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 generative AI research capacity. Cleveroad is the stronger option for startups needing generative AI 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 | Research-oriented engagement style built for startup speed, not enterprise procurement | 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, OpenAI API | Python, OpenAI API, React Native |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Retail & e-commerce, Healthcare, Logistics |
DataRoot Labs vs Cleveroad: overview
DataRoot Labs
DataRoot Labs runs out of Kyiv and has focused on applied data science research since founding in 2016. Public staff counts vary widely, from about 11 to nearly 200 depending on the source, likely a function of how contractors get counted differently across trackers. Its generative AI and machine learning work sits alongside computer vision pipelines and hands-on AI R&D for startups that need research capability without hiring a full internal team.
Cleveroad
Cleveroad has operated since 2011, though public sources disagree on headquarters, LinkedIn listing Claymont, Delaware while other trackers point to Krakow, Poland. Employee counts vary similarly, from roughly 113 up to a LinkedIn-reported 201-500. The firm's roots are in mobile and web development, with safe, production-grade generative 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 | ✓ | ✓ |
| OpenAI API | ✓ | ✓ |
| PyTorch | ✓ | 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 | Standing up a generative AI proof of concept ahead of a seed round., Getting a second, independent build on a generative AI or computer vision pipeline. | Adding generative AI features to a mobile app already in production., Getting a startup MVP built with generative AI as one feature among several. |
| Typical project type | Dedicated team | Fixed project |
DataRoot Labs vs Cleveroad: pros and cons
| DataRoot Labs | |
|---|---|
| + | Research culture suits startups needing genuine experimentation over templated builds. |
| + | Small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision and generative AI projects back up the firm's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little 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. |
| + | Generative AI 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: standing up a generative AI proof of concept ahead of a seed round.
Research-oriented engagement style built for startup speed, not enterprise procurement. Minimum engagement is not publicly disclosed. Works best with clients in Healthtech, Fintech, Retail & e-commerce.
Who should choose Cleveroad?
A typical fit: adding generative 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 | Both may offer discovery engagements |
Use case fit: DataRoot Labs vs Cleveroad
| Use case | DataRoot Labs fit | Cleveroad fit | Winner |
|---|---|---|---|
| Standing up a generative AI proof of concept ahead of a seed round. | Strong | Limited | DataRoot Labs |
| Getting a second, independent build on a generative AI or computer vision pipeline. | Strong | Strong | Both equally |
| Adding generative AI features to a mobile app already in production. | Strong | Strong | Both equally |
| Getting a startup MVP built with generative AI as one feature among several. | 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 Generative AI Development projects. Research-oriented engagement style built for startup speed, not enterprise procurement.
Cleveroad (4.0/5) is worth a look if you need getting a startup MVP built with generative AI as one feature among several. 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 suits 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 company before shortlisting.
What are the main differences between DataRoot Labs and Cleveroad?
DataRoot Labs's primary differentiator is: research-oriented engagement style built for startup speed, not enterprise procurement. 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 company before making a decision.