DataRoot Labs vs DataArt: full comparison for 2026
Quick verdict
DataRoot Labs (3.9/5) edges ahead of DataArt (3.9/5) overall. DataRoot Labs is the better choice for startups needing applied AI research capacity. DataArt is the stronger option for enterprises in finance or healthcare needing AI advisory at global scale. The right choice depends on your project size, budget, and required tech stack.
DataRoot Labs vs DataArt: head-to-head summary
| Criterion | DataRoot Labs | DataArt |
|---|---|---|
| Founded | 2016 | 1997 |
| HQ | Kyiv, Ukraine | New York, United States |
| Team size | 11-50 | 5,700+ |
| Rating | 3.9 / 5 | 3.9 / 5 |
| Primary differentiator | A research-oriented engagement style built for startup speed, not enterprise procurement | Nearly 30 years of engineering history across 30-plus global delivery locations |
| Pricing model | Dedicated team or fixed project | Dedicated team or retainer |
| Min. engagement | Not disclosed | Not disclosed |
| Primary tech stack | Python, PyTorch, scikit-learn | Python, AWS, Azure |
| Industries served | Healthtech, Fintech, Retail & e-commerce | Financial services, Healthcare, Media & entertainment, Travel & hospitality |
DataRoot Labs vs DataArt: 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, likely a function of how contractors get counted differently across trackers. Its work centers on machine learning models, computer vision pipelines, and hands-on AI research and development for startups that need real research capability and technical AI advisory without hiring a full internal team.
DataArt
DataArt goes back to 1997, founded by Eugene Goland, and is headquartered in New York City with roughly 5,700 employees spread across more than 30 locations. The firm delivers data, analytics, and AI advisory for finance, media and entertainment, healthcare, retail, and travel and hospitality clients. Nearly three decades of history give it a longer track record than almost every other agency here, though AI advisory is delivered as part of a broader software engineering practice.
Services and capabilities: DataRoot Labs vs DataArt
| Capability | DataRoot Labs | DataArt |
|---|---|---|
| AI strategy consulting | ✓ | ✓ |
| Generative AI | ✗ | ✗ |
| Machine learning | ✓ | ✗ |
| Data engineering | ✓ | ✓ |
| MLOps | ✗ | ✓ |
| Fixed-price projects | ✓ | ✗ |
| Dedicated team model | ✓ | ✓ |
Tech stack comparison: DataRoot Labs vs DataArt
| Framework / platform | DataRoot Labs | DataArt |
|---|---|---|
| Python | ✓ | ✓ |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Google Cloud | N/A | N/A |
| Kubernetes | N/A | ✓ |
| LangChain | N/A | N/A |
| PyTorch | ✓ | N/A |
Pricing comparison: DataRoot Labs vs DataArt
| Criterion | DataRoot Labs | DataArt |
|---|---|---|
| Minimum engagement | Not disclosed | Not disclosed |
| Engagement models | Dedicated team, Fixed project | Dedicated team, Retainer |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: DataRoot Labs vs DataArt
| Dimension | DataRoot Labs | DataArt |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Healthtech, Fintech, Retail & e-commerce | Financial services, Healthcare, Media & entertainment |
| Best use cases | Getting an independent AI strategy assessment ahead of a seed round., Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. | Getting an AI strategy assessment for finance or healthcare clients with strict compliance needs., Running a long-term AI advisory and data engineering program with a financially established vendor. |
| Typical project type | Dedicated team | Dedicated team |
DataRoot Labs vs DataArt: pros and cons
| DataRoot Labs | |
|---|---|
| + | A research culture suits startups needing genuine experimentation over templated builds. |
| + | A small team keeps direct communication between founders and the engineers doing the work. |
| + | Kyiv's talent pool offers strong ML fundamentals at lower cost than US or Western European teams. |
| + | Named computer vision projects back up the agency's stated specialty. |
| - | Employee counts differ substantially across public sources, making capacity hard to verify |
| - | Little public evidence of enterprise-scale delivery experience |
| DataArt | |
|---|---|
| + | Nearly three decades of software engineering history, among the longest reviewed here. |
| + | 5,700-plus employees across 30-plus locations globally. |
| + | Named industry focus areas (finance, healthcare, travel) show real vertical depth. |
| + | Data and analytics platform experience supports AI advisory grounded in solid data foundations. |
| - | AI advisory sits inside a much broader software engineering practice rather than being the agency's core identity |
| - | Enterprise scale typically means slower onboarding than smaller, more agile AI boutiques |
Who should choose DataRoot Labs?
A typical fit: getting an independent AI strategy assessment ahead of a seed round.
A 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 DataArt?
A typical fit: getting an AI strategy assessment for finance or healthcare clients with strict compliance needs.
Nearly 30 years of engineering history across 30-plus global delivery locations. Minimum engagement is not publicly disclosed. Works best with clients in Financial services, Healthcare, Media & entertainment, Travel & hospitality.
Decision matrix: DataRoot Labs vs DataArt
| 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 DataArt (Not disclosed) |
| You need specialist depth in a specific vertical | DataArt |
| 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 DataArt
| Use case | DataRoot Labs fit | DataArt fit | Winner |
|---|---|---|---|
| Getting an independent AI strategy assessment ahead of a seed round. | Strong | Strong | Both equally |
| Bringing in dedicated research capacity for a specific AI question a small team can't resolve alone. | Strong | Limited | DataRoot Labs |
| Getting an AI strategy assessment for finance or healthcare clients with strict compliance needs. | Strong | Strong | Both equally |
| Running a long-term AI advisory and data engineering program with a financially established vendor. | Limited | Strong | DataArt |
| Fixed-price project | Limited | Limited | Both equally |
| Dedicated team model | Strong | Limited | DataRoot Labs |
Verdict: DataRoot Labs vs DataArt
DataRoot Labs (3.9/5) is the stronger overall choice for most AI Consulting projects. A research-oriented engagement style built for startup speed, not enterprise procurement.
DataArt (3.9/5) is worth a look if you need running a long-term AI advisory and data engineering program with a financially established vendor. If your situation matches that, DataArt is a competitive option.
Related comparisons
DataRoot Labs vs DataArt FAQ
Is DataRoot Labs better than DataArt?
DataRoot Labs (3.9/5) scores higher overall, but "better" depends on your use case. DataRoot Labs's strongest advantage: a research culture suits startups needing genuine experimentation over templated builds. DataArt's strongest advantage: nearly three decades of software engineering history, among the longest reviewed here.
How do DataRoot Labs and DataArt differ in pricing?
DataRoot Labs uses dedicated team or fixed project pricing. DataArt uses dedicated team or retainer 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 DataArt?
DataArt is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each agency before shortlisting.
What are the main differences between DataRoot Labs and DataArt?
DataRoot Labs's primary differentiator is: a research-oriented engagement style built for startup speed, not enterprise procurement. DataArt's primary differentiator is: nearly 30 years of engineering history across 30-plus global delivery locations. They also differ in team size (11-50 vs 5,700+), minimum engagement (Not disclosed vs Not disclosed), and primary industries served (Healthtech, Fintech vs Financial services, Healthcare).
Verify all details directly with each agency before making a decision.