Data Catalog Pricing: Understanding What You're Paying For in 2026

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by Emily Winks, Data governance expert at Atlan.Last Updated on: December 10th, 2025 | 15 min read

Quick answer: What is data catalog pricing?

Data catalog pricing refers to the cost structure for software platforms that help organizations discover, understand, and govern their data assets. Data catalog pricing varies widely and is influenced by factors such as the number of users, data volume, and the depth of features required. Beyond the base price, organizations should also account for implementation, training, and ongoing maintenance to understand the true total cost of ownership.
Key cost components to understand:

  • User licenses: Pricing tiers for viewers, contributors, stewards, and administrators
  • Data volume: Charges based on assets cataloged, metadata storage, or processing units
  • Feature packages: Core platform versus add-on modules for lineage, quality, or governance
  • Implementation scope: Professional services for setup, integration, and customization
  • Support levels: Standard versus premium support with dedicated account managers

Below: Core pricing factors, common pricing models, hidden costs, TCO evaluation framework, choosing the right pricing, modern pricing approaches.

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Understanding data catalog pricing: Product vs. software pricing models

Product and service pricing in data catalogs


Traditional catalog pricing often follows product-centric models where costs accumulate based on individual components. Legacy vendors may charge separately for each connector, creating complexity as organizations scale their data infrastructure. This approach can inadvertently discourage comprehensive data integration since each new system connection adds cost.

Software pricing in modern data catalogs


Modern data catalog pricing has evolved toward value-aligned models that tie costs to actual business outcomes rather than technical implementation details. Leading platforms now structure pricing around three key dimensions: the scale of data estate being governed, the number of active contributors managing and enriching data, and optional capabilities that address specific organizational needs.

Core pricing components that determine data catalog costs

1. Software licensing base price: Platform and data volume


The base license covers the core functionality of the data catalog and typically includes a defined amount of platform capacity. This usually comes with an initial set of users, a fixed number of data connectors, and a certain volume of tables or assets the catalog can index. Most vendors also include one production server and one or two non-production environments so teams can test changes or automations before deploying them to production.

Modern platforms use wide asset volume bands to avoid consumption-based complexity. Organizations typically won’t exceed their asset limit within a year of normal growth, and platforms continue operating seamlessly even if limits are temporarily exceeded, with right-sizing discussions happening at renewal rather than triggering immediate overage charges.

Asset Type

Typical Volume

Why It Matters

Database columns

75% of total assets

Most granular level requiring lineage and governance

BI fields

10% of total assets

Critical for downstream impact analysis

Tables

4% of total assets

Core organizational data structures

Data processes

<2% of total assets

Transformation and pipeline context

What counts as a cataloged asset: Everything with a profile page containing metadata, lineage, ownership, or governance attributes counts toward asset volume. A table with 20 columns represents 21 assets: the table itself plus each column with its own metadata profile.

2. Implementation and integration costs


Professional services for catalog implementation vary dramatically based on platform architecture. Legacy systems with complex on-premise requirements may need $50,000-$200,000 in consulting fees for setup, configuration, and integration. This includes installing software, establishing connectors, building governance workflows, and training teams.

Platforms designed for self-service deployment significantly reduce this burden. Organizations can launch in weeks rather than months by leveraging pre-built connectors, intuitive interfaces, and DIY setup procedures. This approach cuts professional services costs by 60-80% while maintaining implementation quality.

3. Ongoing costs and maintenance


Total cost of ownership extends beyond year-one implementation. Ongoing expenses include annual license renewals (often with 3-5% annual increases), support fees, infrastructure costs, and internal team time for maintenance and user support.

Support tier considerations:

Support Level

Typical Cost

What's Included

Standard

Included

Business hours access, regular updates

Advanced

10-15% of base ARR

24/7 access, faster SLAs, dedicated resources

Premium

15-25% of base ARR

Priority response, quarterly reviews, enhanced escalation

Internal maintenance requirements vary significantly by platform. Legacy catalogs requiring manual metadata curation may consume 10-20% of a data governance team member’s time ongoing. Modern platforms with automation features and minimal maintenance overhead reduce this to 5% or less.

