ClearML Pricing Breakdown: Is the Platform Worth the Investment?

ClearML is an open-source MLOps platform available for free (self-hosted) or as a managed cloud service with paid tiers. But is the platform worth the investment?

In this ClearML pricing article, we break down ClearML’s pricing plans, discuss key cost factors, and help you decide if the platform is worth the investment. Later in the article, we also introduce ZenML (disclaimer: this is our product) as an alternative to ClearML.

TL;DR

Here’s a quick summary of ClearML’s pricing tiers. You decide if they make sense for your team or not:

ClearML is worth investing in when:

✅ You want an all-in-one MLOps platform that covers experiment tracking, orchestration, data versioning, and model deployment in one solution. It can save you from piecing together multiple separate tools.

✅ You can leverage the free open-source version or start on the free tier and only pay as your usage grows. Teams that self-host ClearML get tremendous value at minimal cost, and the option to upgrade to a managed plan later offers flexibility.

✅ Your team values flexibility in deployment and pricing. ClearML’s cloud plans let you pay only for what you use, and it also offers on-premises deployment options if you have strict data requirements.

However, you might consider an alternative (or sticking to free tools) if:

❌ You’re a very small team or solo user with basic needs. If you only need simple experiment tracking or a few pipelines, ClearML might be overkill; a lightweight open-source tool or manual setup could suffice at zero cost.

❌ Your projects generate huge volumes of data or experiments. Exceeding ClearML’s included quotas for storage and logging will incur fees. If you’re one of those users who log everything (large datasets, countless metrics), you might find a fixed-cost platform or self-hosted solution more economical in the long run.

❌ You already have a partial MLOps stack in place. If you only need one missing piece (say, just a model tracker or orchestrator), adopting ClearML’s entire platform (and paying for it) might not be worth it. In such cases, using a dedicated tool for that specific function (often free or open-source) could be more efficient.

ClearML Pricing Plans Overview

ClearML’s pricing combines seat-based fees with usage-based charges on the managed service. You can start with the free Community plan (no license cost if you host it yourself, or a limited free SaaS tier for a few users) and then upgrade to paid plans where you pay per user and for any additional usage of the platform’s resources.

The key idea is that ClearML lets you ‘pay as you grow.’ A small team can begin at $0, and as the team expands or runs more experiments, costs scale up gradually (via the $15 per user fee and whatever cloud resources you consume). This can be very cost-effective if your usage remains moderate.

Next, let’s look at some factors that influence how much you might end up paying for ClearML.

ClearML Pricing Factors to Consider

When budgeting for ClearML, keep the following factors in mind:

1. Self-Hosting vs. Managed Service

ClearML’s open-source nature means you have a choice: run it yourself for free (aside from your infrastructure costs) or pay for the convenience of ClearML’s hosted cloud service.

If you have the expertise and capacity, self-hosting ClearML can eliminate subscription fees entirely. On the other hand, the managed service (starting with the Pro plan) saves you the work of deploying and maintaining the infrastructure, which is worth the cost for many teams.

In short, you’ll either invest in managing the platform yourself or invest in ClearML’s service – decide which trade-off makes sense for your team.

2. Team Size and Users

Your team’s size will impact costs. The free Community tier allows up to 3 users on the hosted version, which covers an individual or very small team.

As soon as you have more collaborators, you’ll be paying per user on the Pro plan. Very large teams (beyond 10 users) will need a custom Scale or Enterprise plan.

Remember: ClearML’s open-source self-hosted plan does not impose user limits, so a larger team with DevOps know-how could self-host and avoid per-seat fees. Otherwise, you should budget for the per-user costs as your headcount grows on the hosted service.

3. Usage Volume (Data and Compute)

ClearML’s managed plans include generous usage quotas, but it’s important to monitor how much data you store and log.

Storage: Experiment artifacts like model files, datasets, etc., and metrics are metered if they exceed the free allowance. If your workflow involves saving many large models or logging extensive metrics, those overage fees (e.g., ~$0.10 per extra GB of artifacts) can accumulate.

Compute: ClearML doesn’t charge directly for compute time in a simple per-hour way, but if you use its cloud features to spin up training instances, you’ll ultimately pay for that compute either through your cloud provider or via a custom plan.

The bottom line is that for most moderate workloads, you might never exceed the included limits, and costs will remain predictable. But if you’re a power user running very large experiments or tons of jobs, you should budget for the possibility of additional charges (or consider self-hosting to leverage your own infrastructure at cost).

