Outerbounds Pricing Guide: How Much Does It Cost?

When evaluating MLOps platforms, pricing transparency plays a crucial role in decision-making. Outerbounds, built on the open-source Metaflow framework, offers a managed platform for building and deploying ML workflows at scale. But the question is – how much does it actually cost, and is it worth the investment for your team?

In this Outerbounds pricing guide, we talk about all the pricing plans the platform offers, key cost factors, and conclude whether the platform is worth investing in. We'll also look at an alternative, ZenML, that might better suit your requirements and budget.

TL;DR

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

Outerbounds is worth investing in when:

✅ You want a fully managed, production-grade MLOps platform deployed securely inside your own cloud account, keeping sensitive data completely in your control.

✅ Your team is large or rapidly growing, benefiting significantly from the unlimited user seats model without incurring extra per-user charges.

✅ You require strong built-in compliance (SOC 2), reliable SLA-backed support, and easy integration with single sign-on providers (e.g., Google, GitHub) without additional overhead.

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

❌ You're a small team or solo developer with limited workflows. Paying $2,499/month (nearly $30,000/year) is steep compared to free or cheaper open-source tools, especially if your compute needs remain modest.

❌ You prefer completely vendor-managed infrastructure (SaaS-style) rather than managing a platform deployed in your own cloud environment, even if deployment itself is managed.

Outerbounds Pricing Plans

Outerbounds has 3 official pricing plans and 3 other plans that a few marketplaces offer.

Let’s start by understanding the three official plans Outerbounds offers – Community, Starter, and Enterprise.

The Community plan is essentially the open-source Metaflow project that you can use for free by setting it up yourself. The paid plans (Starter and Enterprise) provide Outerbounds’ fully managed platform deployed in your cloud account, with a fixed subscription fee rather than usage-based billing.

This means you pay a predictable platform fee for unlimited usage, which is a unique selling point compared to some MLOps platforms that charge per user or per computation.

Another way to purchase and deploy Outerbounds is via some major cloud marketplaces. Outerbounds is available on AWS, Google Cloud, and Azure marketplaces, which means you can use your existing cloud billing and even enterprise discounts/credits to pay for Outerbounds. We talk about each of the above plans in detail later in the article.

But before that, let's discuss the factors to consider before making a decision to invest in Outerbounds.

Outerbounds Pricing Factors to Consider

When evaluating how much Outerbounds will cost your team, keep in mind a few key factors that influence the value and total cost of ownership.

1. Compute Capacity

Most cloud platforms charge you based on how much computing power you actually consume – you end up paying for things like each hour your code runs or each gigabyte of data you process.

Outerbounds works differently: you pay a fixed monthly fee for the platform, then you can run as much computing as you want (within the limits of your cloud account and the included nodes).

The starter plan includes 3 managed compute nodes (for example, 3 VMs or Kubernetes nodes orchestrated by Outerbounds); this is typically enough to handle small to moderate ML workloads concurrently.

Heavy users win big – Outerbound’s flat fee becomes a steal when you’re running lots of workloads, since you pay raw cloud rates without markup. Your cost becomes predictable and often much lower per run.

Light users pay a premium – If you only run occasional workflows, that fixed flat fee is quite expensive for you. You’d save a ton of money with pay-as-you-go solutions.

2. Seats and Workstations

One of the biggest advantages of Outerbounds is that it doesn’t charge per seat. All plans allow unlimited users on the platform. It’s quite helpful for you if your ML and data engineering teams are constantly growing.

However, one limitation to note is in the included development environment: the Starter plan offers only 3 cloud workstations for your team. These are basically managed development instances hosted in your cloud for convenient access to data and code.

For a small team, 3 shared workspaces might be fine, but if you have, say, 10 users who all want their own isolated cloud workstation, the Starter tier’s allowance could be a bottleneck.

You’d then have only one option, that is to talk to Outerbounds' team for an Enterprise deal.

3. Security and Compliance Add-Ons

For many companies, particularly in regulated industries or large enterprises, the cost of a platform is justified only if it meets strict security and compliance standards.

Security and compliance features are tiered across Outerbounds' plans.