Common pricing models explained

I. Subscription-based annual contracts


The most common approach uses annual or multi-year subscriptions with fixed licensing fees. Pricing tiers typically bundle user counts, data volumes, and feature sets into packages like “Starter,” “Professional,” and “Enterprise.” This model provides budget predictability but may lock you into capacity you don’t fully utilize.

Annual contracts often include 10-20% discounts compared to monthly billing, with deeper discounts for 3-year commitments. However, scaling up mid-contract usually requires renegotiation and can trigger retroactive pricing adjustments.

II. Asset volume-based pricing


Progressive vendors structure pricing around the volume of data assets cataloged rather than the number of connectors or technical systems. This aligns costs with governance value delivered: managing lineage, access control, and policy enforcement across millions of assets directly correlates with platform value.

III. Usage-based consumption models


Some cloud-native data catalogs use consumption-based pricing tied to metrics such as monthly active users, API calls, metadata queries, or data processing units. This pay-as-you-go model aligns cost with actual usage but can introduce unpredictability. Many modern platforms now include unlimited guest access to avoid penalizing broad data discovery, charging only for users who contribute value through metadata enrichment and governance activities rather than those who simply consume information.

Most platforms differentiate pricing by user role and activity level:

User Type

Access Level

Pricing Approach

Guest/Viewer

Read-only discovery and search

Often free to encourage democratization

Member/Contributor

Edit metadata, create documentation

Per-seat pricing for active contributors

Admin

Platform configuration, governance setup

Premium per-seat pricing for management

Volume discounting

Enterprise platforms typically include volume discounts that activate at scale. Asset pricing may decrease for organizations managing 10M+ assets, 50M+ assets, or 100M+ assets. Similarly, per-user costs often decline beyond 100 seats, 500 seats, or 1,000 seats.

IV. Hybrid tiered pricing


Many enterprise data catalog tools combine elements of both approaches. A base subscription covers platform access and a minimum user count, with additional charges for users beyond thresholds, premium features, or consumption that exceeds included allowances. This balances predictability with flexibility.

Understanding the included baseline and overage pricing prevents unexpected cost escalation. Ask vendors for detailed scenarios showing how your projected usage maps to their pricing tiers.

What are the hidden costs of data catalogs beyond the license fee

I. Training and enablement


User adoption determines data catalog ROI, making training essential. Organizations must budget for developing documentation, conducting onboarding sessions, and supporting users. Vendors offering comprehensive training programs may charge $10,000-$50,000 annually for workshops, certifications, and dedicated trainers.

Intuitive interfaces reduce training requirements, but even simple platforms require 40-80 hours of internal time for documentation and training coordination. Complex platforms with steep learning curves multiply this investment significantly.

II. Custom connector development


While leading platforms include 50+ pre-built connectors for common systems at no additional cost, legacy or niche systems may require custom development. Partner-built connectors for legacy systems typically start at $2,000 per month. Custom-built connectors for proprietary in-house systems follow similar pricing.

Organizations with extensive legacy infrastructure should clarify connector costs early. Some vendors charge per connector regardless of the asset volume those connectors manage, while others include unlimited connector usage as long as organizations stay within their overall asset volume limits.

III. Infrastructure and operational overhead


Cloud-based catalogs eliminate most infrastructure costs, but organizations must consider associated expenses like dedicated network connections, private endpoints, or additional cloud resources for high-security deployments.’

Data catalog total cost of ownership evaluation framework

Calculate comprehensive year-one costs


Start with platform licensing for your projected user count and data volume. Add implementation costs whether external consulting or internal team time valued at loaded salary rates. Include training programs and any required infrastructure.

Organizations frequently underestimate internal time investment. If your data engineering team dedicates 500 hours to implementation, that represents $50,000-$100,000 in opportunity cost at typical engineering salaries.

Project multi-year total cost


Beyond year one, annual costs typically include base license renewals with modest annual increases, ongoing support fees, infrastructure expenses, and continuous internal maintenance time. Platforms with strong automation and minimal maintenance requirements show significantly lower total costs over 3-5 years.