All Plans that ClearML Offers (Free and Paid)

ClearML currently offers 1 open-source option and 3 paid plans. Let’s see what each of these plans are capable of.

1. Community Plan - $0

Community is ClearML’s free tier (open source). It’s essentially the full ClearML platform available at no cost.

You can either deploy it yourself (open-source, unlimited users) or use ClearML’s free hosted server (limited to 3 users and certain usage caps).

This plan includes all the core features:

• Experiment tracking
• Pipeline orchestration
• Dataset versioning
• Model repository, and so on.

For small-scale use, the provided free resources - around 100 GB of storage and 1M API calls per month on the hosted service - are usually sufficient.

The Community plan is ideal for individuals, researchers, or small projects to get started with ClearML without any budget. Once you outgrow the user count or the usage limits, move up to a paid plan.

2. Pro Plan - $15 per user per month (+ Usage)

The Pro plan is ClearML’s primary paid offering for teams. It’s a fully managed SaaS plan that supports up to 10 users.

At $15 per user per month, it provides all the convenience of a hosted solution and adds important features on top of what the Community plan offers.

Pro users get advanced capabilities like:

• Cloud auto-scaling of compute, via which ClearML can automatically spin up cloud instances to run your jobs
• Built-in hyperparameter optimization
• Automated pipeline scheduling/triggers
• Custom dashboards for monitoring experiments

In terms of limits, the Pro plan increases your included usage. It offers ~120 GB of storage, and 1.2M API calls per month are covered at no extra charge. This means many teams won’t immediately incur additional fees. If you do go beyond those limits, you’ll pay the transparent overage rates as noted.

For most small-to-medium teams, ClearML’s Pro plan offers great value for the price, as long as extremely heavy experiment workloads are kept in check.

3. Scale Plan - Pay-as-You-Go (Custom Pricing)

The Scale plan is meant for organizations that need ClearML at a larger scale or with more stringent environment control. There’s no fixed price tag; ClearML will work out a custom quote based on your requirements.

Typically, with Scale, ClearML sets up the platform in your own cloud environment (a dedicated VPC), and you pay for what you use in terms of compute and storage. There are no per-user fees on this tier – you can usually have unlimited users. Instead, the cost is usage-based and negotiated in advance.

Scale includes all Pro features plus further enhancements needed by growing teams. For example, you get enterprise-level additions like advanced data management features, single sign-on (SSO) integration for user management, and priority support. What’s more, you might even get a private Slack channel with the ClearML team for fast assistance.

Essentially, it’s designed for mid-size companies or heavy ClearML users who want the convenience of a managed platform but deployed within their own cloud for data isolation. The usage-based model means if you ramp up your ML efforts, costs will ramp up too, but you’re also not paying for capacity you don’t use.

4. Enterprise Plan - Custom (VPC/On-Prem)

The Enterprise plan is ClearML’s top-tier offering for large enterprises or organizations with special compliance and security needs. Like Scale, pricing is custom and is negotiated usually via an annual contract.

Enterprise deployments can be in your private cloud or on-premises data center. This plan includes everything in Scale and adds even more enterprise-only benefits.

These include custom integrations like hooking ClearML into your company’s internal user authentication system (like LDAP or Active Directory), more advanced role-based access control for projects and teams, and support for external job schedulers or other infrastructure specific to your environment.

At this level, the investment is significant, but it ensures the platform fits into your enterprise environment with full support and customization.

Enterprise customers get ‘white-glove’ treatment: ClearML provides dedicated support personnel, custom SLAs for uptime and response, and professional services to assist with onboarding or integration.

In short, this plan is for when ML is mission-critical and you need ClearML tailored exactly to your organization. If you’re a small company, you might not need this tier, but it’s yours if you do.

Is ClearML Expensive?

ClearML’s pricing is fairly reasonable for most use cases. The open-source Community plan is free, and even the Pro plan (at $15 per user per month) is inexpensive compared to many other MLOps services. For light or moderate workloads, you might pay little to nothing at all.

Where costs can rise is under heavy workloads – if you log very large volumes of data or run countless experiments, the pay-as-you-go fees (for storage, etc.) will add up accordingly.

In those extreme cases, ClearML could cost more than a fixed-price solution, but you’re also deriving that much more value from it.

Overall, ClearML’s pricing scales with usage, and many teams find it a worthwhile trade-off given the platform’s breadth of features.

