How is MinIO storage capacity calculated for pricing?

Understanding how MinIO calculates storage capacity for pricing is essential for budget planning and choosing the optimal licensing model. This covers capacity measurement methodology, compression considerations, and licensing models.

Answer

MinIO pricing is based on usable capacity measured before compression. This means you’re billed on the actual bytes stored by your applications, not the compressed size on disk. MinIO supports both consumption-based and capacity commit licensing models.

Pricing Model Options

Two Primary Licensing Models:

1. Consumption-Based Licensing

   • Pay for actual usage
   • Flexible scaling with demand
   • No upfront capacity commitment
   • Higher per-unit pricing

2. Capacity Commit Term Model

   • Commit to specific capacity terms
   • Discounted pricing for commitments
   • Predictable costs
   • Lower per-unit pricing with volume discounts

Capacity Measurement

How Capacity is Calculated:

MinIO pricing is based on usable capacity before compression. This is the actual size of data as stored by your applications, regardless of any compression savings achieved on disk.

Key Points:

Why Pre-Compression?

Compression ratios vary significantly by data type:

• Text/logs: 5-10x compression
• Images/video: 1-1.5x (already compressed)
• Mixed workloads: 2-3x typical

Measuring before compression provides predictable, consistent billing regardless of data characteristics.

Example Capacity Calculation:

Customer Data Stored: 100 TiB (original size)
Compression Ratio: 2:1 (50% reduction)
Compressed Size: 50 TiB (on disk)
Erasure Coding: EC 8:4 (1.5x overhead)
Raw Disk Usage: 75 TiB

Billed Capacity: 100 TiB (pre-compression customer bytes)

Replication and Capacity

Each cluster is measured independently. When using site replication for disaster recovery or geo-distribution, capacity is calculated separately for each cluster.

Example with Replication:

Source Cluster: 100 TiB customer data → Billed: 100 TiB
Target Cluster: 100 TiB replicated data → Billed: 100 TiB

Total Billed Capacity: 200 TiB

Key Points:

• Replicated data counts toward the target cluster’s capacity
• Each site in a multi-site deployment is measured separately
• Active-active configurations count capacity at all participating sites
• Plan your subscription tier based on total capacity across all clusters

This means a 100 TiB workload with one replica requires 200 TiB of licensed capacity (or two 100 TiB subscriptions).

Consumption-Based Licensing

Benefits:

• Flexible scaling - Pay only for what you use
• No upfront commitment - Start small and grow
• Budget flexibility - Align costs with usage
• Risk mitigation - No over-provisioning costs

Ideal For:

• Variable workloads
• Growing businesses
• Proof-of-concept deployments
• Unpredictable storage needs

Pricing Structure:

• Per TB/month pricing
• Usage-based billing
• Monthly or quarterly billing cycles
• Automatic scaling with consumption

Capacity Commit Model

Benefits:

• Significant discounts - Lower per-unit costs
• Predictable pricing - Fixed costs for planning
• Volume discounts - Better rates for larger commitments
• Term flexibility - Various commitment periods

Ideal For:

• Predictable storage growth
• Large-scale deployments
• Budget certainty requirements
• Long-term strategic projects

Commitment Options:

• Annual commitments
• Multi-year terms
• Tiered pricing based on capacity
• Volume discount brackets

Pricing Comparison

Licensing Flexibility

Hybrid Approaches:

• Combine base commit with consumption overages
• Seasonal scaling options
• Migration between models
• Graduated pricing tiers

Enterprise Options:

• Custom pricing for large deployments
• Multi-site licensing
• Global enterprise agreements
• Special pricing for specific industries

Cost Optimization Strategies

For Consumption-Based:

1. Monitor usage patterns to identify commit opportunities
2. Implement lifecycle policies to tier or expire old data
3. Regular usage review for potential model transitions
4. Delete unnecessary data to reduce billable capacity

For Capacity Commit:

1. Accurate capacity planning to avoid over-commitment
2. Growth projections for appropriate term selection
3. Tiered commitments for different data types
4. Review cycles for commitment adjustments

Decision Framework

Choose Consumption-Based When:

• Storage growth is unpredictable
• Starting with MinIO evaluation
• Seasonal or variable workloads
• Preference for operational expense model

Choose Capacity Commit When:

• Predictable storage requirements
• Long-term strategic deployment
• Budget requires cost certainty
• Seeking maximum cost efficiency

Implementation Considerations

Billing and Measurement:

• Monthly usage reporting
• Automated billing integration
• Usage analytics and forecasting
• Multi-tenant billing support

Support Inclusion:

• Technical support included in subscription
• Different support tiers available
• Professional services options
• Training and certification programs

Getting Started

Next Steps:

1. Assess current and projected storage needs
2. Evaluate workload patterns for model selection
3. Contact MinIO sales for specific pricing
4. Request proof-of-concept pricing
5. Compare total cost of ownership across models

Key Advantages

MinIO’s flexible pricing approach provides:

• Model choice - Consumption or commit based on needs
• Cost optimization - Discounts for commitments
• Scalability - Support for any size deployment
• Predictability - Options for budget certainty
• Flexibility - Ability to change models as needs evolve

Important Notes

• Specific pricing details should be obtained directly from MinIO sales
• Volume discounts are available for larger deployments
• Custom arrangements possible for enterprise customers
• Professional services available for implementation support
• Support is included in subscription pricing

This dual-model approach ensures that organizations can choose the pricing structure that best aligns with their usage patterns, budget requirements, and strategic objectives while maintaining the flexibility to adapt as their storage needs evolve.