• The Community plan offers single-tenant deployment.
• The Starter plan adds Single Sign-On (SSO) capabilities for Google, GitHub, and GitLab, along with machine-to-machine authentication and SOC 2 compliance.
• The Enterprise plan expands on this, offering broader SSO options including Okta and Azure AD, unlimited advanced Role-Based Access Control (RBAC), audit logs, and multi-region deployment, all while maintaining SOC 2 compliance.

All Plans Included that Outerbounds Offers (Open Source + Paid)

Outerbounds currently offers 1 open-source option and 2 paid plans. Below, we dive deeper into what each plan includes in terms of features and ideal use cases:

Community Plan (Open Source Metaflow)

Outerbound’s community plan is nothing but the option to use Metaflow – an open-source framework for ML pipelines. In this plan, you self-host everything.

You install Metaflow in your environment (it can run on a local machine or on your own cloud infrastructure) and manage all the supporting infrastructure like storage, compute, and integrations.

Talking about support, Outerbounds provides documentation and a Slack community, but no formal support or manager services for this plan.

Here’s what you get:

• Access to all core open-source Metaflow capabilities for building and managing ML pipelines.
• Automatic versioning and tracking of all workflow runs, parameters, and artifacts.
• Built-in support for AWS services like S3, Batch, and SageMaker without additional configuration.
• Access to third-party integrations to platforms like MLflow, Kubeflow, Airflow, and more.

Unfortunately, for the Community plan, you don’t get any of the Outerbounds’ managed services or UI. There’s no fancy Outerbounds dashboard – you’ll likely be using the command-line and custom scripts for visualization.

You won’t have the Outerbounds cloud workstations or one-click deployments. So, setting up cloud resources like a Kubernetes cluster for Metaflow’s step functions, or a tracking server, is on you.

🤷 Who is it for: This plan is ideal for individual developers, researchers, or very early-stage startups that have more time than money and are comfortable managing infrastructure.

There are some drawbacks of Metaflow due to which engineering teams tend to look for alternatives. Drawbacks like:

• Teams must handle significant operational overheads.
• Metaflow’s user experience is largely code-and-CLI-driven.
• The platform has no native Windows support

All these drawbacks are taken care of by Outerbounds’ Starter and Enterprise plans, as well as by the plans that different marketplaces offer.

Starter Plan – $2,499 per Month (billed annually)

The Starter plan is the entry point to Outerbounds’ fully managed platform. At $2,499 per month (with annual commitment), it’s aimed at small-to-medium teams that want a turnkey MLOps solution.

Here’s what the plan offers:

• Outerbounds takes care of provisioning and managing the core infrastructure in your cloud subscription. You get the benefits of a cloud service while keeping data and compute within your cloud tenancy for security.
• Comes with 3 managed compute nodes. These can be autoscaled to fully managed - no need to manually handle EC2 instances or Kubernetes node groups; Outerbounds does that for you.
• There are no limits on the number of workflows, experiments, or users (with a maximum of 3 workstations and 3 nodes). You can have 5 or 50 users on the platform – all are covered under the same fee. This ‘all-you-can-eat’ model is perfect for avoiding variable costs.
• At the Starter level, you get security features like single tenant deployment, authentication with SSO (Google, GitHub, GitLab), machine-to-machine authentication, SOC 2 compliance, and cost attribution and reporting.

🤷 Who is it for: The Starter plan is best for teams that have outgrown pure open-source DIY solutions and need a reliable platform, but aren’t at the enterprise scale yet. This could be a startup with a small ML team, or a mid-size company’s data science department that wants to move faster without waiting on internal platform engineering.

Enterprise Plan – Custom Pricing

Outerbound’s Enterprise plan is for you if you need scalability, advanced features for ML pipelines, and hands-on support.

Apart from everything the Starter plan offers, here’s what the Enterprise plan gives you:

• Supports unlimited compute nodes and workstations. This means if you have 10, 50, or 100 nodes worth of workload, Outerbounds can manage that.
• Integration with an enterprise identity system like Okta and Azure AD for SSO. You also get fine-grained RBAC to define roles and permissions on projects.
• Enterprise customers usually receive a higher level of support.