TCO comparison considerations:

Cost Category

Year 1

Years 2-3

Reduction Strategies

Platform licensing

Base fee

Annual increases (3-5%)

Multi-year commitments

Implementation

High (setup, integration)

Minimal

DIY platforms, pre-built connectors

Training

Moderate (onboarding)

Low (new hires only)

Intuitive interfaces, self-service

Maintenance

Variable by platform

Ongoing (5-20% FTE)

Automation features, active metadata

Measure value delivered against costs


Balance total costs against quantifiable benefits. Calculate time savings from faster data discovery, costs avoided through better quality, reduced compliance risk, and prevention of decisions based on incorrect data.

Research indicates data professionals spend approximately 50% of their time searching for and validating data. Catalogs reducing this to 20-30% deliver substantial productivity gains that often exceed licensing costs within 12-18 months. Organizations achieving 40% time savings on governance activities or reducing impact analysis from weeks to hours realize clear ROI.

How to choose the right data catalog pricing model

Assess your organizational requirements


Begin by inventorying your data estate: count data sources, tables and columns requiring cataloging, BI reports, and data pipelines. Identify potential users across data engineers, analysts, scientists, and business stakeholders who would benefit from catalog access.

Understanding your scale prevents vendors from underscoping proposals that lead to expensive mid-contract adjustments. Document growth projections for 2-3 years to evaluate how pricing scales with your data estate.

Define must-have versus nice-to-have capabilities


Prioritize capabilities based on your top use cases. If automated column-level lineage is critical for compliance, ensure it’s included in base pricing rather than a premium add-on. If AI-powered recommendations are interesting but not essential, consider whether premium tiers delivering them justify cost increases.

Evaluation framework for add-on modules:

Module Type

When Essential

When Optional

Advanced compliance

Regulated industries, strict privacy requirements

General business governance

Data products marketplace

Large distributed teams, data mesh architecture

Centralized data teams

Query/exploration tools

Self-service analytics requirements

Existing BI tool investments

Advanced support

Mission-critical deployments, 24/7 operations

Standard business hours needs

Request detailed scenario-based pricing


Ask vendors for pricing under multiple scenarios: your current state, projected state in 12 months, and projected state in 24 months. Request breakdowns showing platform fees, per-user costs, data volume charges, and feature add-ons separately.

Vendors often present optimistic “starting at” pricing that doesn’t reflect actual organizational needs. Scenario-based quotes reveal how costs evolve during growth phases, preventing budget surprises.

Evaluate total cost of ownership, not just license fees


Compare vendors on comprehensive 3-year TCO including implementation, training, ongoing support, and estimated internal maintenance burden. Platforms with faster deployment, higher adoption rates, and lower maintenance requirements may deliver better value despite similar or slightly higher license fees.

Modern pricing approaches that reduce TCO:

Value-aligned pricing models are transforming data catalog economics. Rather than charging per connector or imposing restrictive user limits, forward-thinking vendors structure costs around actual value delivered: the volume of assets under governance and the number of users actively creating metadata and managing data products.

This approach eliminates several traditional cost drivers. When primary connectors are included at no additional cost with unlimited usage, organizations connect their entire data ecosystem without worrying about per-connector fees accumulating. When guest users who simply discover and consume data access the platform free, organizations achieve true data democratization without billing friction.

The impact on total cost of ownership is substantial. Organizations implementing catalogs with modern pricing achieve:

  • 60-80% lower implementation costs through DIY setup and pre-built connectors
  • 40-60% lower total cost of ownership over 3 years compared to legacy alternatives
  • 3-4x higher adoption rates due to frictionless access for all users
  • Launch timeframes of 6 weeks versus 6 months for traditional implementations

Customer evidence validates this approach. Organizations managing over 1 million data assets with small governance teams of 5-10 people, achieving 40% time savings on governance activities, and deploying in under 6 weeks demonstrate that value-aligned pricing delivers measurable business outcomes while reducing costs.

