Prefect is a workflow orchestration tool that assists you in creating machine learning pipelines. It offers both an open-source version and a managed cloud service (Prefect Cloud) with several pricing tiers. But is Prefect’s pricing high, low, or reasonable for what it provides?

This guide takes an analytical look at Prefect’s pricing plans, key factors to consider before investing in the platform, and whether it’s an investment-worthy MLOps tool.

TL;DR

Here’s an overview of all the plans Prefect offers:

Prefect is worth investing when:

✅ You need a reliable pipeline scheduling, monitoring, and orchestration, but don’t want to manage infrastructure yourself.

✅ Your team fits comfortably within the $100-$400 per month pricing tiers (3-8 developers)

✅ You can use the ‘bring your own compute’ option efficiently.

But there are some use cases where you'd better rely on an alternative and not invest in Prefect:

❌ If you need experiment tracking, model registries, data versioning, or hyperparameter tuning. Prefect requires you to integrate with external tools like MLflow or Weights & Biases for these capabilities, adding complexity and costs.

❌ You're a small ML team that would quickly outgrow the limited free tier (1 user, 5 workflows) but can't justify $100+/month just for basic orchestration when you still need to pay for additional ML tools.

Prefect Pricing Plans

Prefect Cloud uses a straightforward tiered pricing structure that scales with your team’s needs.

In 2025, Prefect introduced new self-serve plans to bridge the gap between a free tier and large enterprise contracts.

All plans use seat-based pricing (you pay for a certain number of developer seats) and include a quota of serverless compute minutes for running workflows on Prefect’s infrastructure.

What’s more, Prefect doesn’t charge per task or execution – you get predictable costs without usage-based fees.

👀 Note: While Prefect doesn’t bill per task, running beyond the monthly serverless-credit allotment is metered (prices aren’t public; you must contact sales).

Here’s a quick breakdown of Prefect’s current plans:

👀 Note: All Prefect plans include core orchestration features like scheduling, monitoring, and logging. Higher-tier plans add capabilities for collaboration and security (role-based access control and single sign-on). Prefect’s pricing model avoids per-run fees, so you won’t be charged for each task or flow execution, which helps keep costs predictable.

Prefect Pricing Factors to Consider

Before deciding if Prefect is worth the investment, it’s important to consider a few key factors that can influence the true cost and value of the platform for your use case. Below, we discuss four critical factors:

• Team size
• Compute usage
• Security requirements
• Integration needs

1. Team Size and Collaboration Needs

As mentioned before, Prefect’s pricing is seat-based, meaning each plan limits the number of developer seats included.

Consider how many engineers or data scientists will actively use Prefect. If you have a growing team, the cost can escalate when moving up tiers.

Also, collaborative features like role-based access control (RBAC) and multi-workspace support are only available in higher plans.

2. Compute Resources and Serverless Credits

Prefect Cloud includes a certain amount of Prefect Serverless execution time (measured in minutes) with each plan. These serverless credits allow you to run flows on Prefect’s managed infrastructure without setting up your own machines.

The quotas are generous for intermittent or smaller workloads, but heavy usage could deplete them quickly. If your data pipelines run very frequently or have long-running tasks, you need to plan for what happens after using up the included minutes.

Prefect’s paid plans (Starter and above) support a “bring your own compute” option – meaning you can deploy and run workflows on your own infrastructure or cloud instances.

3. Security and Governance

Many advanced security features for Prefect are only available in the Team and Enterprise plans.

The Team tier introduces audit log retention and service accounts, but full SSO integration (SAML/OIDC), advanced user roles, and granular RBAC permissions come with the Enterprise plan.

If your company requires single sign-on with an enterprise identity provider, or if you need to enforce fine-grained permissions on flows and data, you have no other option but to opt for the Enterprise plan, which substantially increases your cost.

All Plans that Prefect Offers (Open Source + Paid)

Prefect offers a mix of free and paid options for users. In total, there are two free options – the open-source Prefect software and the Prefect Cloud Hobby tier. And three paid plans on Prefect Cloud – Starter, Team, and Enterprise.

Below, we break down each of these offerings, including their pricing details and key features.

Prefect Open Source – Free Plan (Self-Hosted)

Prefect Open Source is the free, self-hosted version. It’s 100% free to use under an open-source license, with no limits on users, workflows, or runtime – you can run as many flows as your own infrastructure can handle. This option essentially means setting up the Prefect orchestration engine on your own servers or cloud environment.

