrastructure
Based on evaluations in 6 rankings: Best Durable Workflow Engines for Production in 2026, Best Open-Source Workflow Engines for Engineers in 2026, Best Process Orchestration Platforms 2026, Best Automation Tools for Data Teams in 2026, Best ETL & Data Pipeline Tools 2026, Best Open Source Automation Platforms 2026
Pricing Plans
Hobby (Free)
Free forever
- ✓1 workspace, 5 deployments
- ✓500 serverless min/mo
- !2 users, 7-day retention
Starter
$100/mo flat, up to 3 users
- ✓20 deployments, 75 serverless hrs
- ✓No per-task/per-run charges
- !3 users
Team
$100/user/mo, 4-user minimum ($400/mo floor)
- ✓More serverless hours
- ✓Overage $0.005/min
- !4-8 users
Enterprise
Custom (contact sales)
- ✓Dedicated infra, audit logs, SLA
About Prefect
Prefect is a Python-first workflow orchestration platform used to schedule, observe, and recover data pipelines and operational jobs. Founded in 2018 by Jeremiah Lowin, a former Apache Airflow committer, the company is based in Washington DC and has raised roughly $50M across Series A and B rounds (third-party trackers differ on the exact total) and announced its acquisition of Dagster Labs on 13 July 2026. The open-source engine is Apache 2.0 licensed; the GitHub repository (prefecthq/prefect) passed 23,000 stars by July 2026 and the project ships under semantic versioning, with Prefect 3 generally available as of Q3 2024.
The core abstraction is a flow, which is an ordinary Python function decorated with @flow. Tasks are functions decorated with @task. Both can be composed dynamically: tasks can be created inside flows at runtime, parameters can change shape between runs, and there is no static DAG to compile. This dynamicness is the main differentiator from Airflow, where DAG structure must be known at parse time. Prefect retries, caches, schedules, and observes individual task runs through a control plane that the user can host in Prefect Cloud or self-host as Prefect Server.
Hybrid execution is the operational model that distinguishes Prefect Cloud from most managed orchestrators. The control plane (UI, API, scheduler, observability) runs in Prefect's infrastructure; the worker pool that actually executes flow code runs in the customer's environment, typically as Kubernetes work pools, ECS tasks, Cloud Run jobs, or local processes. Customer code and data never leave the customer's network, which is why Prefect is common in regulated industries that cannot put PII on a vendor's compute.
Pricing as of July 2026: the free Hobby tier covers two users and five deployments; Starter is $100 per month flat for up to three users; Team is $100 per user per month with a four-user minimum; Pro and Enterprise are quote-based, varying with seat count, audit-log retention, and SSO requirements. Self-hosted Prefect Server is free under Apache 2.0 with no feature gating on the core engine.
flowchart LR
A[Prefect Cloud Control Plane] -->|Schedules, polls| B[Customer Work Pool]
B --> C[Worker on Kubernetes]
C -->|Executes @flow code| D[Customer Data]
C -->|Reports state, logs| A
A --> E[UI, Alerts, Audit Log]
Editor's Note: A 35-person climate-tech client migrated 41 Airflow DAGs to Prefect 3 in late 2025; engineering time per pipeline change fell from a measured median of 38 minutes to 9 minutes, mostly because dynamic task generation removed a layer of "macro-then-templating-then-Jinja" indirection. Where Prefect punishes you: the cost model is opaque until you actually run sustained workloads, because work-pool concurrency limits and observability retention quietly compound. The same client overshot the Pro plan by week 6 and re-tiered to a $1,400/mo Enterprise quote; budget for that, do not assume Pro covers a real team. — Rafal Fila, ShadowGen
Integrations (4)
Other Workflow Automation Tools
Activepieces
No-code workflow automation with self-hosting and AI-powered features
Workflow AutomationAutomatisch
Open-source Zapier alternative
Workflow AutomationBardeen
AI-powered browser automation via Chrome extension
Workflow AutomationCalendly
Scheduling automation platform for booking meetings without email back-and-forth, with CRM integrations and routing forms for lead qualification.
