Wholesale vs. Retail Colocation Models
Executive Summary
Key Takeaways
- ✓ Wholesale colocation leases large, dedicated space or power blocks to a relatively small number of tenants; retail colocation leases smaller rack or partial-rack units to a larger, more diversified tenant base.
- ✓ Wholesale deals carry lower per-tenant count but higher per-deal revenue and typically longer contract tenor; retail deals carry higher tenant count, shorter average tenor, and greater pricing and churn granularity.
- ✓ Retail colocation typically generates materially higher cross-connect revenue density per unit of space, given its larger, more diverse, and more interconnected tenant base relative to wholesale.
- ✓ A facility or portfolio spanning both segments should model wholesale and retail revenue streams separately, since blending them conceals which segment is actually driving performance.
Overview¶
Wholesale and retail colocation are both business models within Colocation Financial Models, sitting under the broader Data Centre Financial Modelling pillar, but differ in deal size, tenant diversification, pricing granularity, and cross-connect revenue density.
Side-by-Side Comparison¶
| Dimension | Wholesale Colocation | Retail Colocation |
|---|---|---|
| Typical deal size | Large (250kW+, full suite/cage) | Small (individual rack, partial rack) |
| Tenant count | Relatively small | Larger, more diversified |
| Pricing approach | Individually negotiated per deal | Standardised rate card, tiered by density |
| Contract tenor | Typically longer | Typically shorter, more variable |
| Cross-connect revenue density | Lower | Higher |
| Occupancy tracking granularity | Lower (fewer, larger tenants) | Higher (many tenants, density tiers) |
Why the Two Require Different Modelling Granularity¶
A wholesale colocation model can reasonably track a smaller number of larger, individually negotiated contracts in detail. A retail colocation model requires finer occupancy, pricing, and churn granularity across a much larger tenant base, typically aggregated by density tier rather than tracked contract by contract, consistent with the discipline described in Rack Revenue Models.
Modelling a Mixed Wholesale/Retail Portfolio¶
A facility or portfolio spanning both segments should model wholesale and retail revenue streams separately, given their materially different deal size, pricing granularity, and churn characteristics, rather than blending them into a single portfolio-wide occupancy and rate assumption that would conceal which segment is actually driving performance.
Continue Reading¶
Related Technical Guides¶
Related Glossary¶
Related Pillars¶
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Frequently Asked Questions
What is the primary structural difference between wholesale and retail colocation?
Wholesale colocation leases large, dedicated space or power blocks, typically 250kW and above, or a full suite or cage, to a relatively small number of tenants. Retail colocation leases smaller units, individual racks or partial-rack space, to a larger, more diversified tenant base.
How does pricing granularity differ between the two?
Wholesale pricing is typically individually negotiated per large deal, with fewer, larger contracts to track. Retail pricing typically follows a more standardised rate card structure applied across a much larger number of smaller, more homogeneous contracts, requiring finer pricing and occupancy granularity in the model.
Which segment generates higher cross-connect revenue density?
Retail colocation typically generates materially higher cross-connect revenue density per unit of space, given its larger, more diverse tenant base and the greater number of potential interconnections among tenants and network carriers relative to a wholesale facility's smaller tenant count.
How should a model handle a portfolio spanning both wholesale and retail colocation?
By modelling wholesale and retail revenue streams separately, given their materially different deal size, pricing granularity, and churn characteristics, rather than blending them into a single portfolio-wide occupancy and rate assumption that would conceal which segment is actually driving performance.
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