🤷 Who is it for: The Enterprise plan, of course, is tailored more towards larger companies, or smaller ones with pretty stringent requirements. If ML is a core part of your business and you need a robust platform to integrate with your enterprise IT, this plan makes sense.

Outerbounds on Could Marketplaces

Apart from the above plans, another convenient way to purchase and deploy Outerbounds is via three different cloud marketplaces – AWS, Google Cloud, and Azure.

AWS Marketplace

The Outerbounds Platform is available on the AWS Marketplace. The pricing structure on AWS is based on a combination of contract duration and terms, plus additional usage.

For instance, a 12-month contract for ‘Platform access, onboarding and standard support’ is listed at $60,000. Beyond this, additional usage costs are incurred at $0.10 per Outerbounds Consumption Unit (OBU). You pay recurring monthly usage fees directly through your AWS bill.

To estimate how much additional usage cost you might incur, use the AWS pricing calculator.

This AWS X Outerbounds plan has similar features and limits to the Starter plan of Outerbounds. The platform offers features like SOC 2, single-tenant K8 cluster, event bus orchestration, metadata tracking, and SLA support.

All in all, it’s effectively a Starter plan with some extra capacity. It offers unlimited seats, up to 3 cloud workstations, 5 compute nodes (3 base + 2 add-on). You can add more nodes via a private offer.

Google Cloud Marketplace

Outerbounds joined the Google Cloud Marketplace as a Partner Advantage offering. This means if you’re a GCP user, you can purchase Outerbounds through GCP.

The benefit here is leveraging Google’s marketplace incentives – if you are committed to using discounts or promotional credits on GCP, Outerbounds might be payable with those.

Integrating Outerbounds within Google Cloud also ensures compatibility with GCP services and easy setup (for instance, deploying Outerbounds control plane on Google Kubernetes Engine).

Outerbounds even announced a partnership with Google Cloud to offer ‘full-stack MLOps on Google Cloud Marketplace,’ highlighting that you can develop and scale ML workflows on GCP with Outerbounds, while using Google’s secure infrastructure.

👀 Note: Google acts only as the billing channel. The features you get depend on whichever Outerbounds tier (Starter or Enterprise) you negotiate and subscribe to.

Azure Marketplace

If you’re a Microsoft Azure user, Outerbounds is listed on the Azure Marketplace as well. Similar to AWS and GCP, this allows Azure enterprise customers to purchase Outerbounds with their Azure agreements.

Again, it’s the standard fully-managed Outerbounds install deployed inside your Azure subscription; the listing just streamlines procurement against Azure commit. The features and pricing for the platform are exactly the same as AWS marketplace offers – a 12-month contract for ‘Platform access, onboarding and standard support’ is listed at $60,000. Beyond this, additional usage costs are incurred at $0.10 per Outerbounds Consumption Unit (OBU).

Is Outerbounds Expensive

Whether Outerbounds is ‘expensive’ depends on your perspective and needs. Let’s break it down:

At $2,499 per month for the Starter plan, Outerbounds certainly asks for a significant investment from a small team or individual. That’s about $30k per year.

If you’re a lone data scientist or a tiny startup with just a couple of ML projects, paying this flat fee isn’t an ideal solution, especially when compared to the $0 cost of using purely open-source tools like Metaflow or ZenML by yourself (aside from cloud bills).

One thing to note, though – Outerbounds’ flat fee actually saves money for a team that runs lots of pipelines. For a mid-sized company or an enterprise, $30k/year might be quite reasonable – it’s likely less than the cost of one senior engineer, and it covers the whole team’s MLOps needs.

For larger enterprises, the question is not the absolute cost, but the value. Outerbounds Enterprise might cost in the high five to six figures annually, but if it enables 50 data scientists to be more productive and gets models to production faster, the ROI is justified.

🤷 Who is it a fit for? Outerbounds is a great fit for teams that need reliability and are hitting the limits of DIY solutions. If you find yourself spending a lot of time maintaining ML pipelines, dealing with AWS infrastructure bugs, or worrying about scaling and security, Outerbounds can be worth it.

