Choosing a data catalog in 2025 isn’t just about finding the lowest price—it’s about understanding the total cost of ownership. This guide breaks down the three cost drivers behind every data catalog—base platform fees, customization needs, and implementation effort—and compares the top 10 tools across these dimensions. Whether you're a cloud-native startup or a large enterprise, this practical comparison will help you align cost with capability and avoid costly surprises.
Data catalogs have become a critical part of the modern data stack. They help teams find, trust, and use data efficiently, a must-have for any organization investing in governance, self-service analytics, or AI readiness. But as adoption rises, so does confusion, especially around data catalog pricing.
At first glance, pricing seems like a logical way to compare tools. But the way vendors package and present their costs varies wildly. Some appear affordable upfront but require costly engineering hours to implement. Others offer premium packages with features you may not even need. That’s why understanding the true cost of a data catalog is more important than ever.
In this blog, we’ll help you move past surface-level pricing comparisons and get clarity on what you’re really paying for.
A common misconception among buyers is that lower-priced data catalogs automatically offer better value. It’s easy to gravitate toward the most affordable-looking option, especially in a crowded market with inconsistent pricing pages and tiered feature sets. But here’s the truth: license cost rarely reflects total cost.
The reality is that pricing structures can hide a lot. Some catalogs introduce usage-based penalties that scale aggressively. Others seem affordable but require weeks (or months) of customization to fit your business.
To properly assess data catalog pricing, you need to look beyond the surface and evaluate three key cost drivers:
Base platform fees
Customization costs
Implementation & ongoing support
Next, we’ll break down each of these factors so you can see how different vendors stack up—well before you ever get to the negotiation table.
While vendors often highlight license fees upfront, data catalog pricing is far more nuanced beneath the surface. The actual cost of a data catalog spans beyond what’s listed in a pricing sheet—it’s embedded in how the platform scales, how much effort customization requires, and what kind of support model it runs on.
In 2025, enterprise buyers are beginning to evaluate cost in terms of outcomes, not just contracts. Based on industry comparisons, the total cost of ownership typically hinges on three
This includes the core software license, but pricing structures vary significantly across vendors. Some offer flat-fee licenses that are easy to predict, while others apply usage-based pricing models tied to compute, number of users, or volume of data processed.
What’s often overlooked is that “base” doesn’t always mean “complete.” Many data catalogs set limits on the number of data sources, metadata assets, or user roles included in the entry-level plan. Once those thresholds are crossed, hidden paywalls kick in—leading to unexpected costs.
There’s also a trade-off between predictability and scalability. Flat models offer stability but may cap features unless upgraded. Usage-based models are more flexible but introduce variability, especially for growing data teams where user count or data activity spikes frequently.
Some vendors combine both—charging a platform fee and layering user-based tiers on top, with steeper pricing for admins or stewards. This makes budgeting difficult unless buyers have a strong grasp of how access and usage will evolve post-deployment.
Every organization has its own metadata standards, business logic, and governance workflows. How easily a catalog adapts to these needs defines the real implementation effort—and cost.
Some platforms are built for self-service configurability, allowing data teams to model their environment using no-code interfaces and flexible templates. Others rely heavily on vendor services or engineering teams to rewire the platform for custom metadata types, workflows, or access policies.
This is where upfront cost savings can quickly reverse. A catalog that looks affordable at first may demand months of technical work to make it usable in context. On the contrary, platforms with built-in configurability and extensibility reduce time-to-value and avoid long-term dependency on vendor support.
Support costs come in two forms: initial implementation and ongoing maintenance.
Some vendors include onboarding, training, and enablement in their base pricing. Others treat implementation as a professional services engagement, with costs that rise depending on deployment complexity and team involvement.
Beyond go-live, support tiers often range from basic ticketing to consultative partnerships. This includes response SLAs, technical troubleshooting, and strategic guidance on evolving governance needs. While not always visible in pricing sheets, the quality of ongoing support often determines whether the tool becomes a success story—or shelfware.
In fast-moving data environments, delays in support or poor onboarding can derail adoption, especially when business users are involved in governance workflows. Evaluating the vendor’s commitment to success beyond implementation is a crucial part of pricing analysis.
The table below outlines how leading data catalogs are priced in 2025, along with usability insights to help evaluate trade-offs:
**The pricing information presented in this blog is based on publicly available data, vendor websites, and insights gathered from industry discussions. Actual pricing may vary based on enterprise agreements, usage tiers, and custom implementations. We recommend reaching out to vendors directly for the most up-to-date pricing details.
Now that we've outlined the key pricing drivers, let's examine how different types of data catalogs stack up across real-world pricing, customization, and support requirements.
These platforms are designed for large-scale enterprises that need advanced governance, security, and compliance features. However, high cost and complexity often limit their appeal for smaller or mid-sized teams.
Base Pricing: High – Enterprise subscription pricing typically starts around ~$198K/year.
Customization Cost: High – Tailored implementations require expert services and often involve partner ecosystems.
