DataRoot Labs vs Cleveroad: full comparison for 2026
Quick verdict
DataRoot Labs (4.4/5) edges ahead of Cleveroad (4.0/5) overall. DataRoot Labs is the better choice for startups needing applied ML research on demand. Cleveroad is the stronger option for startups needing AI features inside a mobile or web product. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs Cleveroad: head-to-head summary
| Criterion | DataRoot Labs | Cleveroad |
|---|---|---|
| Founded | 2016 | 2011 |
| HQ | Kyiv, Ukraine | Krakow, Poland |
| Team size | 11-50 | 113-200 |
| Rating | 4.4 / 5 | 4.0 / 5 |
| Primary differentiator | R&D-oriented engagement style built for startup pace, not enterprise procurement cycles | Production-deployment discipline carried over from a decade of mobile and web delivery |
| Pricing model | Dedicated team or fixed project | Fixed project or dedicated team |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, React Native, AWS |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Retail & e-commerce, Healthcare, Logistics |
DataRoot Labs vs Cleveroad: overview
DataRoot Labs
Kyiv is home base for DataRoot Labs, founded in 2016 with a stated focus on applied data science research rather than broad IT outsourcing. Sources disagree on staff size, some citing as few as 11 employees and others closer to 200, likely reflecting how contractor networks get counted differently across platforms. What stays consistent across sources is the firm's specialization: machine learning models, computer vision pipelines, and hands-on AI R&D for startups that need research capability without building an internal team from scratch.
Cleveroad
Cleveroad has operated since 2011, though public sources disagree on where: LinkedIn lists Claymont, Delaware, while other trackers point to Krakow, Poland as the working base. Employee counts vary similarly, from roughly 113 up to a LinkedIn-reported 201-500. The firm's roots are in mobile and web development for startups and enterprise clients alike, with safe, production-grade AI deployment positioned as a newer strength built on that existing delivery discipline.
Services and capabilities: DataRoot Labs vs Cleveroad
| Capability | DataRoot Labs | Cleveroad |
|---|---|---|
| Generative AI | ✗ | ✓ |
| Machine learning | ✓ | ✓ |
| AI agents | ✗ | ✗ |
| MLOps | ✗ | ✗ |
| AI consulting | ✓ | ✗ |
| Fixed-price projects | ✓ | ✓ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs Cleveroad
| Framework / platform | DataRoot Labs | Cleveroad |
|---|---|---|
| Python | ✓ | ✓ |
| PyTorch | ✓ | N/A |
| TensorFlow | N/A | ✓ |
| LangChain | N/A | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: DataRoot Labs vs Cleveroad
| Criterion | DataRoot Labs | Cleveroad |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Fixed project, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs Cleveroad
| Dimension | DataRoot Labs | Cleveroad |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Retail & e-commerce, Healthcare, Logistics |
| Best use cases | Building an ML proof of concept ahead of a seed-stage fundraise., Getting an independent second opinion or build on a computer vision pipeline. | Adding AI features to a mobile app already in production., Getting a startup MVP built with AI as one feature among several, not the entire product. |
| Typical project type | Dedicated team | Fixed project |
DataRoot Labs vs Cleveroad: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research culture fits startups needing genuine experimentation over templated builds. |
| + | Small enough that founders talk directly to the engineers doing the work. |
| + | Kyiv-based ML talent typically comes at lower rates than US or Western European equivalents. |
| + | Named computer vision projects back up the specialization claim. |
| - | Employee counts vary widely across public sources, making capacity hard to pin down precisely |
| - | Limited public evidence of enterprise-scale delivery experience |
| Cleveroad | |
|---|---|
| + | Mobile and web development roots translate into disciplined production deployment practices. |
| + | Over a decade of delivery history across startup and enterprise clients. |
| + | Operates across four continents, giving flexible timezone coverage. |
| + | AI is positioned as an addition to, not a replacement for, established delivery skills. |
| - | Headquarters and employee count are reported inconsistently across public sources |
| - | AI-specific case studies are less prominent than the firm's mobile and web development portfolio |
Who should choose DataRoot Labs?
A typical fit: building an ML proof of concept ahead of a seed-stage fundraise.
R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose Cleveroad?
A typical fit: adding AI features to a mobile app already in production.
Production-deployment discipline carried over from a decade of mobile and web delivery. Minimum engagement is not publicly disclosed. Works best with clients in Retail & e-commerce, Healthcare, Logistics.
Decision matrix: DataRoot Labs vs Cleveroad
| Your situation | Recommended choice |
|---|---|
| You need full-ownership delivery on a defined project scope | DataRoot Labs |
| You need a large dedicated team for an ongoing programme | DataRoot Labs |
| Your budget is at the lower end | Compare: DataRoot Labs (Not disclosed) vs Cleveroad (Not disclosed) |
| You need specialist depth in a specific vertical | DataRoot Labs |
| You need staff augmentation or team extension | Neither; consider alternatives that offer staff aug |
| You need consulting before committing to a build | DataRoot Labs |
Use case fit: DataRoot Labs vs Cleveroad
| Use case | DataRoot Labs fit | Cleveroad fit | Winner |
|---|---|---|---|
| Building an ML proof of concept ahead of a seed-stage fundraise. | Strong | Limited | DataRoot Labs |
| Getting an independent second opinion or build on a computer vision pipeline. | Strong | Strong | Both equally |
| Adding AI features to a mobile app already in production. | Strong | Strong | Both equally |
| Getting a startup MVP built with AI as one feature among several, not the entire product. | Strong | Strong | Both equally |
| Fixed-price build | Limited | Limited | Both equally |
| Staff augmentation | Limited | Limited | Both equally |
Verdict: DataRoot Labs vs Cleveroad
DataRoot Labs (4.4/5) is the stronger overall choice for most AI Development projects. R&D-oriented engagement style built for startup pace, not enterprise procurement cycles.
Cleveroad (4.0/5) is worth a look if you need getting a startup MVP built with AI as one feature among several, not the entire product. If your situation matches that, Cleveroad is a competitive option.
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DataRoot Labs vs Cleveroad FAQ
Is DataRoot Labs better than Cleveroad?
DataRoot Labs (4.4/5) scores higher overall, but "better" depends on your use case. DataRoot Labs's strongest advantage: research culture fits startups needing genuine experimentation over templated builds. Cleveroad's strongest advantage: mobile and web development roots translate into disciplined production deployment practices.
How do DataRoot Labs and Cleveroad differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. Cleveroad uses fixed project or dedicated team pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: DataRoot Labs or Cleveroad?
Cleveroad is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each firm before shortlisting.
What are the main differences between DataRoot Labs and Cleveroad?
DataRoot Labs's primary differentiator is: R&D-oriented engagement style built for startup pace, not enterprise procurement cycles. Cleveroad's primary differentiator is: production-deployment discipline carried over from a decade of mobile and web delivery. They also differ in team size (11-50 vs 113-200), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Retail & e-commerce, Healthcare).
Verify all details directly with each firm before making a decision.