Probayes vs Plain Concepts: full comparison for 2026
Last updated: July 2026
Quick verdict
Probayes (4.1/5) edges ahead of Plain Concepts (3.9/5) overall. Probayes is the better choice for automotive, defense, and finance clients needing rigorous Bayesian and predictive-modeling expertise. Plain Concepts is the stronger option for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery. The right choice depends on your project size, budget, and required tech stack.
Probayes vs Plain Concepts: head-to-head summary
| Criterion | Probayes | Plain Concepts |
|---|---|---|
| Founded | 2003 | 2006 |
| HQ | Montbonnot-Saint-Martin (Grenoble), France | Madrid, Spain |
| Team size | 51–200 | 201–500 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Best for | Automotive, defense, and finance clients needing rigorous Bayesian and predictive-modeling expertise | Enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery |
| Pricing model | Retainer, fixed project | Dedicated team, fixed project, retainer |
| Min. engagement | $25K | $35K |
| Primary tech stack | Python, R, Bayesian modeling frameworks | Python, Azure ML, Azure OpenAI Service |
| Industries served | Automotive, Defense, Financial Services, Healthcare | Enterprise, Retail, Healthcare, Financial Services |
Probayes vs Plain Concepts: overview
Probayes
Probayes, based in Montbonnot-Saint-Martin near Grenoble, France, is a private AI and data science company founded in 2003. With around 86 employees, Probayes specializes in Bayesian modeling, predictive analysis, and optimization for the automotive, defense, finance, and health sectors, making it one of the longest continuously operating AI-focused firms in this list.
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.
Services and capabilities: Probayes vs Plain Concepts
| Capability | Probayes | Plain Concepts |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Probayes vs Plain Concepts
| Framework / platform | Probayes | Plain Concepts |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | ✓ | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: Probayes vs Plain Concepts
| Criterion | Probayes | Plain Concepts |
|---|---|---|
| Minimum engagement | $25K | $35K |
| Engagement models | Retainer, Fixed project | Dedicated team, Fixed project, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Probayes vs Plain Concepts
| Dimension | Probayes | Plain Concepts |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Automotive, Defense, Financial Services | Enterprise, Retail, Healthcare |
| Best use cases | Predictive maintenance modeling for automotive systems, Bayesian risk modeling for finance or defense applications | Azure-native ML model deployment for an enterprise client, Mixed reality plus AI product development |
| Typical project type | Retainer | Dedicated team |
Probayes vs Plain Concepts: pros and cons
| Probayes | |
|---|---|
| + | Over two decades of operating history since founding in 2003, one of the longest-running AI specialists on this list |
| + | Deep, rigorous expertise in Bayesian modeling and predictive optimization rather than trend-driven AI positioning |
| + | Established presence in demanding regulated sectors like defense and automotive |
| + | Located in the Grenoble tech corridor, a recognized French deep-tech hub |
| - | Bayesian and predictive-analytics specialization is narrower than firms covering the full modern generative AI stack |
| - | Smaller regional presence in the Grenoble area versus Paris- or Amsterdam-based firms with broader visibility |
| 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 |
Who should choose Probayes?
Probayes is the right choice for automotive, defense, and finance clients needing rigorous Bayesian and predictive-modeling expertise.
Over two decades of specialization in Bayesian AI and predictive analytics, predating the current ML and AI boom. Minimum engagement starts at $25K. Works best with clients in Automotive, Defense, Financial Services, Healthcare.
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.
Decision matrix: Probayes vs Plain Concepts
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Probayes |
| You need a large dedicated team for an ongoing programme | Plain Concepts |
| Your budget is at the lower end | Probayes |
| You need specialist depth in a specific vertical | Probayes |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Probayes |
Use case fit: Probayes vs Plain Concepts
| Use case | Probayes fit | Plain Concepts fit | Winner |
|---|---|---|---|
| Predictive maintenance modeling for automotive systems | Strong | Strong | Both equally |
| Bayesian risk modeling for finance or defense applications | Strong | Limited | Probayes |
| Azure-native ML model deployment for an enterprise client | Limited | Strong | Plain Concepts |
| Mixed reality plus AI product development | Limited | Strong | Plain Concepts |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: Probayes vs Plain Concepts
Probayes (4.1/5) is the stronger overall choice for most Machine Learning Development projects. Over two decades of specialization in Bayesian AI and predictive analytics, predating the current ML and AI boom. It is best for automotive, defense, and finance clients needing rigorous Bayesian and predictive-modeling expertise.
Plain Concepts (3.9/5) is the better choice when enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery. If your situation matches those criteria, Plain Concepts is a competitive option.
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Probayes vs Plain Concepts FAQ
Is Probayes better than Plain Concepts?
Probayes (4.1/5) scores higher overall, but "better" depends on your use case. Probayes is better for automotive, defense, and finance clients needing rigorous Bayesian and predictive-modeling expertise. Plain Concepts is better for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery.
How do Probayes and Plain Concepts differ in pricing?
Probayes uses retainer, fixed project pricing with a minimum engagement of $25K. Plain Concepts uses dedicated team, fixed project, retainer pricing with a minimum engagement of $35K. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Probayes or Plain Concepts?
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 Probayes and Plain Concepts?
Probayes's primary differentiator is: over two decades of specialization in bayesian ai and predictive analytics, predating the current ml and ai boom. 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. They also differ in team size (51–200 vs 201–500), minimum engagement ($25K vs $35K), and primary industries served (Automotive, Defense vs Enterprise, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.