Support Cost: High – Premium support tiers are common, scaling costs based on SLAs and enterprise agreements.
Base Pricing: High – Known for high license fees with annual contracts starting ~$170K and reaching ~$500K+.
Customization Cost: High – Requires significant configuration and professional service involvement for complex use cases.
Support Cost: High – Enterprise-grade support is priced separately and scales with organization size.
Base Pricing: High – Usage-based pricing (IPUs) quickly scales with data volumes; average contracts range from ~$129K to ~$500K.
Customization Cost: High – Customization typically demands certified professionals and integration with a broader Informatica stack.
Support Cost: High – Premium support is available but comes with additional cost tiers based on SLAs.
Ideal for mid-sized organizations that need robust features without enterprise-level overhead.
Base Pricing: Low – Transparent pricing starts at ~$15.6K -$90K/year for essential tier; enterprise pricing is still competitive.
Customization Cost: Moderate – Offers flexibility via professional services, but doesn't demand heavy technical investments.
Support Cost: Moderate – Included in plans with dedicated SLAs available at enterprise level.
Base Pricing: Moderate – Starter plans begin at ~$6K/year; enterprise features cost more.
Customization Cost: Moderate – APIs and extensibility exist, but onboarding and workflows require guided setup.
Support Cost: Moderate – Support is included in most plans, with additional options for enterprise SLAs.
Base Pricing: High – Starting price of ~$90K/year with enterprise plans crossing ~$180K.
Customization Cost: Moderate – Platform supports integration, but advanced use cases may need consulting.
Support Cost: Moderate – Enterprise support is robust but adds to the overall contract value.
Pay-as-you-go models work well for cloud-native or modular data environments, but can introduce unpredictability in pricing.
Base Pricing: Moderate – Pay-as-you-go pricing starts low but escalates based on asset count and scanning jobs.
Customization Cost: High – Custom integrations and policy enforcement often require broader Azure engineering involvement.
Support Cost: High – Enterprise-grade support is tied to Azure plans and adds substantial cost at higher tiers.
Base Pricing: Low – Free tier includes 1M objects; costs ~$1 per 100K additional objects.
Customization Cost: Moderate – Requires configuration with AWS ecosystem but offers scalable tools out-of-the-box.
Support Cost: Low – Basic support is free; costs rise only with optional AWS support plans.
Open-source catalogs appeal to teams that want full control and are willing to manage infrastructure.
Base Pricing: Low – Typical infra cost ranges from ~$1.2K–$6K/year when self-hosted.
Customization Cost: High – Requires in-house engineering to configure, deploy, and extend for enterprise needs.
Support Cost: Moderate – Community-driven support is free; paid support via managed offerings incurs moderate cost.
Base Pricing: Low – Open-source, but infra stack includes complex dependencies (Kafka, Solr, HBase).
Customization Cost: High – Heavy engineering effort required to tailor and scale, especially in production setups.
Support Cost: High – No formal support unless bundled with Cloudera or managed services.
Buyers today must look beyond the license fee. The real Total Cost of Ownership (TCO) lies in support quality, customization flexibility, and how easily the tool scales with governance maturity. Here's what the pricing breakdown reveals:
Alation, Collibra, and Informatica charge high base license fees and carry heavy ongoing costs for support and customization. These tools are built for large, mature organizations with dedicated resources. For most teams, they represent a long-term financial commitment, often requiring external services for both setup and ongoing operations.
Tools like OvalEdge, Atlan, and data.world strike a better balance between affordability and enterprise-readiness. OvalEdge, in particular, stands out with its low base pricing and moderate support/customization costs, offering meaningful configurability without a reliance on professional services.
Microsoft Purview and AWS Glue may seem affordable upfront. But costs scale quickly with asset count, compute usage, or additional capabilities. Microsoft Purview, in particular, introduces high customization and support costs despite its modular entry point. These models work best for organizations that can tightly control scope and usage from the start.
While OpenMetadata and Apache Atlas eliminate license costs, they shift the burden to internal teams or third-party partners. High customization complexity and variable support needs make them better suited for organizations with strong in-house engineering talent or existing DevOps infrastructure.
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Buyers often focus on license fees and miss the recurring costs of tailoring workflows or getting timely support. In several tools, “moderate” to “high” customization costs mean even basic configuration changes require vendor intervention or service credits. This adds up fast, especially in fast-evolving data environments.
Choosing the right data catalog isn't just about comparing numbers—it's about understanding the trade-offs behind those numbers. What looks affordable on paper may carry hidden costs in setup, support, or long-term scalability. And what seems expensive might save months of engineering time if it fits your needs out-of-the-box.
As the data ecosystem grows more complex, buyers need more than pricing tables—they need context. The true value of a catalog lies in how well it aligns with your team’s technical maturity, governance goals, and future roadmap.
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This guide is a starting point. Use it to ask better questions, spot potential pitfalls, and weigh not just what you’ll pay—but what you’ll get. Because in 2025, managing data complexity isn’t cheap. But making the wrong choice is even more expensive.