ZenML – An Affordable Alternative to Outerbounds

While Outerbounds offers a powerful managed platform, not every team will have the budget or need for it. ZenML is an MLOps framework that can serve as a more accessible alternative in many scenarios. Like Outerbounds, ZenML is designed to help you orchestrate and manage ML pipelines.

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

Feature 1. Cloud-Agnostic Orchestration

ZenML enables you to orchestrate ML workflows across different infrastructures with ease. You define your pipeline in Python using ZenML’s SDK, and then deploy it to the orchestrator of your choice – Apache Airflow, Kubeflow, Prefect, Kedro, and more. This means you’re not tied to a single cloud or platform – you can even run hybrid workloads.

Outerbounds also runs in any cloud, but ZenML gives you more granular freedom: you’re free to integrate with on-premise clusters or managed workflow engines.

ZenML’s approach is flexible: it doesn’t impose new infrastructure; instead, it interfaces with what you already have or prefer, which can save costs if you already use those services.

Feature 2. Pluggable Experiment Tracking

Experiment tracking and model management are crucial in MLOps. Outerbounds provides an integrated solution based on Metaflow’s metadata tracking.

With ZenML, you have the freedom to choose your experiment tracker and model registry. ZenML natively integrates with popular tools like MLflow, Weights & Biases, CometML, and others to track experiments.

This means you can use MLflow Tracking with ZenML to log all your hyperparameters, metrics, and artifacts for each pipeline run, and even use MLflow’s model registry. If tomorrow a new experiment tracking tool comes along that you prefer, you can swap out MLflow for that in your ZenML stack.

This pluggability is powerful: you’re not paying for a proprietary experiment tracker; you use the best-of-breed (often open source or freemium) tools.

Feature 3. Custom Stack Components

ZenML introduces the concept of a ‘stack’ – a set of components that together form your MLOps pipeline infrastructure - orchestrator, artifact store, container registry, experiment tracker, model registry, etc.  It comes with many pre-built integrations for AWS, GCP, Azure, Kubernetes, Spark, and more, but also allows you to create custom stack components.

This means if you have a custom in-house tool or a niche service you want to use, you can write a ZenML integration for it and include it in your pipeline stack. Outerbounds, being a managed platform, is less extensible in that sense – it has a defined set of features and integrations.

ZenML’s customizability is a big plus if you need something beyond what typical platforms offer. For example, if you want to use a specific data versioning system or a specific model deployment target, ZenML likely can integrate with it, or you can add it.

ZenML comes with other capabilities that cover much of what Outerbounds does and more:

• Offers a built-in registry for models to version and manage model deployments.
• Automatically version-controls data artifacts produced in pipelines, ensuring reproducibility.
• Integrate with hyperparameter optimization libraries or orchestrate parallel runs for tuning.
• The platform is developer-friendly (just Python code to write pipelines) and has an active community and comprehensive documentation.

In short, ZenML can replace much of Outerbounds’ functionality at a fraction of the cost, especially for teams with the technical ability to manage their own stack or are willing to use ZenML Cloud. You get orchestration, experiment tracking, model registry, etc., all under one roof.

Wrapping Up

Outerbounds provides a compelling managed MLOps platform with a predictable pricing model, which can be a game-changer for organizations that need to build ‘Netflix-grade’ ML pipelines without assembling everything in-house.

We’ve seen that Outerbounds’ costs – $2,499/month for Starter and custom pricing for Enterprise – can be justified by the breadth of features - unlimited usage, in-your-cloud deployment, security, support, for the right team. If you need the full-service treatment and have the budget, Outerbounds is certainly worth a close look.

That being said, it’s not the only path to successful MLOps. For those who find Outerbounds expensive or simply prefer more control, tools like ZenML offer an attractive alternative.

ZenML’s framework lets you craft an MLOps stack tailored to your needs, tapping into best-in-class tools without heavy fees or lock-in.

It’s a solution that grows with you – use it for free, plug in the platforms you want, and only pay if you opt for additional managed services down the line.

If you’re evaluating Outerbounds and wondering about the trade-offs, we encourage you to also try out ZenML.

With our platform, you can define your ML pipelines once and let the framework handle experiment tracking, versioning, and even deployments for you – all while staying cloud-agnostic and cost-effective.

📚 Also read: Prefect Pricing Guide