WandB Pricing Guide: How Much Does the Platform Cost?

WandB is an MLOps platform widely recognized for its robust capabilities in machine learning experiment tracking, model management, and debugging AI applications. When evaluating MLOps solutions, understanding the pricing structure is key.

This WandB pricing guide provides a breakdown of the platform’s pricing, features, and the value it delivers.

At the end of this Pricing guide, we also introduce an alternative to WandB, ZenML (disclaimer: this is our product), which is an affordable replacement. We also explain how you can replace several MLOps processes with our tool and use WandB inside ZenML to get the best experience when running ML pipelines.

TL;DR

Here’s a quick summary of WandB’s pricing tiers and whether they make sense for your team:

WandB is worth inventing when:

✅ You need a polished, hosted ML tracking platform that logs experiments and metrics efficiently.

✅ Your team values built-in collaboration and UI, with managed infrastructure and support.

✅ Security/compliance matter, and you’re ready to pay for Enterprise features like SSO and audit logs.

But WandB may not be ideal if:

❌ Your team is small or budget-conscious. Even at $50 per user per month, costs add up. For example, a 5-person team pays ~$3,000 per year for Pro seats, which can be high compared to other alternatives on the market.

❌ You prefer open-source or local solutions. WandB’s free Personal plan is strictly for non-commercial use, and managed plans are SaaS.

❌ You need broader MLOps (like orchestration, data versioning, hyperparameter tuning). WandB handles tracking but often requires integrating extra tools, raising the overall cost.

WandB Pricing Plans Overview

WandB uses a tiered pricing structure that scales with team size and usage requirements. The platform distinguishes between cloud-hosted managed services and self-hosted deployments, with different pricing models for each approach.

WandB uses tracked hours as a primary billing metric for cloud services, where one tracked hour equals one hour of wall-clock time during model training. This means if your model takes 8 hours to train, you consume 8 tracked hours regardless of hardware specifications.

The pricing model avoids per-run fees, giving you predictable costs for experiment management, but usage-based billing can become expensive for teams running extensive parallel training jobs or long-running experiments.

Another way to buy managed WandB is via AWS, Google Cloud, and Azure marketplaces. We will cover these in detail in the latter half of this article. First, let's understand some factors that you should consider before investing in WandB.

WandB Pricing Factors to Consider

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

1. Team Size and Seats

WandB’s paid plans charge per user (seat). The Personal plan allows only 1 user, and each Pro user pays a monthly fee (starting at $50 per month, which is per seat). If your team is large or growing, those per-user fees can add up quickly. On the flip side, WandB does not charge extra for guests.

2. Feature Needs vs. Toolchain

WandB excels at experiment tracking and visualization, but it doesn’t natively include things like pipeline orchestration, hyperparameter sweeps (beyond basic WandB ‘Sweeps’), or advanced dataset versioning.

If your workflow demands those features, you’ll need additional tools. That can increase total cost and complexity. For example, using Prefect or Kubeflow alongside WandB means paying for multiple platforms.

3. Data Storage and Usage Limits

Consider how much data and compute you will log. WandB’s Free cloud tier has limits; the free plan has 5 GB of storage, after which extra storage costs ~$0.03 per GB per month.

Even paid tiers can incur overage charges for extremely large runs or storage (especially if using the AWS, GCP, or Azure cloud services). If you log massive datasets or long training runs, these fees will significantly increase your usage charges.

4. Security and Compliance

Advanced security features for WandB come only in the Enterprise plan. The free and Pro plans lack SSO, audit logs, and some compliance certifications. If you work in regulated industries like healthcare or finance, check that WandB Enterprise supports your requirements (the Enterprise plan offers HIPAA compliance, SOC2, and SAML SSO).

All Plans Included That WandB Offers (Open Source + Paid)

WandB currently offers 1 open-source option and 3 paid plans, plus a free academic license. The open-source version provides full experiment tracking capabilities but requires self-hosting, while paid plans offer managed cloud services with varying levels of features and support.

WandB Cloud Hosted Plans

These plans provide a managed service experience, with WandB handling the underlying infrastructure, offering convenience and reduced operational overhead.

Free Plan - Open Source

WandB’s free tier is available on the cloud. It gives you experiment tracking, metrics, and basic artifact logging at no cost. You can log parameters, visualize training curves, and compare runs using WandB’s hosted UI. The free plan offers unlimited projects and teams (for personal use) and community support.

It is ideal for individual ML developers and small projects. But you don’t get advanced features like private projects, SSO, or email support (Academic users can apply for an extended free license with larger storage).

Here’s what you get with the free plan:

• Basics: Up to 5 Model seats, tracked hours (limited to 5 GB of storage), and Weave data ingestion of 1 GB per month.
• Models: Lightweight experiment tracking, centralized model registry, hyperparameter optimization, and automated ML workflows.
• Core Features: Dataset and model versioning, interactive data visualization, and collaborative dashboards.
• Access Control and Permissions: Public and private projects (no other access controls included).
• Security and Compliance: SOC 2 Type II.
• Support: Email only.

