Dataiku vs Databricks: Which One’s Better? (In 2025)

Quick Answer: Dataiku costs $26,000+/year for collaborative data science. Databricks costs $0.15-0.55/DBU plus cloud infrastructure for big data processing. Most business teams need neither. They need simple data preparation tools, like Mammoth, that start at only $19/month.

You’re comparing these platforms because someone said you need an “enterprise data platform.” Here’s the reality: they solve completely different problems, and there’s a good chance neither fits your actual requirements.

Should I Choose Dataiku or Databricks?

Choose Dataiku if:

• You need collaborative data science workflows
• You have $50,000+ annual budget
• Governance/compliance is critical
• You have mixed technical teams

Choose Databricks if:

• You process 100GB+ datasets daily
• You have dedicated data engineers
• Performance is your top priority
• You’re comfortable with variable costs

Consider simpler alternatives if:

• You primarily need data cleaning and automation
• Your team is mostly business users
• You want predictable costs under $25,000/year
• You need results in weeks, not months

At-a-Glance Platform Comparison

What Is Dataiku? (And What It Actually Costs)

The Platform Overview

Dataiku, founded in 2013, is a data science and data analytics platform aimed at democratizing access to data and encouraging collaboration. The platform covers the entire data analysis lifecycle, from preparation to machine learning model deployment.

It focuses on visual workflows that let business users participate in data science projects alongside technical teams.

The Real Pricing Story

Here’s where teams get surprised. The median price for Dataiku is $26,000 per year, but that’s just the starting point.

Unlike transparent SaaS pricing, Dataiku requires sales conversations to get quotes. This creates budget uncertainty during planning.

Dataiku’s plan structure:

• Free Edition: Up to 3 users, basic features, self-hosted
• Discover: Up to 5 users, limited automation
• Business: Up to 20 users, full automation
• Enterprise: Custom pricing for large teams

The progression shows significant restrictions at lower tiers, pushing teams toward higher-cost enterprise options.

When Dataiku Makes Sense

Dataiku works best for organizations that truly need comprehensive data science collaboration. We built Mammoth specifically for teams frustrated with enterprise platforms that require data science degrees to operate effectively.

Dataiku excels when you have:

• Dedicated data science teams
• Strong governance requirements
• Complex ML workflows
• Substantial training budgets

What Is Databricks? (And Why Costs Vary So Much)

The Platform Overview

Databricks is a cloud-based platform founded in 2013 that offers a unified platform for data and AI. Created by the original Apache Spark developers, it provides genuine performance advantages for big data processing.

The platform combines data engineering, data science, and machine learning in a unified lakehouse architecture.

The Pricing Complexity

Databricks offers pay-as-you-go pricing with no upfront costs. But this simplicity is misleading.

How DBU pricing works:

• You pay per Databricks Unit (DBU) consumed
• Different workloads have different DBU rates
• Interactive work: $0.40-0.55/DBU
• Batch jobs: $0.15/DBU
• The same task costs 3-4x more if run interactively

The hidden cost reality: You get two separate bills—Databricks platform fees plus cloud infrastructure costs. Cloud infrastructure expenses often exceed Databricks charges by 50-200%.

When Databricks Justifies Its Complexity

Databricks makes sense for specific high-performance scenarios:

• Processing hundreds of GBs daily
• Dedicated data engineering teams
• Real-time processing requirements
• True big data ML workflows

Budget reality: Plan for $50,000-200,000+ annually including infrastructure.

Key Insight: Most teams comparing Databricks pricing underestimate total costs because they focus only on DBU rates.

The Partnership Approach: Using Both Together

Many large organizations use these platforms together rather than choosing between them.

How the integration works:

• Dataiku provides the visual interface
• Databricks handles computational processing
• Teams get collaboration features plus performance

The reality: This requires expertise in both platforms plus integration management. Budget $150,000+ annually for combined implementations.

What Most Business Teams Actually Need

After building Mammoth Analytics for teams frustrated with enterprise complexity, we’ve learned most requirements are simpler:

• Clean data from multiple sources
• Automate manual reporting processes
• Enable business users without SQL expertise
• Scale without hiring data engineers

These needs don’t require enterprise data science platforms. They need business-friendly data automation tools.

Proven Results Without Enterprise Complexity

Real customer outcomes with Mammoth:

• Starbucks: 764% ROI processing 1B+ rows across 17 countries
• RethinkFirst: 1000% ROI improvement, 30 hours to 4 hours monthly
• Bacardi: 193% ROI, 40+ hours to minutes processing

These results show that purpose-built business tools can handle enterprise-scale processing when designed for specific use cases.

The Cost Difference

Mammoth’s transparent pricing:

• Lite: $19/month for individuals
• Team: $49/month for small teams
• Business: $4,990/month for growing companies
• Enterprise: Custom pricing for large organizations

No hidden infrastructure costs. No separate cloud bills. No DBU calculations.

Decision Framework: Which Path Is Right?

Step 1: Assess Your Data Scale

Less than 10GB processed monthly?
→ Business tools like Mammoth or Power BI alternatives work fine

10-100GB monthly?
→ Either enterprise platform works, but consider cost vs. benefit

100GB+ daily?
→ Databricks likely needed for performance

Step 2: Evaluate Your Team

Mostly business users?
→ Enterprise platforms create unnecessary complexity

Mixed technical teams?
→ Dataiku’s collaboration features provide value

Dedicated data engineers?
→ Databricks performance advantages justify complexity

Step 3: Budget Reality Check

Step 4: Test Before You Commit

Smart evaluation approach:

1. Try Mammoth’s 7-day free trial with real data first
2. If it solves 80% of requirements, you’ve saved significant budget
3. Only then evaluate enterprise platforms for remaining needs

Most teams discover their “enterprise data science” needs were actually “business data preparation” requirements.

Common Implementation Mistakes

Mistake 1: Choosing Based on Demos

Platform demos use perfect datasets and showcase advanced features you may never need.

Better approach: Test with your actual messy data and real use cases.

Mistake 2: Underestimating Training Costs

Both platforms require significant learning investment beyond platform fees.

Reality check: Budget 2-4 weeks per user for productivity, plus ongoing support.

Mistake 3: Ignoring Total Cost of Ownership

Focus only on platform pricing without including infrastructure, training, and implementation.

For Databricks: Add 100-200% for cloud infrastructure costs
For Dataiku: Add 50-100% for training and implementation services

Alternatives Worth Considering

For Business-Focused Teams

• Alteryx competitors and alternatives for visual analytics
• Tableau alternatives for visualization-heavy workflows
• Mammoth for no-code data preparation and automation

For Technical Teams

• KNIME alternatives for open-source flexibility
• Custom Spark solutions for teams with deep technical expertise
• Cloud-native platforms like Snowflake or BigQuery

Key Takeaways

The platform choice depends on your specific requirements:

✅ Choose Dataiku for collaborative data science with governance needs and $50,000+ budget

✅ Choose Databricks for massive data processing with technical teams and variable cost tolerance

✅ Choose business-focused alternatives like Mammoth for data preparation, automation, and broad team adoption

Most important insight: Validate your actual requirements before committing to enterprise complexity. Many teams discover that simpler tools designed for business users deliver better ROI than comprehensive platforms designed for different use cases.

Ready to test this approach? Start Mammoth’s free trial and see how much you can accomplish with tools built for business teams rather than data scientists.

The best enterprise platform might be the one you don’t need to buy.