Plain Concepts vs Transparity: full comparison for 2026
Last updated: July 2026
Quick verdict
Plain Concepts (3.9/5) edges ahead of Transparity (3.7/5) overall. Plain Concepts is the better choice for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery. Transparity is the stronger option for uK enterprises fully standardized on Microsoft Azure wanting AI transformation through a certified Microsoft partner. The right choice depends on your project size, budget, and required tech stack.
Plain Concepts vs Transparity: head-to-head summary
| Criterion | Plain Concepts | Transparity |
|---|---|---|
| Founded | 2006 | 2015 |
| HQ | Madrid, Spain | United Kingdom |
| Team size | 201–500 | 201–500 |
| Rating | 3.9 / 5 | 3.7 / 5 |
| Best for | Enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery | UK enterprises fully standardized on Microsoft Azure wanting AI transformation through a certified Microsoft partner |
| Pricing model | Dedicated team, fixed project, retainer | Retainer, fixed project, dedicated team |
| Min. engagement | $35K | $30K |
| Primary tech stack | Python, Azure ML, Azure OpenAI Service | Azure ML, Azure OpenAI Service, Power BI |
| Industries served | Enterprise, Retail, Healthcare, Financial Services | Insurance, Financial Services, Enterprise, Public Sector |
Plain Concepts vs Transparity: overview
Plain Concepts
Plain Concepts, founded in 2006 and headquartered in Madrid, Spain, is a 450-plus person technology consultancy with offices across the USA, UK, Spain, Germany, the Netherlands, and Romania. As a Microsoft Gold Partner, Microsoft AI Partner, and 2016 Microsoft Partner of the Year, Plain Concepts brings deep Azure-native AI and machine learning delivery experience alongside mixed reality and IoT engineering.
Transparity
Transparity, founded in 2015 by David Jobbins and Colin Macandrew, is a UK-headquartered Microsoft pureplay technology partner with around 289 employees. The company delivers AI and machine learning transformation primarily through Microsoft Azure and Copilot technologies via its proprietary AI Factory framework, as demonstrated in its Bordereaux Sync project built with Charles Taylor InsureTech.
Services and capabilities: Plain Concepts vs Transparity
| Capability | Plain Concepts | Transparity |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✗ |
| MLOps | ✓ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Plain Concepts vs Transparity
| Framework / platform | Plain Concepts | Transparity |
|---|---|---|
| Python | ✓ | N/A |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | N/A | N/A |
| Azure | ✓ | ✓ |
| Kubernetes | ✓ | N/A |
Pricing comparison: Plain Concepts vs Transparity
| Criterion | Plain Concepts | Transparity |
|---|---|---|
| Minimum engagement | $35K | $30K |
| Engagement models | Dedicated team, Fixed project, Retainer | Retainer, Fixed project, Dedicated team |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Plain Concepts vs Transparity
| Dimension | Plain Concepts | Transparity |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Enterprise, Retail, Healthcare | Insurance, Financial Services, Enterprise |
| Best use cases | Azure-native ML model deployment for an enterprise client, Mixed reality plus AI product development | Azure-native AI transformation for an insurance or financial services client, Microsoft Copilot deployment across enterprise workflows |
| Typical project type | Dedicated team | Retainer |
Plain Concepts vs Transparity: pros and cons
| Plain Concepts | |
|---|---|
| + | Two decades of operating history since founding in 2006, with Microsoft Gold and AI Partner status |
| + | Multi-country office footprint across Spain, the UK, Germany, the Netherlands, Romania, and the US for broad coverage |
| + | Deep Azure-native ML and AI delivery credentials, useful for Microsoft-standardized enterprises |
| + | Recognized with Microsoft Partner of the Year award in 2016 |
| - | Azure-centric specialization may be less ideal for clients standardized on AWS or GCP |
| - | Broader technology consultancy scope, including mixed reality and IoT, means ML is one of several core practices |
| - | Larger enterprise-oriented engagement sizes, less accessible for very small startup budgets |
| Transparity | |
|---|---|
| + | Deep Microsoft pureplay partnership status with a proprietary AI Factory delivery framework |
| + | Demonstrated production case study, Bordereaux Sync, built with Charles Taylor InsureTech |
| + | A decade of operating history since founding in 2015, with a growing UK enterprise client base |
| + | Strong fit for insurance and financial services clients needing Azure-based compliance |
| - | Azure-exclusive positioning is a poor fit for clients on AWS, GCP, or open-source ML stacks |
| - | AI and ML transformation is delivered through a broader Microsoft cloud consulting practice rather than as a standalone ML specialization |
| - | Smaller named public case study base than larger, longer-established firms on this list |
Who should choose Plain Concepts?
Plain Concepts is the right choice for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery.
Deep Azure-native AI and ML delivery credentials as a Microsoft Gold and AI Partner, plus mixed reality expertise. Minimum engagement starts at $35K. Works best with clients in Enterprise, Retail, Healthcare, Financial Services.
Who should choose Transparity?
Transparity is the right choice for uK enterprises fully standardized on Microsoft Azure wanting AI transformation through a certified Microsoft partner.
Proprietary AI Factory framework built specifically around Microsoft Azure and Copilot technologies. Minimum engagement starts at $30K. Works best with clients in Insurance, Financial Services, Enterprise, Public Sector.
Decision matrix: Plain Concepts vs Transparity
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Plain Concepts |
| You need a large dedicated team for an ongoing programme | Plain Concepts |
| Your budget is at the lower end | Transparity |
| You need specialist depth in a specific vertical | Plain Concepts |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Plain Concepts |
Use case fit: Plain Concepts vs Transparity
| Use case | Plain Concepts fit | Transparity fit | Winner |
|---|---|---|---|
| Azure-native ML model deployment for an enterprise client | Strong | Strong | Both equally |
| Mixed reality plus AI product development | Strong | Limited | Plain Concepts |
| Azure-native AI transformation for an insurance or financial services client | Strong | Strong | Both equally |
| Microsoft Copilot deployment across enterprise workflows | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Plain Concepts vs Transparity
Plain Concepts (3.9/5) is the stronger overall choice for most Machine Learning Development projects. Deep Azure-native AI and ML delivery credentials as a Microsoft Gold and AI Partner, plus mixed reality expertise. It is best for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery.
Transparity (3.7/5) is the better choice when uK enterprises fully standardized on Microsoft Azure wanting AI transformation through a certified Microsoft partner. If your situation matches those criteria, Transparity is a competitive option.
Related comparisons
Plain Concepts vs Transparity FAQ
Is Plain Concepts better than Transparity?
Plain Concepts (3.9/5) scores higher overall, but "better" depends on your use case. Plain Concepts is better for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery. Transparity is better for uK enterprises fully standardized on Microsoft Azure wanting AI transformation through a certified Microsoft partner.
How do Plain Concepts and Transparity differ in pricing?
Plain Concepts uses dedicated team, fixed project, retainer pricing with a minimum engagement of $35K. Transparity uses retainer, fixed project, dedicated team pricing with a minimum engagement of $30K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Plain Concepts or Transparity?
Plain Concepts is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Plain Concepts and Transparity?
Plain Concepts's primary differentiator is: deep azure-native ai and ml delivery credentials as a microsoft gold and ai partner, plus mixed reality expertise. Transparity's primary differentiator is: proprietary ai factory framework built specifically around microsoft azure and copilot technologies. They also differ in team size (201–500 vs 201–500), minimum engagement ($35K vs $30K), and primary industries served (Enterprise, Retail vs Insurance, Financial Services).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.