Real stories from real customers: Pricing investments that paid off

Porto: 40% governance cost reduction through efficient scaling

“A major insurance and banking organization launched their data catalog in under 6 weeks and achieved 40% time savings on governance activities. Their team of just 5 people now manages over 1 million data assets effectively, with plans to reach 1,000 users by year-end. What previously took weeks now takes hours, freeing their team to focus on strategic initiatives rather than manual cataloging work.”

Data Team

Porto

🎧 Listen to podcast: Porto: 40% governance cost reduction through efficient scaling

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Takealot: $6,000 annual savings plus 50% faster incident response

“South Africa's leading eCommerce platform used automated lineage and asset popularity metrics to identify unused cloud resources, generating $6,000 in annual savings. More significantly, root cause analysis that previously took hours now completes in minutes, accelerating incident response by 50% and reducing business impact from data issues.”

Data team

Takealot

🎧 Listen to podcast: Takealot's $6,000 annual savings plus 50% faster incident response

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Moving forward with data catalog pricing decisions

Understanding data catalog pricing requires looking beyond headline license fees to evaluate total cost of ownership, hidden implementation costs, and ongoing maintenance requirements. Vendors delivering best value combine transparent pricing with deployment models that minimize services dependency and accelerate adoption.

Start by documenting your current data estate and user needs, then request scenario-based pricing showing how costs scale with growth. Modern platforms with self-service setup, flexible user models, and value-aligned pricing typically deliver 40-60% lower TCO while achieving adoption rates 3-4x higher than traditional alternatives.

Explore transparent, personalized,value-aligned data catalog pricing.

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FAQs about data catalog pricing

1. What is the average cost of a data catalog?


Data catalog costs vary widely from $10,000-$15,000 annually for small teams using basic cloud-native tools to $200,000-$500,000 for enterprise platforms supporting thousands of users. Mid-market organizations typically invest $50,000-$150,000 annually including licensing, implementation, and support. Total cost of ownership often runs 40-60% higher than base licensing when including professional services, training, and internal maintenance costs.

2. How do modern data catalogs avoid per-connector pricing?


Leading platforms include 50+ primary connectors at no additional cost, recognizing that a connector managing 1,000 assets delivers different value than one managing 1,000,000 assets. Instead of charging per connector, modern vendors align pricing with the total volume of assets cataloged across all systems. This approach encourages comprehensive data integration rather than creating financial disincentives to connect additional systems.

3. What’s included in asset-based pricing?


Asset-based pricing counts everything with a profile page in the catalog: database columns, tables, BI fields, dashboards, data pipelines, and business glossary terms. Columns typically represent 75% of total assets since each has its own profile with metadata, lineage, and governance attributes. A table with 20 columns counts as 21 assets total. This model aligns costs with governance value delivered rather than technical implementation details.

4. Why do some platforms offer unlimited guest users?


Modern platforms recognize that organizations already pay for governing and enriching data assets through platform and contributor licensing. Once data is trustworthy and discoverable, democratizing access to data consumers shouldn’t create additional billing friction. Unlimited guest access for read-only users encourages adoption across entire organizations, delivering on the promise of data democratization without penalizing success.

5. How can I estimate my cataloged data estate?


Start by counting columns in your data warehouse using SQL queries against information_schema tables. Multiply this column count by 1.33 (dividing by 75%) to estimate total assets, since columns typically represent 75% of all cataloged assets. Alternatively, if you know your BI field count, divide by 10% to estimate total assets. For organizations with technical or legacy catalogs already in place, simply sum all existing assets including columns, tables, fields, and processes.

6. What’s the difference between data catalog platform editions?


Data catalog platforms typically offer multiple editions based on enterprise requirements rather than core functionality. Standard editions provide essential cataloging, lineage, and governance features for small to mid-sized deployments. Enterprise editions add capabilities like extended audit logs, increased API limits, and administrative sub-roles for larger organizations. Business-critical editions include advanced security features like additional instances, extended compliance logging, transit gateways, and premium support SLAs for regulated industries with stringent requirements.

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