The major benefit is cost savings: you pay nothing to Prefect, and you avoid any seat fees or cloud subscription.

However, the open-source plan comes with a trade-off in features and maintenance.

• You’ll be responsible for deploying and managing the Prefect server (or using Prefect’s CLI for local orchestration).
• There is a basic UI for Prefect open source, but it lacks the polished dashboards and aggregated insights of Prefect Cloud.
• The OSS version doesn’t include advanced cloud-only features like hosted automation, built-in SSO, or long-term logging.
• You get no official support beyond community forums.

Prefect Cloud Hobby Plan – Free Tier

The Hobby plan is Prefect Cloud’s free tier, which offers a managed solution for individuals and small projects. At a cost of $0, it provides a low-friction way to try Prefect Cloud without installing anything yourself.

Here are the key features you get with the Hobby plan:

• 1 developer seat, 1 Workspace, and up to 5 workflows (deployments).
• 500 minutes of serverless compute per month, meaning you can run flows on Prefect’s cloud infrastructure up to that runtime limit. This is usually enough for testing and small-scale schedules.
• Despite being free, the Hobby plan benefits from Prefect Cloud’s hosted environment – you get the nice UI, cloud-hosted orchestration, and easy setup.
• Features like Logging & alerting, and 5 event-driven automations
• API rate limit of 250 per minute
• Community support channel

This tier is good for you to learn about Prefect or automate a few personal tasks.

That said, limitations of the Hobby plan might be too restrictive for most production teams: only one user account is allowed, and 5 workflows might be too restrictive if you have multiple projects or complex pipelines.

There are feature restrictions like no service accounts or advanced permissions. Once you start pushing those limits – say, you want to collaborate with teammates or run more workflows on a schedule – you’ll need to move to a paid tier.

Starter Plan – $100 per Month

The Starter plan costs $100 per month and is Prefect’s entry-level paid offering. It’s designed for small teams or more serious projects that have outgrown the free tier.

Here’s what it offers:

• 3 developer seats, 1 Workspace, 20 deployments.
• 4,500 minutes of serverless compute per month
• 1 additional work pool. Work pools are a bridge between the Prefect orchestration layer and the infrastructure where flows are run.
• Ability to bring your own compute. This means your Prefect agents can run on your own servers or a Kubernetes cluster, in addition to using Prefect’s hosted execution.
• Features like Logging and alerting, webhooks, and 10 event-driven automations.
• SCMI (GitHub, Google, and Microsoft) allows you to control access to Prefect Cloud from your preferred identity provider.
• 7-day run retention and API rate limit of 500 per minute.
• Community support

Team Plan – $400 per Month

The Team plan is priced at $400 per month and targets growing teams with greater orchestration demands.

• The plan supports up to 8 developer seats, 1 workspace, and 50 monthly deployments.
• Includes 13,500 minutes of serverless compute credits per month.
• 3 additional work pools.
• Ability to bring your own compute.
• All features in the Starter Plan + service accounts and 50 event-driven automations.
• 24 hours audit log retention and 14 days run retention.
• API rate limit of 1,000 per minute.

Enterprise Plan – Custom Pricing

The Enterprise (Pro) plan is Prefect’s top-tier offering with custom pricing. To know how much this plan will cost, contact the Prefect's support team.

The plan offers everything Prefect has:

• Unlimited developer seats, workflows, and deployments.
• Custom serverless compute credits and unlimited additional work pools.
• 500+ event-driven automations and all features that the Team plan offers.
• SAML/OIDC, SCIM security, custom audit log retention, and run retention.
• Granular permissions & RBAC.
• 24x7 support available via portal, email, and response SLAs.
• 99.9%+ uptime service level agreements (SLAs).

Is Prefect Expensive?

Now to the big question: Is Prefect worth the money, or is it expensive for what it offers?

The answer depends on your context, but there are a few observations we can make.

First, Prefect’s introduction of the Starter and Team self-serve plans has lowered the cost barrier significantly for small and mid-sized teams.

At $100 or $400 per month, these plans are relatively affordable in a business setting (roughly $33–$50 per user seat on Team, for example).

Compared to its earlier pricing (where the only paid option was ~$1850/month), Prefect is now much more accessible.

For what you get – a fully managed orchestration platform with a lot of functionality – the value can be quite good at those tiers.

However, Prefect can indeed become expensive at scale or for certain use cases. The free options have strict limits (1 user or very limited runs), which means any serious team will be paying for at least the Starter plan. If you need to go to Enterprise, the costs jump dramatically into custom pricing territory.

