DATAFOREST vs Plain Concepts: full comparison for 2026
Last updated: July 2026
Quick verdict
DATAFOREST (4.1/5) edges ahead of Plain Concepts (3.9/5) overall. DATAFOREST is the better choice for small and mid-market businesses needing data engineering plus ML analytics as a combined offering. 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.
DATAFOREST vs Plain Concepts: head-to-head summary
| Criterion | DATAFOREST | Plain Concepts |
|---|---|---|
| Founded | 2018 | 2006 |
| HQ | Kyiv, Ukraine | Madrid, Spain |
| Team size | 51–200 | 201–500 |
| Rating | 4.1 / 5 | 3.9 / 5 |
| Best for | Small and mid-market businesses needing data engineering plus ML analytics as a combined offering | 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 | $15K | $35K |
| Primary tech stack | Python, Airflow, AWS | Python, Azure ML, Azure OpenAI Service |
| Industries served | E-commerce, SaaS, Fintech, Healthcare | Enterprise, Retail, Healthcare, Financial Services |
DATAFOREST vs Plain Concepts: overview
DATAFOREST
DATAFOREST is a data science and software development agency founded in 2018, headquartered in Kyiv, Ukraine, with an additional office in New York. The company, with an estimated 50 to 249 employees, provides ETL pipelines, data analytics, and custom machine learning solutions, and has been recognized by The Manifest as a top-reviewed IT agency in Ukraine, per company website; independently unverifiable.
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: DATAFOREST vs Plain Concepts
| Capability | DATAFOREST | Plain Concepts |
|---|---|---|
| ML model development | ✓ | ✓ |
| Computer vision | ✗ | ✗ |
| NLP | ✗ | ✗ |
| Generative AI / LLM integration | ✗ | ✗ |
| MLOps | ✗ | ✓ |
| AI strategy consulting | ✓ | ✓ |
| Staff augmentation | ✗ | ✗ |
Tech stack comparison: DATAFOREST vs Plain Concepts
| Framework / platform | DATAFOREST | Plain Concepts |
|---|---|---|
| Python | ✓ | ✓ |
| TensorFlow | N/A | N/A |
| PyTorch | N/A | N/A |
| AWS | ✓ | N/A |
| Azure | N/A | ✓ |
| Kubernetes | N/A | ✓ |
Pricing comparison: DATAFOREST vs Plain Concepts
| Criterion | DATAFOREST | Plain Concepts |
|---|---|---|
| Minimum engagement | $15K | $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: DATAFOREST vs Plain Concepts
| Dimension | DATAFOREST | Plain Concepts |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | E-commerce, SaaS, Fintech | Enterprise, Retail, Healthcare |
| Best use cases | Building ETL pipelines feeding a downstream ML model, Predictive analytics for e-commerce customer behavior | Azure-native ML model deployment for an enterprise client, Mixed reality plus AI product development |
| Typical project type | Fixed project | Dedicated team |
DATAFOREST vs Plain Concepts: pros and cons
| DATAFOREST | |
|---|---|
| + | Combines core data engineering (ETL and pipelines) with ML analytics under one team |
| + | Growing review base and recognition from The Manifest as a top-reviewed Ukraine IT agency |
| + | Competitive pricing relative to Western European ML firms |
| + | New York office adds coverage for US-based clients |
| - | Kyiv, Ukraine-based delivery carries the same operational-continuity considerations as other Ukraine-linked firms |
| - | Founded in 2018, a shorter track record than more established European ML consultancies |
| - | Data engineering heritage means the ML practice is comparatively newer within the firm |
| 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 DATAFOREST?
DATAFOREST is the right choice for small and mid-market businesses needing data engineering plus ML analytics as a combined offering.
Combined data engineering (ETL) and ML analytics practice with a growing review base. Minimum engagement starts at $15K. Works best with clients in E-commerce, SaaS, Fintech, 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: DATAFOREST vs Plain Concepts
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DATAFOREST |
| You need a large dedicated team for an ongoing programme | DATAFOREST |
| Your budget is at the lower end | DATAFOREST |
| You need specialist depth in a specific vertical | DATAFOREST |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DATAFOREST |
Use case fit: DATAFOREST vs Plain Concepts
| Use case | DATAFOREST fit | Plain Concepts fit | Winner |
|---|---|---|---|
| Building ETL pipelines feeding a downstream ML model | Strong | Limited | DATAFOREST |
| Predictive analytics for e-commerce customer behavior | Strong | Strong | Both equally |
| 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: DATAFOREST vs Plain Concepts
DATAFOREST (4.1/5) is the stronger overall choice for most Machine Learning Development projects. Combined data engineering (ETL) and ML analytics practice with a growing review base. It is best for small and mid-market businesses needing data engineering plus ML analytics as a combined offering.
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
DATAFOREST vs Plain Concepts FAQ
Is DATAFOREST better than Plain Concepts?
DATAFOREST (4.1/5) scores higher overall, but "better" depends on your use case. DATAFOREST is better for small and mid-market businesses needing data engineering plus ML analytics as a combined offering. Plain Concepts is better for enterprises standardized on Microsoft Azure wanting a certified Microsoft AI Partner for ML delivery.
How do DATAFOREST and Plain Concepts differ in pricing?
DATAFOREST uses fixed project, dedicated team pricing with a minimum engagement of $15K. 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: DATAFOREST 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 DATAFOREST and Plain Concepts?
DATAFOREST's primary differentiator is: combined data engineering (etl) and ml analytics practice with a growing review base. 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 ($15K vs $35K), and primary industries served (E-commerce, SaaS vs Enterprise, Retail).
Last reviewed: July 2026. Verify all details directly with each company before making a decision.