Pro Plan

The Pro plan is a paid subscription that starts at $50 per month. It includes everything in the Free plan, plus team and enterprise capabilities. Key additions are CI/CD integrations, Slack/email alerts on run status, unlimited collaborators within your organization, role-based access controls, and priority email/chat support.

In short, Pro is for professional ML teams that need collaborative features and responsive support. Pricing scales with your number of users.

Here’s what you get with the Pro plan:

• Basics: Up to 10 Model seats, 500 tracked hours per month (additional hours billed at $1 per hour), 100 GB per month storage (additional storage $0.03 per GB), and Weave data ingestion of 1.5 GB per month.
• Models: Everything from the free plan, plus access to workflow automations.
• Core Features: Dataset and model versioning, interactive data visualization, and collaborative dashboards.
• Access Control and Permissions: Public and private projects, team-based access controls, and service accounts.
• Security and Compliance: SOC 2 Type II and customizable data retention.
• Support: Email only, with first response time from 4 to 24 hours, depending on severity.

Enterprise Plan

The Enterprise tier is custom-priced (contact the WandB sales team to get the pricing).

It bundles all Pro features and adds enterprise-grade options: single-tenant deployment (in the cloud region of your choice), HIPAA-compliant setups, secure private VPC connectivity, customer-managed encryption keys, SSO/SAML, and several other features that you can take a look at in the list below:

• Basics: Custom number of Model seats with unlimited tracked hours, customizable storage, and weave data ingestion, along with a dedicated, scalable deployment.
• Models: All features included - same as in Pro plan.
• Core Features: Dataset and model versioning, interactive data visualization, and collaborative dashboards.
• Access Control and Permissions: Public and private projects, team-based access controls, project-level access controls, custom roles, and service accounts.
• Security and Compliance: SOC 2 Type II, customizable data retention, single sign-on, audit logs, bring your own bucket, and HIPAA compliance.
• Support: Email and chat support with 1 to 4 hours of first response time. You also get a dedicated Slack/MS Teams support channel and a dedicated AI solution engineer.

WandB Privately Hosted Plans

These options let you deploy and manage WandB on your own infrastructure, offering maximum control over data and enhanced privacy.

Personal - Free Plan

WandB’s Personal plan lets you run an on-premises WandB server locally, at no license cost. It provides 1 user seat for personal or academic use only.

You can pip install wandb and start a local server to log experiments and artifacts. The features are basic: 1 user seat, experiment tracking, a local artifact registry, and lineage visualization. There is no support for multiple users or teams, and corporate usage is explicitly prohibited.

However, it’s fully free for allowed use cases and useful if you want WandB’s capabilities without cloud dependency.

Advanced Enterprise

For on-prem enterprise needs, WandB’s Advanced Enterprise plan is also custom-priced. It includes the same local deployment model as Personal, but with full enterprise features.

In addition to the Personal plan’s tracking and one-seat, it adds all the Enterprise-level options: multiple users, flexible deployment (e.g., Docker/Kubernetes), HIPAA compliance, private networking, SAML/SSO, audit logs, machine-to-machine auth, and 24/7 dedicated support.

Essentially, it’s the complete WandB stack you host on your own servers. As a custom offering, pricing is aligned with your scale and requirements.

WandB on Marketplaces

You can also access WandB via cloud marketplaces. Cloud marketplaces are increasingly serving as central procurement hubs for enterprise software. They offer simplified billing, consolidated spending, and often pre-negotiated terms or the ability to apply existing cloud credits.

1. AWS Marketplace

WandB is available on the AWS Marketplace, offering a contract-based pricing model. An ‘Annual Single User License for WandB Models’ is priced at $4,800 for a 12-month period, while an ‘Annual Commitment for WandB Weave, 10GB’ costs $25,000 for 12 months.

Additional usage costs, like storage overage, are billed at $0.001 per unit. With AWS, you get access to WandB with your existing billing accounts, which can be particularly advantageous for leveraging pre-existing AWS enterprise discounts or committed spend agreements.

2. Google Cloud Marketplace

WandB is recognized as a Google Cloud Partner Advantage offering that helps you to make a purchase directly through the Google Cloud Platform.

This integration allows you to capitalize on Google's marketplace incentives, including the application of committed-use discounts or promotional credits.

It also ensures seamless compatibility with Google Cloud services and simplifies deployment, like orchestrating the WandB control plane on Google Kubernetes Engine (GKE).

WandB on Google Cloud is designed to accelerate AI development, supporting the evaluation and monitoring of Generative AI applications and integrating with Vertex AI for robust model training and fine-tuning.

The pricing for the plans is as follows:

• Annual Single User License for WandB Models: $400 per month + usage fee
• Annual Commitment for WandB Weave 10GB: $2,100 per month + usage fee

3. Azure Marketplace

WandB is also listed on the Azure Marketplace, enabling Microsoft Azure enterprise customers to acquire the platform through their existing Azure agreements.

This streamlines the procurement process, allowing you to offset WandB costs against their Azure consumption commitments.

WandB on Azure provides a suite of tools for building AI applications and models with confidence, integrating with Azure OpenAI and Azure Machine Learning services.

