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 data-heavy startups needing applied ML research capacity. 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-style engagement model built for startups, 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, 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
DataRoot Labs is a Kyiv-based data science and AI consulting company founded in 2016. Team size estimates vary by source, ranging from roughly 11 to 200 employees depending on whether contractors and R&D partners are counted, but the firm consistently positions itself around applied research and development for data science and AI-powered startups rather than broad enterprise IT outsourcing. Its focus stays narrow: machine learning models, computer vision pipelines, and AI R&D partnerships for companies that need a research-capable team without hiring one in-house.
Cleveroad
Cleveroad was founded in 2011, and public sources disagree on headquarters, with LinkedIn listing Claymont, Delaware and other trackers pointing to Krakow, Poland as the operational base. Employee estimates likewise vary, from roughly 113 up to a LinkedIn-reported 201-500 range. The company's roots are in mobile and web development for startups and enterprise clients, with safe, production-grade AI deployment positioned as a newer strength built on top of 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 | Standing up a machine learning proof of concept before a startup's seed round closes., Getting a second opinion or independent 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-oriented culture suits startups that need genuine ML experimentation, not templated builds. |
| + | Small team keeps communication direct between founders and the engineers doing the work. |
| + | Kyiv talent pool gives strong ML fundamentals at lower rates than US or Western European firms. |
| + | Computer vision work is a genuine specialty backed by named client projects. |
| - | Reported employee counts vary widely by source, making true capacity hard to verify |
| - | Limited public information on 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 positioned as an addition to, not a replacement for, established product 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 machine learning proof of concept before a startup's seed round closes.
R&D-style engagement model built for startups, 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 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 |
|---|---|---|---|
| Standing up a machine learning proof of concept before a startup's seed round closes. | Strong | Limited | DataRoot Labs |
| Getting a second opinion or independent build on a computer vision pipeline. | Strong | Strong | Both equally |
| Adding AI features to a mobile app already in production. | Limited | Strong | Cleveroad |
| 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-style engagement model built for startups, not enterprise procurement.
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.
Related comparisons
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-oriented culture suits startups that need genuine ML experimentation, not 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: R&D-style engagement model built for startups, 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.