TL;DR
- ✓“Specialization” is often used as shorthand for capability. The directory can show where partners list products, industries, and services, but it cannot prove demand, delivery quality, certification depth, or current capacity. This is a supply-side map of how public Dynamics partners describe themselves.
- ✓The current reproducible cohort contains 1,507 public, active, non-hidden Dynamics VAR and mixed-model partners. Product records cover 1,429 partners (94.8%), industry records cover 1,429 (94.8%), and service records cover 1,432 (95.0%).
- ✓Counts are distinct partner profiles, not junction rows. A partner listing several products appears once in each relevant product category.
“Specialization” is often used as shorthand for capability. The directory can show where partners list products, industries, and services, but it cannot prove demand, delivery quality, certification depth, or current capacity. This is a supply-side map of how public Dynamics partners describe themselves.
The current reproducible cohort contains 1,507 public, active, non-hidden Dynamics VAR and mixed-model partners. Product records cover 1,429 partners (94.8%), industry records cover 1,429 (94.8%), and service records cover 1,432 (95.0%).
The most commonly listed products
Counts are distinct partner profiles, not junction rows. A partner listing several products appears once in each relevant product category.
| Product | Distinct partners | Share of 1,507 eligible partners |
|---|---|---|
| Microsoft Azure | 1,061 | 70.4% |
| Microsoft 365 | 1,035 | 68.7% |
| Business Central | 918 | 60.9% |
| Power BI | 884 | 58.7% |
| Power Platform | 609 | 40.4% |
| Dynamics 365 Sales | 522 | 34.6% |
| Dynamics 365 Customer Service | 390 | 25.9% |
| Dynamics 365 Finance | 331 | 22.0% |
| Dynamics 365 Supply Chain Management | 289 | 19.2% |
| Power Apps | 268 | 17.8% |
Industries are broadly represented
The ten most frequently listed industries are shown below. These values describe profile coverage, not completed implementations, demand, or market size.
| Industry | Distinct partners | Share of 1,507 eligible partners |
|---|---|---|
| Professional Services | 1,108 | 73.5% |
| Manufacturing | 1,034 | 68.6% |
| Retail | 883 | 58.6% |
| Wholesale & Distribution | 812 | 53.9% |
| Financial Services | 708 | 47.0% |
| Healthcare | 667 | 44.3% |
| Education | 420 | 27.9% |
| Transportation & Logistics | 359 | 23.8% |
| Government | 339 | 22.5% |
| Non-Profit | 316 | 21.0% |
Product × industry concentration
Each cell counts distinct eligible partners listing both the row product and column industry. Profiles can appear in several cells, so this is an overlapping cross-tab rather than market share.
| Product / industry | Professional Services | Manufacturing | Retail | Wholesale & Distribution | Financial Services |
|---|---|---|---|---|---|
| Microsoft Azure | 839 | 792 | 714 | 615 | 604 |
| Microsoft 365 | 832 | 768 | 700 | 604 | 588 |
| Business Central | 706 | 705 | 564 | 588 | 432 |
| Power BI | 723 | 698 | 619 | 574 | 536 |
| Power Platform | 449 | 411 | 342 | 266 | 241 |
The largest overlaps in this displayed matrix are Microsoft Azure × Professional Services (839 partners), Microsoft 365 × Professional Services (832 partners), Microsoft Azure × Manufacturing (792 partners). A lower cell means fewer public profiles list that combination; it does not establish unmet demand or partner availability.
Service listings show implementation is only part of the picture
The 1,432 service-covered partners have 12,414 distinct partner–service links, an average of 8.7 listed services per covered partner.
| Service | Distinct partners | Share of 1,432 service-covered partners |
|---|---|---|
| System Integration | 1,145 | 80.0% |
| Custom Development | 1,136 | 79.3% |
| Cloud Migration | 1,052 | 73.5% |
| BI & Analytics | 904 | 63.1% |
| ERP Implementation | 873 | 61.0% |
| CRM Implementation | 712 | 49.7% |
| Data Migration | 690 | 48.2% |
| AI & Copilot Implementation | 644 | 45.0% |
| Ongoing Support | 616 | 43.0% |
| Power Platform Development | 496 | 34.6% |
How to use the map when shortlisting
- Start with the partner directory filters for product and industry, but treat profile matches as a first-pass screen.
- Use the comparison tool to compare a short list on product, service, and location data.
- Ask for recent references matching the same product modules, industry workflows, geography, integrations, and delivery model.
- Verify that each listed service is a current delivery capability rather than a historical or broad marketing label.
Methodology and limits
The latest source timestamp in this snapshot is July 24, 2026 at 3:45 AM UTC (2026-07-24T03:45:24.797Z). The denominator is listings_v2 rows where status = 'active', website_hidden_at IS NULL, and dynamics_partner_type IN ('var', 'mixed'). Product, industry, and service links come from their canonical junction tables with server-side reference joins.
Every paginated query orders by its primary key and requests an exact server count. Publication fails if fetched and server-counted totals differ, if a cohort or junction primary key repeats, if a joined label is missing, or if a junction points outside the eligible cohort. Aggregation then de-duplicates by partner ID per label. The verified input contains 7,215 distinct partner–product links, 8,071 partner–industry links, and 12,414 partner–service links.
Product coverage is 1,429 of 1,507 (94.8%); industry coverage is 1,429 (94.8%); service coverage is 1,432 (95.0%). The data shows listed supply, not demand, revenue, project volume, commercial opportunity, partner quality, or implementation outcomes. Buyers should confirm fit directly.
Share this analysis
LinkedIn: We mapped products, industries, and services across 1,507 current public Dynamics VAR and mixed-model partners. The useful takeaway is a reproducible first-pass view of listed supply—and the questions to verify in a real shortlist. →
X: A current specialization map for 1,507 public Dynamics partners: product bars, a product-by-industry heatmap, service coverage, and explicit limits on what directory data can prove. →