The pricing structure of WandB on Azure is exactly the same as you saw for the AWS marketplace.

Is WandB Expensive?

Whether WandB is expensive depends on your perspective and needs. The free plan costs nothing, so individuals benefit heavily. The Pro plan at $50 per user per month is a significant investment for larger teams – a 10-person team pays around $6,000 per year.

For a small startup, that might seem steep when compared to free (open-source) tools like MLflow or ZenML.

However, WandB’s hosted solution eliminates the engineering effort of building your own tracking system. Many enterprises justify the cost as WandB’s polished UI and reliability can speed up development and collaboration.

By comparison, the Advanced Enterprise (on-prem) plan can be very expensive (high five to six figures annually) since it covers all enterprise requirements. However, for large organizations, this cost is offset by the ROI of having 50+ data scientists easily share experiments and meet compliance.

In short, WandB’s per-user fees are on the higher side, but may be reasonable for teams that need its convenience and features. If you’re a smaller team with limited funds, you might find WandB’s paid plans hard to justify.

ZenML – An Affordable Alternative to WandB

For teams concerned about WandB’s cost or scope, ZenML offers a compelling free open-source and a paid alternative.

ZenML is an MLOps framework that provides end-to-end pipeline orchestration, experiment tracking, model management, and deployment capabilities. Unlike WandB's usage-based pricing, ZenML operates on a different philosophy: provide powerful MLOps tools without vendor lock-in or escalating costs based on your ML workload intensity.

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

Feature 1. Experiment Tracking

ZenML automatically logs metrics and parameters for each pipeline run. Its experiment tracking is built-in, or optionally, you can integrate popular trackers like MLflow or even WandB to track and visualize your pipeline runs.

📚 Read more about ZenML + WandB integration.

When you run a ZenML pipeline, it records each training run’s results and metadata. In fact, ZenML links experiments to pipeline runs and stores all related metrics and artifacts, providing a reproducible record. This happens with no additional license fee – it’s part of the framework.

Unlike WandB's tracked hours model, ZenML's experiment tracking doesn't charge based on training time, making it ideal for teams running extensive hyperparameter sweeps or distributed training jobs.

Feature 2. Artifact and Dataset Versioning

Every output (artifact) from a ZenML step is automatically saved and versioned. The ZenML pipeline engine creates a complete data lineage for you.

This means your datasets, preprocessed data, model checkpoints, etc., are all stored with version control and lineage tracking. You can easily retrieve previous artifacts from past runs and see exactly which run produced them.

Creating artifacts with ZenML just takes a few lines of code:

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]
    })

Whereas with WandB, you might need to manage storage quotas or use external versioning tools, which is time-consuming and utilizes extra resources, increasing your overall pricing.

Feature 3. Model Registry

ZenML includes a model registry in its stack. You can register models, tag versions, and attach metadata. The Model Control Plane lets you register and version models directly as part of your pipelines.

In fact, the platform’s model management is designed so that by properly registering, versioning, linking artifacts, and tracking metadata, you can create a transparent and reproducible workflow.

All of this is available in the open-source core (with a free Python SDK); a managed Pro version adds a dashboard, but even the OSS edition handles full model lifecycle management without extra cost.

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

• Unlike WandB, which focuses primarily on experiment tracking, ZenML provides complete pipeline orchestration capabilities, integrating with tools like Apache Airflow, Kubeflow, and Kubernetes for production-grade workflow management.
• ZenML automatically caches pipeline steps and artifacts, reducing computational costs and development time by avoiding redundant processing.
• The platform includes secure secret management for API keys, database credentials, and other sensitive information needed for ML pipelines.
• ZenML ensures complete reproducibility by tracking code versions, dependencies, infrastructure configurations, and data versions for every pipeline run.
• ZenML's modular architecture allows teams to choose best-of-breed tools for each component (orchestrator, artifact store, container registry) rather than being constrained by a single vendor's ecosystem.

Is WandB Worth Your Investment or are There Better Alternatives on the Market?

WandB offers flexible pricing tiers – from a free plan to enterprise contracts to suit different team sizes and needs. The platform has a decent cloud-hosted service that is robust and feature-rich, but the costs (especially for multiple users or enterprise security) can add up. We also found the free plan to be somewhat limited.

Also, the platform's usage-based billing can become expensive for compute-intensive workloads, and the per-seat costs may not align with all team structures and budgets.

ZenML is comparatively more cost-effective with no functionality sacrifice to be made. Its open-source foundation, comprehensive MLOps capabilities, and freedom from usage-based pricing make it particularly attractive for teams prioritizing flexibility and cost control.

The choice between WandB and alternatives like ZenML ultimately depends on your team's specific requirements: whether you prioritize managed services versus infrastructure control, collaborative features versus cost optimization, and vendor-supported tools versus open-source flexibility. Both platforms can support successful ML operations, but they serve different organizational philosophies and financial constraints.

Want to know what ZenML has in store for you? Sign up for a 14-day free trial and see if it's the right WandB alternative for you and your team.

📚 Relevant Reading:

• Prefect Pricing Guide
• Outerbounds Pricing Guide
• MLflow vs Weights & Biases