Workflow AutomationSee How It Ranks
Best Durable Workflow Engines for Production in 2026
A ranked list of the best durable workflow engines for production deployments in 2026. Durable workflow engines persist execution state to a database so that long-running workflows survive process restarts, deployments, and infrastructure failures. The ranking covers Temporal, Prefect, Apache Airflow, Camunda, Windmill, and n8n. Tools were evaluated on production reliability, developer experience, scalability, open-source health, and documentation quality. The shortlist intentionally mixes code-first engines (Temporal, Prefect, Airflow) with hybrid visual platforms (Camunda, Windmill, n8n) to reflect how production teams actually choose workflow engines in 2026.
Best No-Code Automation Platforms in 2026
A ranked list of no-code automation platforms in 2026. The ranking covers visual workflow builders that allow non-engineering teams to connect SaaS apps, route data, and add conditional logic without writing code. Entries cover proprietary cloud platforms (Zapier, Make, Pipedream, IFTTT) and open-source visual builders (n8n, Activepieces). Scoring reflects integration breadth, pricing accessibility, visual editor ease, reliability and error handling, and self-hosting availability.
Questions About Prefect
How much does Kestra cost in 2026?
Kestra's core platform is free and open-source (Apache 2.0) with unlimited flows and executions when self-hosted. As of July 2026 the Enterprise Edition is a quote-based annual subscription priced per instance, and the managed Kestra Cloud is in early access with usage-based pricing; neither publishes dollar prices, so budgeting starts with infrastructure costs plus a sales conversation.
Prefect vs Apache Airflow: which workflow orchestrator should you choose in 2026?
Apache Airflow 3.3 is the ecosystem standard: 46,000+ GitHub stars, the deepest operator ecosystem, and managed options from AWS, Google Cloud, and Astronomer — with Airflow 2 end-of-life since 22 April 2026. Prefect 3 offers a lighter Python-native model (@flow/@task, no XComs) with Prefect Cloud from $100/month, and announced its acquisition of Dagster Labs on 13 July 2026.
What are the best open-source workflow engines in 2026?
The top open-source workflow engines in 2026 are [Temporal](/tools/temporal-workflows/) (durable execution with multi-language SDKs), [Apache Airflow](/tools/apache-airflow/) (the de facto data DAG orchestrator), and [Prefect](/tools/prefect/) (modern Python-first workflow framework).
What are the best Prefect alternatives in 2026?
As of April 2026, the leading Prefect alternatives are Apache Airflow (most-deployed open-source orchestrator), Dagster (asset-based pipelines), Temporal (durable workflow execution), Windmill (script-first platform), and Mage (notebook-friendly data pipelines). Choice depends on whether the team prefers DAG files, software-defined assets, or general-purpose code.
Learn More
Kestra vs Apache Airflow (2026): Declarative YAML vs Python DAGs
Kestra and Apache Airflow are both open-source orchestration platforms, but they represent different generations of design: Airflow (2015, Apache Software Foundation) defines pipelines as Python DAGs with a vast managed-service ecosystem, while Kestra (2019 project, 2021 company) defines flows in declarative YAML on a Java engine with event-driven and realtime triggers. This comparison covers language model, triggers, ecosystem maturity, deployment, and licensing as of July 2026.
Temporal vs Apache Airflow 2026: Durable Workflows vs DAG Orchestration
Apache Airflow is an Apache 2.0 DAG-based workflow scheduler created at Airbnb in 2014 and now maintained by the Apache Software Foundation. Temporal is an MIT-licensed durable execution engine started in 2019 by the team behind Uber Cadence. Airflow specialises in scheduled batch data pipelines; Temporal specialises in stateful, long-running application workflows. Many data platforms in 2026 run both side-by-side.
dbt vs Apache Airflow in 2026: Transformation vs Orchestration
A detailed comparison of dbt and Apache Airflow covering their distinct roles in the modern data stack, integration patterns, pricing, and real 90-day deployment data. Explains when to use each tool alone and when to use both together.