Another consideration is the “hidden” costs.

Using Prefect Cloud in production might entail paying for additional compute. If your workflows need more than the included serverless minutes, you’ll incur cloud infrastructure costs on your side.

Additionally, if you require capabilities outside Prefect’s scope (like experiment tracking, model management, etc.), the cost of those additional tools contributes to the overall expense of your MLOps stack.

ZenML – An Affordable Alternative to Prefect

If you’re concerned about Prefect’s pricing or its limited scope for ML-specific needs, ZenML is an affordable alternative.

ZenML is an open-source MLOps framework that focuses on pipeline-centric machine learning workflows. The platform’s philosophy is to provide an end-to-end pipeline tool that integrates experiment tracking, model management, and more, reducing the need for multiple disparate tools.

Let’s look at how ZenML can replace or augment key aspects of Prefect’s functionality, often at a more accessible price point.

Feature 1. Workflow Orchestration

ZenML lets you define pipelines (analogous to Prefect flows) with multiple steps (tasks) in Python. The platform takes care of running them with the correct dependencies and order.

The key difference - ZenML is designed from the ground up for ML workflows; things like data preparation, model training, evaluation, and deployment can all be orchestrated through ZenML pipelines. It integrates with multiple third-party orchestration tools like Apache Airflow, AzureML, Databricks, Docker, and more.

Prefect focuses on orchestration and observability, ZenML bundles orchestration plus experiment tracking, model registry, etc.

From a pricing perspective, ZenML’s orchestration comes built-in with the open-source offering, so you’re not paying per seat or per run. You don’t need a separate cloud account just to schedule pipelines; you can run ZenML on your existing infrastructure or even locally. To get managed orchestration in Prefect, you’d have to be on a paid cloud plan.

Feature 2. Experiment Tracking

Experiment tracking refers to recording metrics, parameters, and outcomes of model training runs – something data scientists often need. Prefect itself doesn’t offer experiment tracking; you would have to integrate Prefect with an external platform, like MLflow or Weights & Biases. This not only adds complexity but also extra costs.

ZenML has experiment tracking capabilities baked in. When you run ZenML pipelines, it can log parameters and metrics for each step, version your artifacts, and keep track of model versions. If needed, it also has the option to integrate with an experiment tracker.

Feature 3. Integration with MLOps Tools

Both Prefect and ZenML offer integration with external tools, but ZenML's integrations are specifically curated for the ML lifecycle:

• Modeling: Hugging Face, PyTorch, TensorFlow
• Artifact store: Amazon S3, Azure Blog Storage, GCS
• Data visualization: Facets
• Deployer: BentoML, Databricks, Seldon

And more.

ZenML's ‘stack’ concept makes it simple to compose these integrations into a cohesive MLOps platform, avoiding the fragmentation that often occurs when building ML pipelines with general-purpose tools like Prefect.

From a cost perspective, ZenML’s integrations are part of the open-source platform. This means you’re not paying extra to connect, say, your pipeline to AWS S3 or to a model registry – it’s all handled in the same framework. In contrast, using Prefect might lead you to adopt multiple vendors (each potentially with its own cost) for various needs around your pipeline.

Beyond the points above, it’s worth noting some additional features ZenML offers that Prefect does not natively provide:

• Model Registry: ZenML can function as a model registry or connect to one, allowing you to version and catalog your machine learning models within the pipeline workflow.
• Hyperparameter Tuning: Integrates with hyperparameter optimization frameworks (like Optuna or others) to enable hyperparameter tuning as part of your pipelines.
• Data Versioning: ZenML emphasizes reproducibility, including data versioning support. It can tie in with data version control systems or ensure that datasets and data artifacts used in pipelines are tracked.

Wrapping Up

Prefect offers a range of pricing options to accommodate different user needs, from free plans suitable for individuals and small teams to paid options for larger organizations with more complex requirements. For general data orchestration, Prefect provides a solid platform with competitive pricing compared to other enterprise options.

However, for teams working specifically with machine learning workflows, ZenML is a pretty good alternative that combines workflow orchestration with ML-specific features at a more accessible price point. Its integrated approach to the ML lifecycle eliminates the need for multiple tools, potentially resulting in significant cost savings.

If you're looking for a platform where you can build reproducible AI workflows for your MLOps platform team, ZenML fits the bill.

Try ZenML for free today and discover how it can streamline your ML workflow orchestration at a fraction of the cost compared to Prefect.