ZenML – An Alternative to ClearML

At ZenML, we offer an alternative approach for teams who want robust MLOps capabilities with straightforward pricing.

ZenML is also open-source and allows you to get started for free, with an optional managed service that keeps costs predictable and flat.

Here’s how ZenML compares on key features:

Workflow Orchestration

ZenML helps you automate and orchestrate your ML workflows (pipelines) across any environment. You can run pipeline steps locally, on cloud VMs, or on Kubernetes — ZenML will manage the execution across these environments. It ensures your pipeline runs are reproducible and gives you flexibility to use your preferred infrastructure.

from zenml import pipeline, step
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

@step
def ingest_data():
    return pd.read_csv("data/dataset.csv")

@step
def train_model(df):
    X, y = df.drop("target", axis=1), df["target"]
    model = RandomForestRegressor(n_estimators=100)
    model.fit(X, y)
    return model

@step
def evaluate_model(model, df):
    X, y = df.drop("target", axis=1), df["target"]
    rmse = mean_squared_error(y, model.predict(X)) ** 0.5
    print(f"RMSE: {rmse}")

@pipeline
def ml_pipeline():
    df = ingest_data()
    model = train_model(df)
    evaluate_model(model, df)

ml_pipeline()

Experiment Tracking

Every ZenML pipeline you run tracks parameters, metrics, and artifacts automatically, so you get experiment tracking out of the box. You can compare results in the ZenML dashboard (or even export to other tracking tools if needed).

This covers the same experiment tracking needs as ClearML. Because ZenML’s tracking is built into pipeline execution, everything recorded is automatically tied to a reproducible run.

Artifact and Dataset Versioning

ZenML automatically versions artifacts produced by pipeline steps. Whenever a pipeline step produces an output (data or model), ZenML stores it and keeps track of versions. You always know which data and model were used in each run.

ZenML’s artifact versioning is seamlessly integrated into the pipeline – you don’t have to manage dataset or file versions separately; it happens as part of running pipelines.

Creating artifacts in ZenML is pretty quick and straightforward:

from zenml import pipeline, step
import pandas as pd

@step
def create_data() -> pd.DataFrame:
    """Creates a dataframe that becomes an artifact."""
    return pd.DataFrame({
        "feature_1": [1, 2, 3],
        "feature_2": [4, 5, 6],
        "target": [10, 20, 30]
    })

Model Registry

The platform provides a way to register and manage models through its stack components.

After your pipeline trains a model, you can register that model in a repository for versioning and later deployment.

In ZenML, model registration is part of the normal workflow, so promoting a model from experiment to deployment is straightforward. Being open-source, ZenML also lets you choose where and how your models are stored, giving you full control.

What’s more, ZenML offers several capabilities that extend beyond ClearML’s core functionality:

• Smart Caching: If nothing changed in a step’s inputs or code since the last run, ZenML will skip that step and reuse its output, saving time and compute.
• Secret Management: Comes with a built-in secret management system for securely storing credentials (API keys, passwords, etc.) and injecting them into pipelines safely.
• Hyperparameter Tuning: The platform is capable of running hyperparameter search experiments as part of your pipelines, executing multiple runs with different parameters, and tracking each result.

In summary, ZenML provides similar capabilities to ClearML - workflow orchestration, experiment tracking, data and model management, etc.

The pricing philosophy for both ZenML and ClearML is similar; both platforms use flat pricing (not usage-based billing), so you know exactly what you’ll pay. This approach can often be more cost-effective for teams worried about unpredictable bills.

Wrapping Up

ClearML’s pricing can be very attractive for many teams. It offers a lot of functionality and the flexibility to start free and grow into paid plans as needed. Whether ClearML is ‘worth it’ comes down to your team’s size, budget, and how intensively you’ll use its features.

But remember, it’s not one-size-fits-all. If you foresee issues with ClearML’s pricing model (for example, you expect extremely high usage or you prefer fixed costs), remember that you have alternatives.

ZenML is one such alternative that we recommend considering – it provides similar end-to-end MLOps capabilities while keeping costs predictable and starting at $0.

In the end, the best platform is one that saves you time and fits your budget. Take advantage of free tiers to try ClearML and compare it with ZenML (or other tools) using your own projects. With that hands-on experience, you can confidently decide if ClearML is worth the investment.

If you think ClearML’s pricing isn’t justified, you should book a demo with ZenML to learn how we can cater to your business in a better and more efficient way.

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