Imaginary Cloud vs Plain Concepts: full comparison for 2026
Last updated: July 2026
Quick verdict
Imaginary Cloud (4.0/5) edges ahead of Plain Concepts (3.9/5) overall. Imaginary Cloud is the better choice for companies wanting ML capabilities delivered alongside strong product design and UX engineering. 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.
Imaginary Cloud vs Plain Concepts: head-to-head summary
| Criterion | Imaginary Cloud | Plain Concepts |
|---|---|---|
| Founded | 2010 | 2006 |
| HQ | Lisbon, Portugal | Madrid, Spain |
| Team size | 51–200 | 201–500 |
| Rating | 4.0 / 5 | 3.9 / 5 |
| Best for | Companies wanting ML capabilities delivered alongside strong product design and UX engineering | Enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery |
| Pricing model | Fixed project, dedicated team | Dedicated team, fixed project, retainer |
| Min. engagement | $20K | $35K |
| Primary tech stack | Python, React, Node.js | Python, Azure ML, Azure OpenAI Service |
| Industries served | SaaS, Fintech, Healthcare, E-commerce | Enterprise, Retail, Healthcare, Financial Services |
Imaginary Cloud vs Plain Concepts: overview
Imaginary Cloud
Imaginary Cloud, founded in 2010 and headquartered in Lisbon, Portugal, is an AI-first software development company with roughly 77 employees. The firm combines design, engineering, and AI to deliver custom software and machine learning-enabled products, positioning itself around what it calls seamless digital acceleration, per company website.
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: Imaginary Cloud vs Plain Concepts
| Capability | Imaginary Cloud | Plain Concepts |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: Imaginary Cloud vs Plain Concepts
| Framework / platform | Imaginary Cloud | Plain Concepts |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | ✓ | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | N/A | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: Imaginary Cloud vs Plain Concepts
| Criterion | Imaginary Cloud | Plain Concepts |
|---|---|---|
| Minimum engagement | $20K | $35K |
| Engagement models | Fixed project, Dedicated team | Dedicated team, Fixed project, Retainer |
| Rate transparency | Minimum disclosed | Minimum disclosed |
| Price tier | Accessible | Accessible |
Target audience comparison: Imaginary Cloud vs Plain Concepts
| Dimension | Imaginary Cloud | Plain Concepts |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | SaaS, Fintech, Healthcare | Enterprise, Retail, Healthcare |
| Best use cases | AI-enabled consumer product design and development, Custom software with embedded ML recommendation features | Azure-native ML model deployment for an enterprise client, Mixed reality plus AI product development |
| Typical project type | Fixed project | Dedicated team |
Imaginary Cloud vs Plain Concepts: pros and cons
| Imaginary Cloud | |
|---|---|
| + | 15 years of operating history since founding in 2010 as a Lisbon-based software studio |
| + | Strong design and UX engineering complements ML and AI delivery for consumer-facing products |
| + | EU-headquartered in Portugal, useful for European data-residency requirements |
| + | Positions AI as a first-class design consideration, not a bolted-on backend feature |
| - | Broader software and design studio heritage means ML depth is narrower than pure-play ML specialists |
| - | Smaller team of around 77 relative to larger regional generalists on this list |
| 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 Imaginary Cloud?
Imaginary Cloud is the right choice for companies wanting ML capabilities delivered alongside strong product design and UX engineering.
Design-led software development studio with AI positioned as a first-class capability, not an afterthought. Minimum engagement starts at $20K. Works best with clients in SaaS, Fintech, Healthcare, E-commerce.
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: Imaginary Cloud vs Plain Concepts
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | Imaginary Cloud |
| You need a large dedicated team for an ongoing programme | Imaginary Cloud |
| Your budget is at the lower end | Imaginary Cloud |
| You need specialist depth in a specific vertical | Imaginary Cloud |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | Imaginary Cloud |
Use case fit: Imaginary Cloud vs Plain Concepts
| Use case | Imaginary Cloud fit | Plain Concepts fit | Winner |
|---|---|---|---|
| AI-enabled consumer product design and development | Strong | Limited | Imaginary Cloud |
| Custom software with embedded ML recommendation features | Strong | Limited | Imaginary Cloud |
| 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: Imaginary Cloud vs Plain Concepts
Imaginary Cloud (4.0/5) is the stronger overall choice for most Machine Learning Development projects. Design-led software development studio with AI positioned as a first-class capability, not an afterthought. It is best for companies wanting ML capabilities delivered alongside strong product design and UX engineering.
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.
Related comparisons
Imaginary Cloud vs Plain Concepts FAQ
Is Imaginary Cloud better than Plain Concepts?
Imaginary Cloud (4.0/5) scores higher overall, but "better" depends on your use case. Imaginary Cloud is better for companies wanting ML capabilities delivered alongside strong product design and UX engineering. Plain Concepts is better for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery.
How do Imaginary Cloud and Plain Concepts differ in pricing?
Imaginary Cloud uses fixed project, dedicated team pricing with a minimum engagement of $20K. 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: Imaginary Cloud 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 Imaginary Cloud and Plain Concepts?
Imaginary Cloud's primary differentiator is: design-led software development studio with ai positioned as a first-class capability, not an afterthought. 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 ($20K vs $35K), and primary industries served (SaaS, Fintech vs Enterprise, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.